PDF version of this document is located at
http://web.mit.edu/paltsev/www/docs/move04.pdf
GAMSMPSGE codes of the static and dynamic models are located
here
(The paper also appears as a Technical Note 4, Joint Program on the Science
and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA, 2004.
It can be found at:
https://globalchange.mit.edu/publication/14238 ).
Moving from Static to Dynamic General Equilibrium
Economic Models (Notes for a beginner in MPSGE)
Sergey Paltsev
Joint Program on the Science and Policy of Global Change
Massachusetts Institute of Technology
June 1999 (Revision April 2004)
Abstract
The document is intended to serve as a guide for beginners in MPSGE.
It starts with a short introduction to the class of economic problems
which can be solved with MPSGE, followed by a detailed description of
stepbystep transformation of a simple static general equilibrium
model into a dynamic Ramsey model. The model is based on a simplified
dataset. Two cases are considered: the first dataset represents an
economy on the steadystate growth path and the second dataset is off
the steadystate growth path in a base year. The paper includes
GAMSMPSGE codes which can be copied and used as a starting point for
further exploration of dynamic economic modelling.
1 Introduction
Originally, this document has been written as notes for myself during
my struggle with understanding how to build a dynamic general
equilibrium model based on Lau, Pahlke, and Rutherford
(1997) paper^{1}. I
subsequently discovered that the paper could be quite helpful to
other novice modellers. The paper is organized around a simple
twosector, twofactor model. In the next section, I will try to
share my understanding of what is MPSGE and what kind of problems can
be solved with its help. Section 3 describes how a simple static
model can be represented in the MPSGE format. If you are already
familiar with MPSGE, you may skip that part and turn directly to
section 4, which shows the transition of a static model into a
dynamic model^{2}. This is done
for the case where an economy is initially on a steadystate growth
path. Section 5 presents adjustments in the model, which are
necessary for the off steadystate case. Program listings are
provided in the Appendices. I have also included Appendices on
MPSGEMCP conversion based on the example of a simple ShovenWhalley
(1984) model, and representation of different functional forms in
MPSGE format. They are not directly relevant to dynamic models.
However, they are important for better understanding the process of
economic modelling with MPSGE. The author is greatly thankful to Thomas
Rutherford for correcting endless errors.
2 MPSGE and General Economic Equilibrium Models
MPSGE (Mathematical Programming System for General Equilibrium
Analysis) is a programming language designed by Thomas Rutherford
(1999) in the early 80s for solving ArrowDebreu economic equilibrium
models. MPSGE uses the programming language GAMS (General Algebraic
Modeling System) as an interface^{3}. As a result, if one
wants to use MPSGE, he needs to learn basic GAMS syntax (Brook et al,
2003) as well. As it follows from the name, MPSGE solves computable
general equilibrium (CGE) economic models. These models consist of
economic agents who interact among each other through prices which
emerge from markets for goods and factors of production. The word
"general" means that all economic flows are accounted for, i.e
there is a "sink" for every "source". Finding an equilibrium for
a CGE model involves finding equilibrium prices, quantities, and
incomes.
As an illustration of a simple economic model, consider the task of
finding an equilibrium in the economy (for the future reference I
will call it Wonderland), which consists of two economic agents:
consumers and producers.
Consumers have an initial endowment of labor L and capital K. For
simplicity, there is a single representative consumer^{4} CONS in
Wonderland. The consumer
derives his income I from the sales of his endowments. Then he
purchases his preferred choice of goods. There are two goods, X and
Y, in the economy. The consumer obtains utility from consumption of
the goods. Producers are the firms that take initial endowments of
the consumer as inputs of production and convert them into outputs.
Both production sectors, X and Y, are characterized by the
available technology F and G, respectively.
We want to determine the prices and quantities which maximize
producers' profits and consumer's
utility^{5}.
The solution to this problem (which is a simple ArrowDebreu (1954)
problem) can be found using a nonlinear formulation. It can be
represented as an optimization problem of the consumer subject to
income, technology, and feasibility constraints.
maxW(X,Y)  s.t  p_{x} X+p_{y} Y = w L + r K 
  X = F(K_{x}, L_{x}) 
  Y = G(K_{y}, L_{y}) 
  L = L_{x} + L_{y} 
  K = K_{x} + K_{y}

where W is a utility function; p_{x} and p_{y} are the prices of
goods X and Y; r is a price of capital K; w is a price of
labor L; K_{x} and L_{x} are capital and labor used in the
production sector X; K_{y} and L_{y} are capital and labor used in
the sector Y. This is a standard microeconomic textbook
optimization problem, and a usual technique for finding the solution
is the method of Lagrange multipliers. This problem can be solved in
GAMS as a nonlinear programming (NLP).
There are some cases (such as a presence of several consumers,
taxes, or other distortions) where it is not possible to solve the
problem of finding a market equilibrium as an optimization problem.
Then the problem could be approached in a different way. It can be
turned into a Mixed Complimentarity Problem (MCP) and solved as a
system of nonlinear equations. NLP problems are a subset of MCP and
MPSGE finds an equilibrium as a solution to MCP.
For the purposes of the models presented in this paper,
complimentarity really plays no role. Therefore, I just briefly
describe my understanding of MCP. An interested reader is referred to
Rutherford (1995) paper. I consider a nonlinear complimentarity
problem (NCP), which is also a special case of MCP. It can be written
as:
Given: f: R^{n} ® R^{n}
Find: z Î R^{n}
s.t. f(z) ³ 0, z ³ 0, z^{T} f(z) = 0.
I have tried to convert the mathematical symbols into simple English,
but it has not helped me very much. It sounds like: given the
function f between two ndimensional sets of real numbers
(function f assigns to each member of the first set exactly one
member of the second set), find z which belongs to an
ndimensional set of real numbers, such that function f (z)
is greater or equal to zero, z is greater or equal to zero, and
associated complimentary slackness condition^{6} is satisfied. The complimentary slackness condition requires
that either z equals zero (i.e. the dual multiplier vanishes),
or the inequality constraint is satisfied with strict equality, or
both.
However, I found out that these words is just a usual scientific way
of hiding something rather simple. Do you recall from your 6th grade math
the solution to an equation like: x(5x)=0? Yes, it is x=0 and
x=5. To make it more clear for a comparison, the equation above can
be rewritten as xf(x)=0, where f(x)=5x. It leads to the
condition that either x or f(x) has to be equal zero. This is the
main idea behind MCP.
In the case when we have not just one x, but a vector of [`x] = (x_{1},x_{2},...,x_{n}), then there is a system of n equations like
[`x] f([`x])=0, which forms an MCP problem. The word "mixed"
in MCP reflects the fact that the solution is a mix of equalities
f(x)=0 and inequalities f(x) > 0.
Mathiesen (1985) has shown that the ArrowDebreu economic equilibrium
model can be formulated as MCP, where three inequalities should be
satisfied: zero profit condition, market clearance condition, and
income balance condition. A set of three nonnegative variables is
involved in solving MCP problem: prices, quantities (they are called
as activity levels in MPSGE), and income levels.
Zero profit condition requires that any activity operated at a
positive intensity must earn zero profit (i.e. value of inputs must
be equal or greater than value of outputs). Activity levels y
for constant returns to scale production sectors are the
associated variables with this condition. It means that either y > 0
(a positive amount of y is produced) and profit is zero, or
profit is negative and y=0 (no production activity takes place).
In terms of MCP, the following condition should be satisfied for
every sector in an economy:
profit ³ 0, y ³ 0, output^{T} (profit) = 0. 

Market clearance condition requires that any good with positive price
must have a balance between supply and demand and any good in excess
supply must have a zero price. Price vector p (which includes prices
of final goods, intermediate goods and factors of production) is
associated variable. In terms of MCP, the following condition should
be satisfied for every good and every factor of production:
supplydemand ³ 0, p ³ 0, p^{T}(supplydemand) = 0. 

Income balance condition requires that for each agent (including any
government entities) value of income must equal the value of factor
endowments and tax revenue:
income = endowment + tax revenue. 

In the MPSGE formulation the complimentarity conditions relate to
zero profit condition and market clearance condition only. The income
balance condition only serves as a substitute for the income equation
and does not exhibit complimentarity slackness with the value of
factor and tax revenue^{7}.
To illustrate the equilibrium conditions, consider the Wonderland
example. For the reasons which will be clear later, additional
production sector W is introduced to represent utility derived from
the consumption of X and Y. Accordingly, P_{w} is a price of the
output of the production sector W. First, consider zero profit
conditions. In the Wonderland example, an activity level y is a
vector with the following components y=(X, Y, W). Profit in a
particular sector is defined as a difference between total revenue
and total cost. We assume that activity levels X, Y, and
W are positive (i.e., the condition y ³ 0 is satisfied with the
strict inequality as y > 0). Hence, zero profit conditions
((profit) = 0) can be written as
(P_{x}  C_{x}(w, r)) = 0, 
 (1) 
(P_{y}  C_{y}(w, r)) = 0, 
 (2) 
(P_{w}  e(P_{x}, P_{y})) = 0, 
 (3) 
where C_{x}, C_{y} are unit cost functions for X and Y (cost of production of
one unit of a good under the factor prices w and r); e is a unit cost
(expenditure) function for W (cost of buying one unit of utility
under the prices P_{x} and P_{y}).
Market clearance condition requires that if prices are positive then
supply should be equal to demand. It is represented by the following
equations
L = 
¶C_{x}
¶w

X + 
¶C_{y}
¶w

Y, 
 (7) 
K = 
¶C_{x}
¶r

X + 
¶C_{y}
¶r

Y. 
 (8) 
On the demand side, partial derivatives ¶e / ¶P_{i},
¶C_{i} / ¶w, and ¶C_{i} / ¶r
represent compensated^{8} demand functions (by Shephard's
lemma)^{9}.
Income balance condition states that the value of endowments equals
consumer's income:
MPSGE would solve nine equations (19) for nine unknowns: activity
levels, X, Y, W; prices, P_{x}, P_{y}, P_{w}, w, r; and income I. It
should be noted that equilibrium conditions are hard to formulate in
an explicit form for certain problems. The good news for a MPSGE user
is that one does not need to spend any time deriving the equilibrium
conditions. MPSGE builds them for you automatically. However, it is
always helpful to understand what the algebraic representation of a
particular MPSGE model is^{10}. For a comparison, alternative
formulations of a simple model of taxation and international trade
(Shoven and Whalley, 1984) in MPSGE and its algebraic form (MCP
formulation) are presented in Appendix 2. An MCP formulation of a
dynamic Ramsey problem can be found in the Rutherford's (1997) paper.
It should be noted that MPSGE is a tool for the formulation but not
for the solution of complimentarity problems. In order to run MPSGE
program, MCP solvers should be used. GAMS has two MCP solvers: MILES
and PATH. In Tom's words, MILES is his abandoned child, which leaves
PATH as the first choice.
3 A Static Model
In the previous section we have briefly discussed how MPSGE finds an
equilibrium. It is nice to know that MPSGE will solve it not as an
optimization problem, but instead by finding an equilibrium in a
system of inequalities. However, in order to create a simple MPSGE
program a user does not need to know this. What is much more
important for a beginner is the syntax of the MPSGE
language^{11}. A big advantage of
MPSGE is that it is based on nested constant elasticity of
substitution (CES) utility and production functions^{12}. Use of nested functions can provide a flexible
representation of how inputs are related. To introduce the MPSGE
syntax^{13}, we will consider
again the Wonderland example described in Section 2. The GAMSMPSGE
program of the Wonderland static model (move1_1.gms) is
listed in Appendix 3.
Economic models are based on data which in turn are usually organized
into a Social Accounting Matrix (SAM)^{14}, which is also sometimes called as microconsistent matrix (MCM). We
assume the following data represented in Table 1 where a
SAM has three production sectors X, Y, and W (they
are declared in the $SECTORS block of the
MPSGE program), one consumer CONS (declared in the
$CONSUMERS block), and five markets X, Y,
W, L, and K (declared in the $COMMODITIES block). Because the
variables which are associated with commodities are their prices, they
usually begin with the letter P.
 Production Sectors  Consumers 
Markets  X  Y  W  CONS 
PX  100   100  
PY   100  100  
PW    200  200 
PL  40  60   100 
PK  60  40   100 
Table 1. A Static Model SAM (the Wonderland example)
A SAM describes a data set, which is usually referred to
as a benchmark dataset. Numbers represent the values (price times
quantity) of economic transactions in a given period of time. A
positive entry means a value of output (or sales). A negative entry
reflects a value of input (or purchases). Zero profit, market
clearance and income balance conditions imply that row sums and
column sums are equal to zero.
To illustrate how SAM is balanced, consider the following row and
column of Table 1. A production sector X purchases 40 units^{15} of labor and 60 units of capital, and sales 100 units of
good X . The sum over X column is equal to zero. It shows that there
is no excess profit (zero profit) in the sector X. A row PX shows
that 100 units of X which are produced and sold (positive entry of
100) are consumed by sector W (negative entry of 100). The sum over
PX row is equal to zero. It shows that supply equals to demand
(market clearance) for a good X.
For simplicity, sometimes a Harberger convention is adopted in a
benchmark SAM. It consists in normalizing all prices to 1. Then
quantities in a SAM represent expenditures, or how much of a good or
factor one can buy for $1. It should be noted that an
ArrowDebreu economy only depend upon relative prices. Doubling all
prices double money profits and income, which is resulted in the same
solution for quantities (or activity levels). The absolute price
level has no impact on the equilibrium outcome.
As it has already been noted, MPSGE does not require a user to
formulate an algebraic representation of production and utility
functions. The user only needs to provide reference quantities,
reference prices, and elasticities. Based on provided information,
MPSGE constructs underlying production and utility functions. At the
solution, MPSGE returns equilibrium values for the variables,
described in $SECTORS, $COMMODITIES, and $CONSUMERS.
In the Wonderland example, there are two products (X and Y), two factors
(L and K), and one consumer (CONS). An extra row and column (PW and W)
are introduced in order to explicitly represent utility derived from
an aggregate consumption. As we will discuss later, it could have been
done directly in the $DEMAND block^{16}.
The program starts with declaration of the
title, which will be printed in a solution listing. Usually,
GAMSformatted declarations of scalars, parameters, and sets then
follow. We do not need them in our simple static model.
The next portion is in MPSGE format.
$ONTEXT
$MODEL:NAME
These two blocks shifts control from GAMS to MPSGE compiler. Model
name is chosen by a user. For example, our model has the name
MOVE1_1. The model name has to be a legitimate filename, because a
file NAME.GEN (MOVE1_1.GEN in our case) is going to be generated.
$SECTORS:
In this block, production sectors are declared. They determine how
inputs are converted into outputs. The variables here are activity levels
and they are associated with zero profit conditions (i.e. they make
sure that a production sector earn zero profit). In the Wonderland
model, we have three sectors: X, Y, and W. Comments can be made in the same
line after ! mark.
$COMMODITIES:
Commodities are declared in this block. The variables here are prices.
They are associated with market clearance conditions (i.e. they make
sure that supply is equal to demand). We have five commodities: two
output prices PX, PY, two factor prices PK, PL, and price index PW for
welfare (i.e. utility derived from an aggregate consumption).
$CONSUMERS:
Consumers who supply factors and receive utility are described. The
variable here is income from all sources. It is associated with income
balance condition (i.e. it makes sure that total income is equal to
total consumption (or total demand)). In our case, we have one representative
consumer CONS.
$PROD:
Production block constructs the underlying production function (i.e., the
rule how inputs are converted into output). Sectors and commodities
used in a production block should be declared in respective blocks.
Consider the first production block for X (Production blocks for Y
and W have a similar structure):
$PROD:X s:1
O:PX Q:100
I:PL Q:40
I:PK Q:60
This block describes a CobbDouglas production function (we can see
it from s:1, which means that the elasticity of substitution
between inputs is equal to one). Inputs here are PL and PK (we can
see it from I:PL and I:PK). Output is PX (O:PX). This production sector converts 40 units of PL and 60 units
of PK into 100 units of PX. Mathematically, the production
function for this technology can be written as X = fL^{0.4} K^{0.6}
(See Appendix 1 for conversion of production and utility functions
into the MPSGE format). Figure 1 illustrates the calibration of a
production function to benchmark prices and quantities.
Fig.1. Production function calibration
MPSGE specifies the production function with a single reference point. In this
example we have explicitly specified reference quantities only.
Reference prices are set to 1 by default. Relative price of
inputs determines the slope of isocost (which in turn equals to
the slope of isoquant at the reference quantities). It means that our
production block is identical to:
$PROD:X s:1
O:PX Q:100 P:1
I:PL Q:40 P:1
I:PK Q:60 P:1
The curvature of isoquant is determined by s, the elasticity of
substitution between inputs (s:1 corresponds to a CobbDouglas
production function). Default value for elasticity is zero. The
reference quantities and prices are used only for calibration.
They are not used by the solver as starting values of the
variables. Production block Y is similar to X.
Production block for W (Welfare) serves as a tool for conversion of
goods X and Y consumption into utility derived from an aggregate
consumption. In this simple model we have introduced this
production block for clarity purposes only. We could have eliminated
the production block W and changed the demand block into^{17}
$DEMAND:CONS
D:PX Q:100
D:PY Q:100
E:PL Q:100
E:PK Q:100
However, in more complicated models the representation of welfare
as a production block may be very useful (for example, in the
analysis of a consumption tax).
$DEMAND:
Demand block represents an income balance constraint.
$DEMAND:CONS
D:PW Q:200
E:PL Q:100
E:PK Q:100
A consumer receives income from its L and K endowments (E:PK
and E:PL) and demands good PW (D:PW). Reference
quantities (Q fields) are used for calibration of the utility function in
the same way that the production block calibrates production function.
$OFFTEXT
$SYSINCLUDE mpsgeset NAME
These two blocks shift control from MPSGE back to GAMS.
NAME.ITERLIM = 0;
This command defines the maximum number of iterations which are going
to be taken by the solver. When this is zero, we tell the solver not
to solve the model but to return the values which are based on the
initial data. Setting the
iteration limit to zero is important to assure that the benchmark
data (i.e., the data described in a SAM) represent the equilibrium
solution.
$INCLUDE NAME.GEN
SOLVE NAME USING MCP;
These two blocks are used for running the model.
The solution of the static model (reported in a listing
(move1_1.lst) file) is:
LOWER LEVEL UPPER MARGINAL
VAR X . 1.0000 +INF .
VAR Y . 1.0000 +INF .
VAR W . 1.0000 +INF .
VAR PX . 1.0000 +INF .
VAR PY . 1.0000 +INF .
VAR PL . 1.0000 +INF .
VAR PK . 1.0000 +INF .
VAR PW . 1.0000 +INF .
VAR CONS . 200.0000 +INF .
In the solution, LOWER and UPPER columns show the bounds of
respective variables. Zero is represented by ".". INF means
infinity. The LEVEL column reports the solution value. The MARGINAL
column shows the value of the complimentary slackness (shadow)
variable. We need to pay close attention to LEVEL and MARGINAL
columns. Complimentary slackness implies that in equilibrium,
either the level of a variable will be positive or the marginal value
will be positive, but not both. If both of them are positive, we need
to check our model.
The solution to the static model tells us that the benchmark data
(represented in the SAM) are consistent with the equilibrium of the model.
We can see it because all marginals are equal to zero after benchmark
replication (i.e., after solving the model with ITERLIM=0).
Equilibrium activity levels X, Y, and Z are equal to 1 (and not to
100, 100, and 200 as in SAM !). Equilibrium prices are equal to 1.
Equilibrium income level of a representative consumer is equal to 200.
Somewhat confusing for beginners is that activity levels in the
solution are all equal to one. This is just due to rescaling and it
is done for the purpose of future analysis of counterfactual
experiments, where a user can easily
see the percentage change of a value deviated from 1. If we want to
see unscaled activity levels, we need to multiply the levels from the
solution by Q: fields in the production blocks. For example, for
the activity level X it equals to 1*100=100.
There are other ways to get actual activity levels in the solution
listing. One possible way is to rescale production
blocks and assign initial values for activities as it is done in the
program move1_3.gms. The solution for this case directly
replicates the benchmark data:
LOWER LEVEL UPPER MARGINAL
VAR X . 100.0000 +INF .
VAR Y . 100.0000 +INF .
VAR W . 200.0000 +INF .
VAR PX . 1.0000 +INF .
VAR PY . 1.0000 +INF .
VAR PL . 1.0000 +INF .
VAR PK . 1.0000 +INF .
VAR PW . 1.0000 +INF .
VAR CONS . 200.0000 +INF .
However, for numerical reasons, it is advisable to scale equilibrium
values close to unity. That is why it is better to use a REPORT block
as it is shown in the program move1_4.gms, where the
initial SAM is recreated using the reported values.
All initial values for activity levels and prices are equal to 1 by
default. A variable CONS, which represents a consumer income level,
is somewhat different. It is determined by endowments. Remember from
income balance condition that income is equal to total value of
endowments, and in turn equal to total demand. In the case of static
model it is equal to 200 because endowment of capital (E:PK) is 100
and endowment of labor (E:PL) is 100, and prices PK and PL are equal
to 1 by default.
As we have noted, the model determines only relative prices. So if we
assign all initial prices as being equal to 2, then variable CONS
will be calculated to be equal to 400. We can check it by modifying
the static model. Initial price levels can be assigned after
$SYSINCLUDE mpsgeset NAME, but before
$INCLUDE NAME.GEN command in the following way:
PX.L = 2;
PY.L = 2;
PW.L = 2;
PK.L = 2;
PL.L = 2;
Then the solution listing shows an increase in all prices and the
income level.
LOWER LEVEL UPPER MARGINAL
VAR X . 1.0000 +INF .
VAR Y . 1.0000 +INF .
VAR W . 1.0000 +INF .
VAR PX . 2.0000 +INF .
VAR PY . 2.0000 +INF .
VAR PL . 2.0000 +INF .
VAR PK . 2.0000 +INF .
VAR PW . 2.0000 +INF .
VAR CONS . 400.0000 +INF .
Due to determination of relative prices only, usually one price is fixed by
a user. If no price is fixed initially (i.e. no good is chosen as a
numeraire), then MPSGE will do a default price normalization. In the
case of our static model, a consumer's income level CONS is fixed. It can
be seen in the solution listing file (.lst) by looking at the
statement Default price normalization using income for CONS.
The model can also be specified in a different format. Instead of
using numbers in reference quantity and price field, we could have introduced
parameters at the beginning of the program and used them for
calibration in the fields of production and demand blocks. An example
of the vector syntax is presented in the program move1_5.gms.
After benchmark calibration, counterfactual experiments (for example,
an introduction of taxes or changes in labor endowment) are
performed. It is important to remember to change the iteration limit
before running counterfactuals, which is done with the GAMS statement
like NAME.ITERLIM=2000;. The solver will stop as soon as
this limit is reached if the solution is not found before a user
defined number of iterations. In this paper, we will not describe any
counterfactuals with our static model^{18}. Our interest here is
different and consists of the conversion of the static model into a
dynamic one.
4 Dynamic models
When I had started to learn economic modeling, I immediately wanted
to build a dynamic model. I wanted to predict the future, something
which struck me as patently impossible with a static model. Later on,
I realized that my attempts of dynamic modeling were akin to building
a spaceship with a screwdriver. Besides, I wanted too much out of
dynamic models. Dynamic models are nowhere near the goal of
predicting the future. A model can more or less tell you what is
going to happen if there are no shocks and structural changes in an
economy. In addition, one need to make assumptions about the rate of
economic growth for several decades into the future, the rate of time
preference, the growth rate of population, inflation, depreciation,
etc. All these necessary assumptions bring us very far from the
reality. But policy makers still need to make their decisions and
economists need to provide answers about the future. Therefore,
dynamic general equilibrium models are important tools for economic
policy evaluation. But now I know that a good static model is much
more useful than a bad dynamic one; and good dynamic models are quite
rare.
With such an encouragement, we will attempt to develop a dynamic model
based on the Wonderland example. There are different approaches to
dynamic economic modeling (see Ginsburgh and Keyzer
(1997) for an overview). We limit our discussion to a simple
Ramsey model. The model behaves differently if it is on a so called
steadystate than it is not. A steadystate is defined as a situation
in which the various quantities (such as capital, output,
consumption, etc.) grow at constant rates. We start our consideration
with the situation when benchmark data describe an economy on a
steadystate in a base period.
4.1 An economy on a steadystate in a base period
In a dynamic version of the Wonderland example, a representative
consumer maximizes the present value of his lifetime utility

max
 
¥ å
t=0


æ è

1
1+r

ö ø

t

U(c_{t}), 
 (10) 
where t  time periods, r  individual timepreference parameter,
U  utility function, c_{t}  consumption in period t. The
consumer faces the following constraints. First, total output produced in
the economy is divided to consumption and investment, I_{t}. Second,
capital depreciates at the rate d. This constraints can be written as
c_{t} = F(K_{t}, L_{t})I_{t}, 
 (11) 
K_{t+1} = K_{t}(1d)+I_{t}, 
 (12) 
where K is capital and F represents production function.
To find the firstorder conditions, consider two periods: t and
t+1. A representative agent makes his decision not only about his
consumption today, but rather about consumption today and investment
today (which will determine his capital and consumption tomorrow). So
there are three decision variables: c_{t}, K_{t}, and I_{t} (It means
that we need to consider two constraints on capital, one constraint
is for K_{t}, and the other constraint is for K_{t+1}.) Therefore,
the Lagrangian is:
Á = 
æ è

1
1+r

ö ø

t

U(c_{t}) +l_{1} (F(K_{t},L_{t})I_{t}c_{t}) + l_{2}(K_{t1}(1d)+I_{t1}K_{t}) + l_{3} (K_{t}(1d)+I_{t}K_{t+1}) 
 (13) 
We have three Largange multipliers: l_{1} is a multiplier on a
consumption constraint, l_{2} is a multiplier on a "capital
today" constraint, and l_{3} is a multiplier on a "capital
tomorrow" constraint. Solving the utility maximization problem
results in the following firstorder conditions:

¶Á
¶c_{t}

= 
æ è

1
1+r

ö ø

t


¶U(c_{t})
¶c_{t}

 l_{1} = 0 
 (14) 

¶Á
¶K_{t}

= l_{1} 
æ è

¶F
¶K_{t}

ö ø

 l_{2} + l_{3}(1d) = 0 
 (15) 

¶Á
¶I_{t}

= l_{1} + l_{3} = 0 
 (16) 
Now recall that the Lagrange multiplier shows the sensitivity to
changes in the constraint. In economic terms it is a measure of value
of the scarce resources in the problem under consideration. In a
usual utility maximization problem (maximization of utility subject
to a budget constraint), the Lagrange multiplier can be interpreted
as the marginal utility of (budget) money. In a usual cost
minimization problem the Lagrange multiplier can be interpreted as a
marginal cost of production (or the internal value, or imputed value,
or more frequently, the shadow price). Mathematically, all these
problems are identical. What is different is our interpretation. So
in our Lagrange optimization the multuplier shows the shadow price
(or marginal cost). Under constant returns to scale and perfect
competition assumptions, price equals marginal cost. Therefore, we can
rewrite the firstorder conditions as:
P_{t} = ( 
1
1+r

)^{t} 
¶U(c_{t})
¶c_{t}

, 
 (17) 
PK_{t} = (1d)PK_{t+1} + P_{t} 
¶F(K_{t}, L_{t})
¶K_{t}

, 
 (18) 
where P_{t}, PK_{t}, and PK_{t+1} are the values of the
corresponding Lagrange multipliers. They can be interpreted as the
price of output, the price of capital today, and the price of capital
tomorrow, respectively. The utility maximization problem above is
formulated as an NLP problem.
As has already been discussed, MPSGE solves it as an MCP problem. Let
RK_{t} and W_{t} represent rental rate of capital and wage rate in
period t. Denote a unit cost function as C(RK_{t},W_{t}) (Unit cost function is a solution of the problem:
min (W_{t} L_{t} + RK_{t} K_{t}) s.t. F(K_{t},L_{t}) = 1) , and a demand
function as D(P_{t}, M) (Demand function is a solution of the
problem: max å(1/(1+r))^{t}U(c_{t}) s.t. åP_{t} c_{t} = M) , where
M is consumer's income. Then MCP can be formulated as
follows^{19}.
Zero profit conditions:
P_{t} ³ PK_{t+1}, I_{t} ³ 0, I_{t}(P_{t}  PK_{t+1}) = 0 
 (20) 
PK_{t} ³ RK_{t} + (1d) PK_{t+1}, K_{t} ³ 0, K_{t}(PK_{t}  RK_{t} +(1d) PK_{t+1}) = 0 
 (21) 
C(RK_{t}, W_{t}) ³ P_{t}, Y_{t} ³ 0, Y_{t}(C(RK_{t}, W_{t})  P_{t})=0 
 (22) 
Market clearance conditions:
Y_{t} ³ D(P_{t},M)+I_{t}, P_{t} ³ 0, P_{t}(Y_{t}  D(P_{t},M)+I_{t}) = 0 
 (23) 
L_{t} ³ Y_{t} 
¶C(RK_{t},W_{t})
¶W_{t}

, W_{t} ³ 0, W_{t}(L_{t}  Y_{t} 
¶C(RK_{t},W_{t})
¶W_{t}

) = 0 
 (24) 
K_{t} ³ Y_{t} 
¶C(RK_{t},W_{t})
¶RK_{t}

, RK_{t} ³ 0,RK_{t}(K_{t}  Y_{t} 
¶C(RK_{t},W_{t})
¶RK_{t}

= 0 
 (25) 
Income balance condition:
M = PK_{0} K_{0} + 
¥ å
t=0

W_{t} L_{t}, M > 0 
 (26) 
As noted before, a modeller does not need to program these
equilibrium conditions explicitly. MPSGE constructs them
automatically. However, we need to describe a change in capital over
time. This requires modification of the SAM, presented in Table 1.
We add one more production sector for investment. This, in turn, will
change the composition of the welfare block W, because now output can
be consumed or invested, rather than just consumed as in the static
case.
For the Wonderland example, a steadystate requires that
investment is equal to 70. How did we come to this number?
In order to quantify the value for investment on a steadystate
growth path, we need to describe the evolution of capital and labor
over time. It requires assumptions on the growth rate of labor,
g, capital depreciation rate, d, and interest rate, r.
Denoting the initial labor force as L_{0}, labor at time t is
L_{t} = L_{0} (1+g)^{t} , 
 (27) 
or, equivalently,
Evolution of capital is given by equation (12). Note that if in a
base period an economy is on a steadystate growth path, then all
quantities (capital, labor, output, consumption) grow at the
same constant rate g (off steadystate case is considered in
the next section). As such, an equation for capital growth can be
represented as
In addition, we assume a constant interest rate r, so all future
prices (including price of labor and capital) in terms of present
value are:
Capital can be bought or rented. Therefore, implementation of
dynamics involves two prices for capital: purchase price, PK, and
rental price, RK. It should be stressed that in a static SAM (Table
1), the total value of capital endowment, VK (Check the
intersection of PK and CONS in Table 1 to see that VK =100),
equals to capital earnings (and not to capital stock!). The
relationship between VK, rental price RK and a stock of
capital K is as follows:
Now we need to consider the firstorder conditions for capital and
investment. Let's rewrite them as
PK_{t} = (1d) PK_{t+1} + RK_{t} 
 (32) 
and
Equation (33) can be rearranged using equation (30) for PK:
Substitution of equation (34) for PK_{t} and equation (33) for PK_{t+1}
into (32) leads to:
(1+r) P_{t} = (1d) P_{t} + RK_{t} . 
 (35) 
As such, an equation for the rental price is^{20}:
From equations (12) and (29), we have the following rule for
investment on a steadystate
or using (31) and (36), investment in a base period is equal to
I_{0} = 
(d+g) VK_{0}
d+r

. 
 (38) 
Equation (38) describes the value of investment which we need to
introduce into the SAM for a steadystate growth path. We assume^{21} d = 0.05, g = 0.02, and r = 0.05. Then,
investment is I = (0.05+0.02)·100/(0.05+0.05) = 70. We assume
that goods are invested in the same proportion (35:35). In addition,
we need to modify the welfare block W by dividing it into
consumption and savings. The total amount of produced goods is
200, 70 of which is invested. It leaves 130 units for consumption in
a base period, 65 of which is consumption of good X and another 65 is
consumption of good Y. The benchmark SAM after these changes is
represented in Table 2.
 Production Sectors  Consumers 
Markets  X  Y  W  inv  CONS 
PX  100   65  35  
PY   100  65  35  
PW    200   200 
PL  40  60    100 
PK  60  40    100 
sav    70  70  
Table 2. Dynamic Model SAM
An important characteristic of the dynamic problem is a treatment of
capital in the last period of modeling. We cannot solve numerically
for an infinite number of periods, hence, some adjustments are needed
for approximation of finite horizon model to the infinite horizon
choices. Special procedures should be introduced for the terminal
capital, otherwise, all capital would be consumed in the last period
and nothing would be invested. The infinite horizon consumer problem
can be partitioned at year T with a single state variable
K_{T+1}, linking the two problems. This means that if you set the
terminal capital stock to the appropriate value, then the time path
of consumer demands will be identical to the infinite horizon
values^{22}.
Here we have followed the Rutherford's (1997) paper and introduced
the level of postterminal capital as a variable
and added a constraint on the growth rate of investment in the
terminal period:

I_{T}
I_{T1}

= 
Y_{T}
Y_{T1}

, 
 (39) 
where T is a terminal period. The advantage of the usage of
this constraint is that it imposes balanced growth in the terminal
period but does not require that the model achieve steadystate
growth. The meaning of the constraint is that investment in a
terminal period should grow at the same rate as output^{23}.
A program listing of the dynamic model is represented in Appendix 3
(file move2_1.gms). Below we list and then discuss the
changes which are necessary to make in the code of the static model.
 Introduce time sets;
 Declare the assumed interest rate, growth rate and depreciation rate;
 Declare four more scalars: rental rate of capital in a base
period, RK0, initial capital stock, K0, initial
investment, I0, and initial capital earnings, VK;
 Declare two parameters which represent the growth rate of
quantities, QREF(T) (i.e., an index of economic activity over the
model horizon in the steadystate equilibrium), and the growth rate of prices,
PREF(T) (i.e., an index of present value prices over the model
horizon in a steadystate equilibrium);
 Introduce two more production blocks: capital accumulation,
K, and investment, I;
 Change the reference quantities in the $PROD:W block:
130, 65, 65 instead of 200, 100, 100 to represent an adjustment for
the level of consumption in a base period;
 Change the reference quantities in the $DEMAND
block: base year consumption is 130 instead of 200 and baseyear capital
stock is K0 instead of 100 (which was a representation
of capital earnings, VK); adjust labor
endowment and consumption for the growth rates QREF and
PREF;
 Introduce terminal capital in $AUXILIARY, $CONSTRAINT
and $DEMAND blocks;
 Make initial values assignment.
Introduction of time sets.
Introduction of time sets requires three sets T, TFIRST, and
TLAST, GAMS code for which is
SET T /1*10/, TFIRST(T), TLAST(T);
TFIRST(T) = YES$(ORD(T) EQ 1);
TLAST(T) = YES$(ORD(T) EQ CARD(T));
In the first line we declare the sets. We have 10 periods. Because
of the importance of the base year period TFIRST and the
terminal period TLAST, they are declared as separate sets
(and subsets of T). The next two lines assign the values for the
first and last periods. It is done in a way which may seem a bit odd
for a novice in GAMS. The purpose of this is convenience; because it
allows you to change the time dimension of the model in a single
line. For example, extension of the model to 100 periods requires
only one change to T /1*100/ .
Declaration of the assumed parameters.
We assume g = 0.02, d = 0.05, and r = 0.05, which is
declared in the following GAMS statement.
SCALAR DELTA DISCOUNT FACTOR /0.05/
R INTEREST RATE /0.05/
G GROWTH RATE /0.02/;
Declaration of initial parameters for capital and
investment.
We directly assign a value for initial capital earnings, VK, as
SCALAR VK INITIAL CAPITAL EARNINGS /100.0/;
Also, from equations (36), (31), and (37) the values for RKO,
KO and IO are
RK0 = DELTA+R;
K0 = VK/RK0;
I0 = (DELTA + G) * K0;
Declaration of growth rates for prices and quantities.
This step implements equations (29) and (30) for the growth rates in
quantities and prices, which are
QREF(T) = (1 + G) ** (ORD(T)  1);
PREF(T) = (1/(1 + R)) ** (ORD(T)  1);
We have ORD(T)1 as an exponent to represent the fact that
in the base year QREF and PREF are equal to 1 and
grow thereafter.
Production blocks for capital and investment.
New production block for K(T) implements capital
accumulation as it represented by equation (32). The MPSGE format of
the production block is (we skip terminal capital for a moment):
$PROD:K(T)
O:PK(T+1) Q:(K0*(1DELTA))
O:RK(T) Q:(K0*(R+DELTA))
I:PK(T) Q:K0
Those who learn the MPSGE representation of the Ramsey dynamic model
are mostly confused by the reference quantity of RK(T).
The difference with equation (32) is that in the production block the
reference quantity for capital rental price^{24} is K0*(R+DELTA), whereas in equation (32) (r+d) does not appear
as a coefficient of RK_{t}. The dynamic model would not calibrate
without this reference quantity. The reason for this is the fact that
RK is used in several production blocks: X, Y, and K and we
need to to reflect the relationship between RK_{t} and P_{t} given by
equation (36)
Alternatively, we could have used K0 as a reference price for
O:RK(T) and assign the rental price growth as
RK.L(T) = (DELTA+R)*PREF(T);, but in this case the input fields
for production of X and Y blocks should also be changed as
represented in the program move2_2.gms of Appendix 3.
Production block I introduces equation (33). We assume that investment
is split equally between two production sectors.
$PROD:I(T) S:0
O:PK(T+1) Q:I0
I:PY(T) Q:(0.5*I0)
I:PX(T) Q:(0.5*I0)
Note that S:0 corresponds to Leontief production function,
i.e., elasticity of substitution between inputs is equal to zero. If
no elasticity is specified in a production block, MPSGE uses zero
elasticity as a default value. Therefore, S:0 can be skipped
from the code of this block.
Change in the Welfare block.
We need to make changes in the production of W, because in the
dynamic version of the model not all output is consumed but some
portion of it is invested. From Table 2 we know the new values for output
(Q:130) and inputs (Q:65).
Change in the Demand block.
For the same reason, in the DEMAND block we need to modify a
reference quantity for PW(T) from 200 to 130. Also, growth in
consumption and labor over time is represented by QREF(T).
We use the reference price PREF(T) as the value of the
discount on the future consumption given by equation (10).
Introduction of individual timepreference as a separate parameter is
not needed because in the Ramsey model, the steadystate leads to a
constant pattern of per capita consumption and to the condition that
r = r.
Another change in the DEMAND block involves the usage of initial
capital stock K0 as a reference quantity for PK(TFIRST),
instead of the value of capital VK as in the static case. This is
determined by income balance condition of the dynamic model.
One more modification in the DEMAND block is the introduction of
intertemporal elasticity of substitution s:1. Note that we
have assumed a CobbDouglas case in our simple dynamic
model. In general, we could have introduced a scalar SIGMA
and use s:SIGMA as an elasticity parameter (see Appendix 1
for conversion utility functions into the MPSGE format).
Terminal capital constraint.
In order to introduce a constraint in MPSGE, we need to use two types
of blocks, which we have not discussed yet. A constraint is declared
in $AUXILIARY block and then formulated in $CONSTRAINT block. For our model, we need to introduce a
constraint on terminal capital given by equation (39). Therefore, a
constraint TK is declared. Also, in order to induce investment in
the last period of modeling, a price PKT is introduced. It
represents the market for a postterminal capital.
The postterminal capital is output of the production blocks K and
I. The condition $TLAST(T) shows that the postterminal
capital is going to be produced only in the last period. In CGE
models, everything which is produced has to be consumed, therefore,
an endowment of terminal capital in the $DEMAND block is
introduced as
E:PKT Q:(1) R:TK
Negative value of the endowment represent the fact that terminal
capital is going to be consumed. The field R:TK implies that
the following condition has to
be satisfied:
SUM(T$TLAST(T), I(T)/I(T1)  Y(T)/Y(T1)) =G= 0;
Summation sign, SUM, is used due to the GAMS syntax which
does not directly allow the assignment of a set member to a variable
with a different domain.
Initial values.
The assignment is done after the $SYSINCLUDE line but
before the $INCLUDE line of the program. The initial values in our
model are assigned to reflect present values (prices are adjusted for
PREF(T) which is given by equation (30)) and growth in
quantities (quantities are adjusted for QREF(T) as
represented by equation (29)). The only one exception is
PK.L(T)=(1+R)*PREF(T), which reflects equation (34).
The initial values for the level and price of a terminal capital
are determined by the same logic as for other quantities and prices
as^{25}:
TK.L = K0 * (1 + G) ** CARD(T);
PKT.L = SUM(TLAST, PK.L(TLAST)/(1+R));
The solution listing (run the program move2_1.gms to obtain
the listing) shows that all marginals are equal to zero, activity
levels are rising over time and prices are falling over time. Total
consumption has the value of 1145. The price of capital differs from
all other prices by (1+r). Table 3 summarizes the
results^{26}. After benchmark
replication, one can run counterfactual experiments, such as
introduction of taxes, change in growth rates, elasticities, etc.
 Quantities  Prices  X  Y  W  K  I 
1  1.0000  1.0000  100.00  100.00  130.00  1000.00  70.00 
2  1.0200  0.9524  102.00  102.00  132.60  1020.00  71.40 
3  1.0404  0.9070  104.04  104.04  135.25  1040.40  72.83 
4  1.0612  0.8638  106.12  106.12  137.96  1061.21  74.29 
5  1.0824  0.8227  108.24  108.24  140.72  1082.43  75.77 
6  1.1041  0.7835  110.41  110.41  143.53  1104.08  77.29 
7  1.1262  0.7462  112.62  112.62  146.40  1126.16  78.83 
8  1.1487  0.7107  114.87  114.87  149.33  1148.69  80.41 
9  1.1717  0.6768  117.17  117.17  152.32  1171.66  82.02 
10  1.1951  0.6446  119.51  119.51  155.36  1195.09  83.66 
Table 3. Steadystate prices and quantities
4.2 Calibrating dynamic models to a benchmark data which are
not on a steadystate
Data for a base period may be inconsistent with a steadystate growth
path. Table 4 gives an example of the situation where investment is "too
big", i.e., from equation (38) the value of investment is 80 > (0.05+0.02) ·100/(0.05+0.05) = 70.
 Production Sectors  Consumers 
Markets  X  Y  W  Inv  CONS 
PX  100   60  40  
PY   100  60  40  
PW    200   200 
PL  40  60    100 
PK  60  40    100 
sav    80  80  
Table 4. A Dynamic Model SAM off a SteadyState
One possible solution for model calibration in the case of the
offsteadystate data is to assume that g
is the labor growth rate, but the capital growth rate is different
initially and converges to g over time^{27}. We use the
calibration which is based on the Secant Method (for a description of
the Secant Method see, for example, Press et al (1992), or just search
the Internet for "secant method").
The method finds the solution to an equation f(x)=0 as:
1. Initial guess on x(n), where n is the number of iteration.
2. Calculate x(n+1) as^{28}:
x_{n+1}=x_{n} 
f(x_{n})
f(x_{n})f(x_{n1})

(x_{n}x_{n1}). 
 (40) 
When the secant method converges it usually does so in 5 to 7
iterations if the initial guesses are good enough.
To reiterate our current dynamic problem, we know the initial values
given in the SAM. The SAM is not consistent with a steadystate growth
path. Also, we know (assumed) depreciation rate and longrun interest
rate but the rental price in a base period is unknown. Our goal is
calibrating a dynamic model to the data represented in the SAM. In
order to do that we use iterations based on the secant algorithm
described above. The calibration involves the following iteration
process: guessing initial rental rate of capital, RK_{0}, calculating
a model of the transition path, observing investment, I_{0}, in the
current equilibrium, and adjusting RK_{0}.
The program listing for the offsteadystate dynamic model is
provided in Appendix 3 (file move3_1.gms). At first, we
guess about the rental price, RKG, in the
first period. In the onsteadystate case the rental price (which is
also equal to return to capital) is d+ r. We make our guess
using this knowledge. Note that if our guess is "too far", the
algorithm does not work. Also, if investment is "too far" from the
steadystate value, the algorithm does not work as well.
The offsteadystate dynamic program has the same structure as
before, except for the fact that we have used an alternative
formulation similar to the program described in Appendix 3 (file
move2_2.gms). We apply equation (40) for finding f(x),
which is in our case the difference between the value of investment
calculated in the program and the one given in the SAM. Accordingly,
x(n) is the guess on rental price at the first period. During the
first iteration we simply add 0.1 to our guess value for RK_{0} in
order to produce a basis for further iterations. During all other
iterations we reproduce equation (40) as
RKG = RKG + ERROR(ITER)/(ERROR(ITER1)ERROR(ITER))
* (RK0VAL(ITER)RK0VAL(ITER1));
where ERROR represents^{29} f(x) and RK0VAL represents x(n).
The last portion of the program includes graphing the results for
investment, consumption, growth rate and interest rate. We have used
GNUPLOT utility to produce graphs. It can be seen from the graphs
(run the model move3_1.gms to obtain the graphs) that
the values eventually converge to a steadystate growth path. The
speed of convergence to a steadystate is determined by production
and preference parameters (Barro and SalaiMartin, 1995).
5 Conclusion
Learn MPSGE!!!
References
 [1]

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Barro, R.J., and X. SalaiMartin (1995). Economic Growth, McGrawHill.
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http://debreu.colorado.edu/primer/paper.htm)
A version of the paper also appears as:
Lau M.I., A. Pahlke and T. F. Rutherford (2002). "Approximating infnitehorizon
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Norton & Company.
Appendix 1. Conversion of production and utility functions
into the MPSGE format
MPSGE represents production and preferences as nested CES functions.
This Appendix provides a very brief discussion of MPSGE production
and consumption blocks. For more information about CES functions and
their calibrated share form, see Rutherford (2002).
Production:
A production technology is identically represented by either the
production function or corresponding cost function. A general form of
a nested CES production function is X(K,L)=(aK^{r}+ (1a)L^{r})^{[1/(r)]} where r is the substitution
parameter (¥ < r < 1), which is related to the elasticity
of substitution s as s = [1/(1r)]. When r = ¥ (or s = 0), CES production function converts into
Leontief (or fixed proportions) production function X_{1}(K,L) = min(aK_{1},(1a)L_{1}). When r = 1 (or s = ¥), then
inputs are perfect substitutes in production X_{2} = aK_{2} + (1a)L_{2}.
When r = 0 (or s = 1), then CES production function
converts into a CobbDouglas case X_{3}(K,L) = K_{3}^{a} L_{3}^{1a}.
Let's focus on a CobbDouglas case X(K,L) = Z K^{a} L^{1a}, where
Z is a scale parameter. If we have data for output [`X], capital
[`K], labor [`L], capital price [`r], and labor price
[`w], we can calibrate function coefficients in the following way:
MPSGE representation of a CobbDouglas production function
is:
$PROD:X s:1
O:PX Q:Y
I:PK Q:K P:r
I:PL Q:L P:w
where s:1 represents the elasticity of substitution for a
CobbDouglas technology (s = 1), whereas Y, K, L, r, w come
from benchmark data used for calibration.
Consider MPSGE representation of a particular example of
CobbDouglas production function: X(K,L)=K^{0.6}L^{0.4}. Based on
equations (41) and (42), the following production block represents
the technology:
$PROD:X s:1
O:PX Q:(0.6**0.6 * 0.4**0.4)
I:PK Q:0.6
I:PL Q:0.4
As such, the following blocks:
a)
$PROD:X s:1
O:PX Q:1
I:PK Q:0.6
I:PL Q:0.4
b)
$PROD:X s:1
O:PX Q:100
I:PK Q:60
I:PL Q:40
c)
$PROD:X s:1
O:PX Q:100
I:PK Q:60 P:2
I:PL Q:40 P:2
d)
$PROD:X s:1
O:PX Q:100 P:2
I:PK Q:60
I:PL Q:40
e)
$PROD:X s:1
O:PX Q:100 P:2
I:PK Q:60 P:2
I:PL Q:40 P:2
represent the same technology X(K,L) = Z K^{0.6}L^{0.4}, where
from equation (42) Z = 1.96. However, a modeller should be careful,
because rescaling of coefficients in the MPSGE blocks gives a
different level of output.
Also remember that reference prices and quantities are used for
calibration only and not transferred to a solver as initial values.
Another important issue is that only relative prices matter for
technology representation. As such, use of P:1 or P:2 for both inputs gives the same marginal rate of substitution.
Price of output does not matter in this example because we have one
output only. In the case of two outputs relative prices would matter
as they determine marginal rate of transformation.
Now consider a production block:
$PROD:X s:1
O:PX Q:1
I:PK Q:0.6 P:2
I:PL Q:0.4
This production block is described by the function
X(K,L) = Z K^{0.75}L^{0.25}, because the value share from
equation (41) is a = [1.2/(1.2+0.4)]=0.75, and from equation
(42) Z = 1/(0.6**0.75 * 0.4**0.25) = 1.844.
Leontief production function can be represented either as
$PROD:X s:0
O:PX Q:100
I:PK Q:60
I:PL Q:40
or as
$PROD:X
O:PX Q:100
I:PK Q:60
I:PL Q:40
because default value for top level elasticity is zero.
Preferences:
General form of a nested CES utility function is
U(X,Y)=(aX^{r} + (1a)Y^{r})^{[1/(r)]}
where r is an elasticity parameter. Elasticity of substitution
s is related to elasticity parameter as s = [1/(1r)]. $DEMAND block represents utility
derived from consumption of goods and consumer's endowments. If
quantities demanded and endowments are all equal to 100, then demand
block in MPSGE is:
$DEMAND:CONS s:(1/(1RHO))
D:PX Q:100
D:PY Q:100
E:PL Q:100
E:PK Q:100
There is a particular utility function which is widely used in a
dynamic analysis. It is a Constant Intertemporal Elasticity of
Substitution utility function (CIES), which has a form:
U(C_{t})=[(C_{t}^{1q}1)/(1q)], where
s = [1/(q)] is the elasticity of substitution.
As q® 0, U(C) approaches a linear case U(C_{t})=C_{t}  1
(which represent the same preferences as U(C_{t})=C_{t}). As q®1, then U(C_{t}) = ln(C_{t}).
In order to see a relationship between CES and CIES utility
functions, we start with CES form:
U(C_{t})=(aC_{t}^{r})^{[1/(r)]}. Then substitute
r = 1q because s = [1/(q)] and s = [1/(1r)]. It leads to
U(C)=(aC^{1q})^{[1/(1q)]}. Maximization of
(aC^{1q})^{[1/(1q)]} and
[(aC^{1q})/(1q)] produces the same result for optimal
value of C (because maxX^{[1/b]} Û maxX^{b} Û max[X/b]). For utility maximization over
infinite horizon we sum over instanteneous utilities and adjust for a
discount factor, which leads to a traditional form of utility function:
U = å^{¥}_{t=0} ([1/(1+r)])^{t} [(C_{t}^{1q}1)/(1q)].
If a consumer has an endowment of labor and capital and elasticity of
intertemporal substitution SIGMA between consumption in different
periods is defined as SIGMA=1/THETA; to reflect
the relationship between s and q, then $DEMAND block for CIES utility function has the following form:
$DEMAND:CONS s:SIGMA
D:PC(T) Q:QREF(T) P:PREF(T)
E:PL(T) Q:QREF(T)
E:PK(TFIRST) Q:K0
Note that if consumer gets utility from two goods (for example, in a
CobbDouglas form) we can keep the same $DEMAND block as
above and add a production block
$PROD:C(T) s:1
O:PC(T) Q:C0
I:PX(T) Q:X0
I:PY(T) Q:Y0
Appendix 2. MPSGE and algebraic formulation
Appendix 2 contains the MPSGE and algebraic (MCP) versions for the following
problem (Shoven and Whalley, Applied GeneralEquilibrium Models of
Taxation and International Trade: An Introduction and Survey, Journal
of Economic Literature, Vol. 22, No. 3. (Sep., 1984), pp. 10071051).
It is provided for illustration purposes. One can compare the degree of
difficulty in both versions. If you are interested in an MCP version
of a dynamic Ramsey model, see Rutherford's [1997] paper. I have some
preliminary notes on conversion of MPSGE into MCP. Check with me if I
have finished them yet.
In this model, there are two final commodities (M, N), two factors of
production (K, L) and two consumers (R,P). Producers are competitive,
minimizing unit cost taking market prices as given.
Production functions are of the constantelasticityofsubstitution
(CES) form:
Q_{i} = f_{i} [d_{i} L_{i}^{ri} + (1d) K_{i}^{ri}]^{1/ri} i Î M,N 

where r_{i} = (s_{i}  1)/s_{i}, and s_{i} is an elasticity
of substitution between capital and labor. Parameters of the
production function are given by:
Sector  f_{i}  d_{i}  s_{i} 
Manufacturing (M)  1.5  0.6  2.0 
NonManufacturing (N)  2.0  0.7  0.5 
Consumers are endowed with primary factors form which they earn
income. This income is used to purchase goods to maximize utility. The CES
utility functions are:
U_{c} = 
é ë

å
i

a_{ic}^{1/sc} X_{ic}^{rc} 
ù û

1/r_{c}

c Î R,P 

where r_{c} = (s_{c}  1)/s_{c}, and s_{c} is an elasticity
of substitution between goods M and N. Commodity endowments and
parameters of the utility function are given by:
Household  K  L  a_{M}  a_{N}  s_{c} 
Rich  25  0  0.5  0.5  1.5 
Poor  0  60  0.3  0.7  0.75 
After computing the benchmark equilibrium, a single counterfactual
experiment is done in which a 50% advalorem tax is levied on each
unit of capital services employed in the manufacturing sector. Tax
revenue is returned as lumpsum income for the two households, 40%
to the rich and 60% to the poor.
$title Compare MPSGE and its algebraic representation
set i /M, N/;
set c /RICH, POOR/;
set rich(c) /rich/;
set poor(c) /poor/;
parameters
sigma /m 2, n 0.5/,
phi /m 1.5, n 2/,
delta /m 0.6, n 0.7/,
sig /rich 1.5, poor 0.75/,
capital /rich 25, poor 0/,
labor /rich 0, poor 60/;
table alpha(c,i)
M N
RICH 0.5 0.5
POOR 0.3 0.7 ;
parameter taxrate(i);
$ontext
$model:work
$SECTORS:
Y(i) !Production
$COMMODITIES:
P(i) !Price
W !Price of labor
R !Price of capital
$CONSUMERS:
cons(c) ! Representative agent
$prod:y(i) s:sigma(i)
o:p(i) q:phi(i)
i:w q:1 p:delta(i)
i:r q:1 p:(1delta(i))
+ a:cons("rich") t:(0.4*taxrate(i))
+ a:cons("poor") t:(0.6*taxrate(i))
$demand:cons(c) s:sig(c)
d:p(i) q:1 p:(alpha(c,i)**(1/sig(c)))
e:r q:capital(c)
e:w q:labor(c)
$offtext
$sysinclude mpsgeset work
taxrate(i) = 0;
work.iterlim = 5000;
$include work.gen
solve work using mcp;
taxrate("M") = 0.5;
$include work.gen
solve work using mcp;
* MCP version
alias(i,j);
taxrate("M") = 0;
parameter pk0(i);
pk0(i) = 1+taxrate(i);
parameter thetak(i), thetal(i);
thetak(i) = 1delta(i);
thetal(i) = delta(i);
parameter abeta(c,i);
abeta(c,i) = alpha(c,i)**(1/sig(c));
equations
prf_y(i)
mkt_y(i)
mkt_k
mkt_l
i_cons(c);
* Zero profit
prf_y(i)..
(thetak(i)*(r*(1+taxrate(i))/(pk0(i)*(1delta(i))))**(1sigma(i))
+ thetal(i)*(w/delta(i))**(1sigma(i)))**(1/(1sigma(i)))
=e= p(i) * phi(i);
* Market clearance
mkt_y(i)..
y(i) * phi(i) =e=
sum(c, 1 *
((((sum(j, abeta(c,j) * (p(j)/abeta(c,j))
** (1sig(c))))**(1/(1sig(c)))) * abeta(c,i) / p(i)) ** sig(c)) *
cons(c) / ( ((sum(j, abeta(c,j) * (p(j)/abeta(c,j))
** (1sig(c))))**(1/(1sig(c))))) );
mkt_k..
sum(c, capital(c)) =e=
sum(i,
((thetal(i)*(w/delta(i))**(1sigma(i))
+ thetak(i)*(r*(1+taxrate(i))/(pk0(i)*(1delta(i))))**(1sigma(i)))
**(1/(1sigma(i))) / (r*(1+taxrate(i))/(pk0(i)*(1delta(i)))))
**(sigma(i)) * y(i));
mkt_l..
sum(c, labor(c)) =e=
sum(i,
((thetal(i)*(w/delta(i))**(1sigma(i))
+ thetak(i)*(r*(1+taxrate(i))/(pk0(i)*(1delta(i))))**(1sigma(i)))
**(1/(1sigma(i))) * delta(i)/w)**(sigma(i)) * y(i));
i_cons(c)..
cons(c) =e= r*capital(c) + w*labor(c)
+ (sum(i, 0.6 * taxrate(i) * y(i) * r *
((thetal(i)*(w/delta(i))**(1sigma(i))
+ thetak(i)*(r*(1+taxrate(i))/(pk0(i)*(1delta(i))))**(1sigma(i)))
**(1/(1sigma(i)))
/ (r*(1+taxrate(i))/(pk0(i)*(1delta(i)))))**(sigma(i))))$poor(c)
+ (sum(i, 0.4 * taxrate(i) * y(i) * r *
((thetal(i)*(w/delta(i))**(1sigma(i))
+ thetak(i)*(r*(1+taxrate(i))/(pk0(i)*(1delta(i))))**(1sigma(i)))
**(1/(1sigma(i)))
/ (r*(1+taxrate(i))/(pk0(i)*(1delta(i)))))**(sigma(i))))$rich(c);
model obana /prf_y.y, mkt_y.p, mkt_k.r, mkt_l.w, i_cons.cons/;
cons.fx("rich") = 34.3368;
cons.fx("poor") = 60.0000;
obana.iterlim = 5000;
solve obana using mcp;
* Introduce tax
taxrate("M") = 0.5;
cons.fx("rich") = 29.0935;
cons.fx("poor") = 61.3484;
solve obana using mcp;
Appendix 3. Program listings
Appendix 3 contains GAMS programs which can be copied and executed.
The following files are located here.
move1_1.gms  Static model  The Wonderland example 
move1_2.gms   no welfare block 
move1_3.gms   initial quantities in the listing 
move1_4.gms   report block 
move1_5.gms   vector syntax 
move2_1.gms  Dynamic model  on a steadystate in a
baseyear 
move2_2.gms   alternative formulation of
capital block 
move3_1.gms   not on a steadystate in a
baseyear 
$TITLE: move1_1.gms  A Simple Static Model (The Wonderland example)
$ONTEXT
$MODEL:MOVE1_1
$SECTORS:
X ! Activity level for sector X
Y ! Activity level for sector Y
W ! Activity level for sector W (welfare index)
$COMMODITIES:
PX ! Price index for commodity X
PY ! Price index for commodity Y
PL ! Price index for primary factor L
PK ! Price index for primary factor K
PW ! Price index for welfare (expenditure function)
$CONSUMERS:
CONS ! Income level for consumer CONS
$PROD:X s:1
O:PX Q:100
I:PL Q:40
I:PK Q:60
$PROD:Y s:1
O:PY Q:100
I:PL Q:60
I:PK Q:40
$PROD:W s:1
O:PW Q:200
I:PX Q:100
I:PY Q:100
$DEMAND:CONS
D:PW Q:200
E:PL Q:100
E:PK Q:100
$OFFTEXT
$SYSINCLUDE mpsgeset MOVE1_1
MOVE1_1.ITERLIM = 0;
$INCLUDE MOVE1_1.GEN
SOLVE MOVE1_1 USING MCP;
$TITLE: move1_2.gms  A Simple Static Model (no welfare block)
$ONTEXT
$MODEL:MOVE1_2
$SECTORS:
X ! Activity level for sector X
Y ! Activity level for sector Y
$COMMODITIES:
PX ! Price index for commodity X
PY ! Price index for commodity Y
PL ! Price index for primary factor L
PK ! Price index for primary factor K
$CONSUMERS:
CONS ! Income level for consumer CONS
$PROD:X s:1
O:PX Q:100
I:PL Q:40
I:PK Q:60
$PROD:Y s:1
O:PY Q:100
I:PL Q:60
I:PK Q:40
$DEMAND:CONS s:1
D:PX Q:100
D:PY Q:100
E:PL Q:100
E:PK Q:100
$OFFTEXT
$SYSINCLUDE mpsgeset MOVE1_2
MOVE1_2.ITERLIM = 0;
$INCLUDE MOVE1_2.GEN
SOLVE MOVE1_2 USING MCP;
$TITLE: move1_3.gms. Simple Static Model: Formulation for Initial Quantities
$ONTEXT
$MODEL:MOVE1_3
$SECTORS:
X ! Activity level for sector X
Y ! Activity level for sector Y
W ! Activity level for sector W (welfare index)
$COMMODITIES:
PX ! Price index for commodity X
PY ! Price index for commodity Y
PL ! Price index for primary factor L
PK ! Price index for primary factor K
PW ! Price index for welfare (expenditure function)
$CONSUMERS:
CONS ! Income level for consumer CONS
$PROD:X s:1
O:PX Q:1
I:PL Q:0.4
I:PK Q:0.6
$PROD:Y s:1
O:PY Q:1
I:PL Q:0.6
I:PK Q:0.4
$PROD:W s:1
O:PW Q:1
I:PX Q:0.5
I:PY Q:0.5
$DEMAND:CONS
D:PW Q:200
E:PL Q:100
E:PK Q:100
$OFFTEXT
$SYSINCLUDE mpsgeset MOVE1_3
X.L=100;
Y.L=100;
W.L=200;
MOVE1_3.ITERLIM = 0;
$INCLUDE MOVE1_3.GEN
SOLVE MOVE1_3 USING MCP;
$TITLE: move1_4.gms  A Simple Static Model (report block)
$ONTEXT
$MODEL:MOVE1_4
$SECTORS:
X ! Activity level for sector X
Y ! Activity level for sector Y
W ! Activity level for sector W (welfare index)
$COMMODITIES:
PX ! Price index for commodity X
PY ! Price index for commodity Y
PL ! Price index for primary factor L
PK ! Price index for primary factor K
PW ! Price index for welfare (expenditure function)
$CONSUMERS:
CONS ! Income level for consumer CONS
$PROD:X s:1
O:PX Q:100
I:PL Q:40
I:PK Q:60
$PROD:Y s:1
O:PY Q:100
I:PL Q:60
I:PK Q:40
$PROD:W s:1
O:PW Q:200
I:PX Q:100
I:PY Q:100
$DEMAND:CONS
D:PW Q:200
E:PL Q:100
E:PK Q:100
$REPORT:
V:X_OUT O:PX PROD:X
V:K_X I:PK PROD:X
V:L_X I:PL PROD:X
V:Y_OUT O:PY PROD:Y
V:K_Y I:PK PROD:Y
V:L_Y I:PL PROD:Y
V:W_OUT O:PW PROD:W
V:X_W I:PX PROD:W
V:Y_W I:PY PROD:W
V:C D:PW DEMAND:CONS
$OFFTEXT
$SYSINCLUDE mpsgeset MOVE1_4
MOVE1_4.ITERLIM = 0;
$INCLUDE MOVE1_4.GEN
SOLVE MOVE1_4 USING MCP;
parameter report;
report("px","X") = x_out.l;
report("py","y") = y_out.l;
report("pw","w") = w_out.l;
report("pl","X") = l_x.l;
report("pk","X") = k_x.l;
report("pl","y") = l_y.l;
report("pk","y") = k_y.l;
report("px","w") = x_w.l;
report("py","w") = y_w.l;
report("pl","cons") = l_x.l+l_y.l;
report("pk","cons") = k_x.l+k_y.l;
report("pw","cons") = w_out.l;
display report;
$TITLE: move1_5.gms  A Simple Static Model (vector syntax)
set i Sectors /x,y/,
f Factors /k,l/;
TABLE sam(*,*) Benchmark data
X Y W CONS
X 100 100
Y 100 100
W 200 200
L 40 60 100
K 60 40 100 ;
PARAMETER Y0(I) Benchmark sectoral output,
FD0(F,I) Benchmark factor demands,
C0(I) Benchmark consumption demand,
E0(F) Factor endowments,
W0 Benchmark total consumption;
* Extract data from the original format into modelspecific arrays:
Y0(I) = SAM(I,I);
FD0(F,I) = SAM(F,I);
C0(I) = SAM(I,"W");
W0 = SUM(I, C0(I));
E0(F) = SAM(F,"CONS");
$ONTEXT
$MODEL:MOVE1_5
$SECTORS:
OUT(i) ! Production Activity Level
W ! Welfare Index
$COMMODITIES:
P(i) ! Price index for commodities
PF(f) ! Price index for factors
PW ! Utility Price Index
$CONSUMERS:
CONS ! Income level for consumer CONS
$PROD:OUT(i)
O:P(i) Q:Y0(i)
I:PF(f) Q:FD0(f,i)
$PROD:W s:1
O:PW Q:W0
I:P(i) Q:C0(i)
$DEMAND:CONS
D:PW Q:W0
E:PF(f) Q:E0(f)
$REPORT:
V:OUT_o(i) O:P(i) PROD:OUT(i)
V:INP(f,i) I:PF(f) PROD:OUT(i)
V:OUT_w O:PW PROD:W
V:INP_w(i) I:P(i) PROD:W
V:C D:PW DEMAND:CONS
$OFFTEXT
$SYSINCLUDE mpsgeset MOVE1_5
MOVE1_5.ITERLIM = 0;
$INCLUDE MOVE1_5.GEN
SOLVE MOVE1_5 USING MCP;
parameter report;
report(i,i) = out_o.l(i);
report("w","w") = out_w.l;
report(i,"w") = inp_w.l(i);
report(f,i) = inp.l(f,i);
report(f,"cons") = sum(i, inp.l(f,i));
report("w","cons") = out_w.l;
display report;
$TITLE: move2_1.gms. A Simple Ramsey Dynamic Model
SET T /1*10/,
TFIRST(T),
TLAST(T);
TFIRST(T) = YES$(ORD(T) EQ 1);
TLAST(T) = YES$(ORD(T) EQ CARD(T));
SCALAR DELTA DISCOUNT FACTOR /0.05/
R INTEREST RATE /0.05/
G GROWTH RATE /0.02/
VK INITIAL CAPITAL EARNINGS /100.0/
K0 INITIAL CAPITAL STOCK
RK0 INITIAL RETURN TO CAPITAL
I0 INITIAL INVESTMENT;
PARAMETER QREF(T) QUANTITIES
PREF(T) PRICES;
RK0 = DELTA+R;
K0 = VK/RK0;
I0 = (DELTA + G) * K0;
QREF(T) = (1 + G) ** (ORD(T)  1);
PREF(T) = (1/(1 + R)) ** (ORD(T)  1);
$ONTEXT
$MODEL:MOVE2_1
$SECTORS:
X(T) ! Activity level for sector X
Y(T) ! Activity level for sector Y
W(T) ! Activity level for sector W (welfare index)
I(T) ! Investment Sector
K(T) ! Capital Accumulation
$COMMODITIES:
PX(T) ! Price index for commodity X
PY(T) ! Price index for commodity Y
PL(T) ! Price index for primary factor L
PK(T) ! Price index for primary factor K
PW(T) ! Price index for welfare (expenditure function)
RK(T) ! Rental rate for capital
PKT ! Postterminal capital constraint
$CONSUMERS:
CONS ! Income level for consumer CONS
$AUXILIARY:
TK ! Terminal Capital Stock
$PROD:X(T) s:1
O:PX(T) Q:100
I:PL(T) Q:40
I:RK(T) Q:60
$PROD:Y(T) s:1
O:PY(T) Q:100
I:PL(T) Q:60
I:RK(T) Q:40
$PROD:K(T)
O:PK(T+1) Q:((1DELTA)*K0)
O:PKT$TLAST(T) Q:((1DELTA)*K0)
O:RK(T) Q:(K0*(DELTA+R))
I:PK(T) Q:K0
$PROD:I(T)
O:PK(T+1) Q:I0
O:PKT$TLAST(T) Q:I0
I:PY(T) Q:(0.5*I0)
I:PX(T) Q:(0.5*I0)
$PROD:W(T) s:1
O:PW(T) Q:130
I:PX(T) Q:65
I:PY(T) Q:65
$DEMAND:CONS s:1
D:PW(T) Q:(130*QREF(T)) P:PREF(T)
E:PL(T) Q:(100*QREF(T))
E:PK(TFIRST) Q:K0
E:PKT Q:(1) R:TK
$CONSTRAINT:TK
SUM(T$TLAST(T), I(T)/I(T1)  Y(T)/Y(T1)) =G= 0;
$OFFTEXT
$SYSINCLUDE mpsgeset MOVE2_1
X.L(T) = QREF(T);
Y.L(T) = QREF(T);
W.L(T) = QREF(T);
I.L(T) = QREF(T);
K.L(T) = QREF(T);
TK.L = K0 * (1 + G) ** CARD(T);
PX.L(T) = PREF(T);
PY.L(T) = PREF(T);
PL.L(T) = PREF(T);
PK.L(T) = (1+R)*PREF(T);
PW.L(T) = PREF(T);
RK.L(T) = PREF(T);
PKT.L = SUM(TLAST, PK.L(TLAST)/(1+R));
MOVE2_1.ITERLIM = 0;
$INCLUDE MOVE2_1.GEN
SOLVE MOVE2_1 USING MCP;
$TITLE move2_2.gms Simple Dynamic Model
* No RK0=DELTA+R in O:RK(T), but in initial RK.L and
* in production blocks
SET T /1*10/,
TFIRST(T),
TLAST(T);
TFIRST(T) = YES$(ORD(T) EQ 1);
TLAST(T) = YES$(ORD(T) EQ CARD(T));
SCALAR DELTA DISCOUNT FACTOR /0.05/
R INTEREST RATE /0.05/
G GROWTH RATE /0.02/
VK INITIAL CAPITAL EARNINGS /100.0/
K0 INITIAL CAPITAL STOCK
RK0 INITIAL RETURN TO CAPITAL
I0 INITIAL INVESTMENT;
PARAMETER QREF(T) QUANTITIES
PREF(T) PRICES;
RK0 = DELTA+R;
K0 = VK/RK0;
I0 = (DELTA + G) * K0;
QREF(T) = (1 + G) ** (ORD(T)  1);
PREF(T) = (1/(1 + R)) ** (ORD(T)  1);
$ONTEXT
$MODEL:MOVE2_2
$SECTORS:
X(T) ! Activity level for sector X
Y(T) ! Activity level for sector Y
W(T) ! Activity level for sector W (welfare index)
I(T) ! Investment sector
K(T) ! Capital accumulation
$COMMODITIES:
PX(T) ! Price index for commodity X
PY(T) ! Price index for commodity Y
PL(T) ! Price index for primary factor L
PK(T) ! Price index for primary factor K
PW(T) ! Price index for welfare (expenditure function)
RK(T) ! Rental rate for capital
PKT ! Postterminal capital constraint
$CONSUMERS:
CONS ! Income level for consumer CONS
$AUXILIARY:
TK ! Terminal Capital Stock
$PROD:X(T) s:1
O:PX(T) Q:100
I:PL(T) Q:40
I:RK(T) Q:(60/RK0) P:RK0
$PROD:Y(T) s:1
O:PY(T) Q:100
I:PL(T) Q:60
I:RK(T) Q:(40/RK0) P:RK0
$PROD:K(T)
O:PK(T+1) Q:((1DELTA)*K0)
O:PKT$TLAST(T) Q:((1DELTA)*K0)
O:RK(T) Q:K0
I:PK(T) Q:K0
$PROD:I(T)
O:PK(T+1) Q:I0
O:PKT$TLAST(T) Q:I0
I:PY(T) Q:(0.5*I0)
I:PX(T) Q:(0.5*I0)
$PROD:W(T) s:1
O:PW(T) Q:130
I:PX(T) Q:65
I:PY(T) Q:65
$DEMAND:CONS s:1
D:PW(T) Q:(130*QREF(T)) P:PREF(T)
E:PL(T) Q:(100*QREF(T))
E:PK(TFIRST) Q:K0
E:PKT Q:(1) R:TK
$CONSTRAINT:TK
SUM(T$TLAST(T), I(T)/I(T1)  Y(T)/Y(T1)) =G= 0;
$OFFTEXT
$SYSINCLUDE mpsgeset MOVE2_2
X.L(T) = QREF(T);
Y.L(T) = QREF(T);
W.L(T) = QREF(T);
I.L(T) = QREF(T);
K.L(T) = QREF(T);
TK.L = K0 * (1 + G) ** CARD(T);
PX.L(T) = PREF(T);
PY.L(T) = PREF(T);
PL.L(T) = PREF(T);
PK.L(T) = (1+R)*PREF(T);
PW.L(T) = PREF(T);
RK.L(T) = RK0*PREF(T);
PKT.L = SUM(TLAST, PK.L(TLAST)/(1+R));
MOVE2_2.ITERLIM = 0;
$INCLUDE MOVE2_2.GEN
SOLVE MOVE2_2 USING MCP;
$TITLE move3_1.gms. Simple Dynamic Model with off steadystate investment
$ontext
Before dynamics. Static model SAM.
Production Sectors Consumers
Markets  X Y W  CONS

PX  100 100 
PY  100 100 
PW  200  200
PL  40 60  100
PK  60 40  100

After dynamics. SAM is consistent with a steadystate in
a base period.
 X Y W Inv  CONS

PX  100 65 35 
PY  100 65 35 
PW  200  200
PL  40 60  100
PK  60 40  100
sav  70 70 
After dynamics. SAM is away of a steadystate in base period.
 X Y W Inv  CONS

PX  100 60 40 
PY  100 60 40 
PW  200  200
PL  40 60  100
PK  60 40  100
sav  80 80 
$OFFTEXT
SET T /1*100/,
TFIRST(T),
TLAST(T);
TFIRST(T) = YES$(ORD(T) EQ 1);
TLAST(T) = YES$(ORD(T) EQ CARD(T));
set trep(t) period labels for investment report /1*5/,
tdecade(t) /1,10,20,30,40,50,60,70,80,90,100/;
SCALAR DELTA DISCOUNT FACTOR /0.05/
R INTEREST RATE /0.05/
G GROWTH RATE /0.02/
KZERO INITIAL CAPITAL EARNINGS /100.0/
K0 INITIAL CAPITAL STOCK
RK0 INITIAL RETURN TO CAPITAL
RKG GUESS ON INITIAL RETURN TO CAPITAL
I0 INITIAL INVESTMENT;
PARAMETER QREF(T) QUANTITIES
PREF(T) PRICES;
* IN THE ONSS CASE, LABOR AND CAPITAL GROW AT A RATE G.
* INITIAL RETURN TO CAPITAL IS RKO = DELTA+R;
* IN THE OFFSS CASE LABOR GROWS AT A RATE G, CAPITAL GROWS AT A
* DIFFERENT RATE, DETERMINED BY THE INITIAL RETURN TO CAPITAL
* (DENOTED AS RKG). WE DON'T KNOW RKG, SO WE MAKE A GUESS TRYING
* TO BE CONSISTENT WITH GIVEN INITIAL INVESTMENT.
RK0 = DELTA+R;
K0 = KZERO/RK0;
I0 = (DELTA + G) * K0 +10;
* RK0 is unknown, we use formulas for RK0, K0, I0 for consistency
* with the on SS dynamic model. We could also simply use the
* declarations as K0 = 1000; IO = 80; (we know these values
* from SAM).
RKG = 0.12;
QREF(T) = (1 + G) ** (ORD(T)  1);
PREF(T) = (1/(1 + R)) ** (ORD(T)  1);
$ONTEXT
$MODEL:DYNAMIC
$SECTORS:
X(T) ! ACTIVITY LEVEL FOR SECTOR X
Y(T) ! ACTIVITY LEVEL FOR SECTOR Y
W(T) ! ACTIVITY LEVEL FOR SECTOR W
I(T) ! INVESTMENT SECTOR
K(T) ! CAPITAL ACCUMULATION
$COMMODITIES:
PX(T) ! PRICE INDEX FOR COMMODITY X
PY(T) ! PRICE INDEX FOR COMMODITY Y
PL(T) ! PRICE INDEX FOR PRIMARY FACTOR L
PK(T) ! PRICE INDEX FOR PRIMARY FACTOR K
PW(T) ! PRICE INDEX FOR WELFARE
RK(T) ! RENTAL RATE FOR CAPITAL
PKT ! POSTTERMINAL CAPITAL CONSTRAINT
$CONSUMERS:
CONS ! INCOME LEVEL FOR CONSUMER CONS
$AUXILIARY:
TK ! TERMINAL CAPITAL STOCK
$PROD:X(T) S:1
O:PX(T) Q:100
I:PL(T) Q:40
I:RK(T) Q:(60/RKG) P:RKG
$PROD:Y(T) S:1
O:PY(T) Q:100
I:PL(T) Q:60
I:RK(T) Q:(40/RKG) P:RKG
$PROD:K(T)
O:PK(T+1) Q:(1DELTA)
O:PKT$TLAST(T) Q:(1DELTA)
O:RK(T) Q:1
I:PK(T) Q:1
$PROD:I(T)
O:PK(T+1) Q:I0
O:PKT$TLAST(T) Q:I0
I:PY(T) Q:(0.5*I0)
I:PX(T) Q:(0.5*I0)
$PROD:W(T) s:1
O:PW(T) Q:130
I:PX(T) Q:65
I:PY(T) Q:65
$DEMAND:CONS s:1
D:PW(T) Q:(130*QREF(T)) P:PREF(T)
E:PL(T) Q:(100*QREF(T))
E:PK(TFIRST) Q:(KZERO/RKG)
E:PKT Q:(1) R:TK
$REPORT:
V:C(T) D:PW(T) DEMAND:CONS
V:XX(T) O:PX(T) PROD:X(T)
V:YY(T) O:PY(T) PROD:Y(T)
$CONSTRAINT:TK
SUM(T$TLAST(T), I(T)/I(T1)  Y(T)/Y(T1)) =G= 0;
$OFFTEXT
$SYSINCLUDE MPSGESET DYNAMIC
X.L(T) = QREF(T);
Y.L(T) = QREF(T);
W.L(T) = QREF(T);
I.L(T) = QREF(T);
K.L(T) = K0*QREF(T);
TK.L = K0 * (1 + G) ** CARD(T);
PX.L(T) = PREF(T);
PY.L(T) = PREF(T);
PL.L(T) = PREF(T);
PK.L(T) = (1+R)*PREF(T);
PW.L(T) = PREF(T);
RK.L(T) = RKG*PREF(T);
PKT.L = SUM(TLAST, PK.L(TLAST)/(1+R));
SET ITER JACOBISECANT ITERATIONS / ITER1*ITER6/;
PARAMETER
INVEST INVESTMENT PATH
INTRATE INTEREST RATE
CONSUM CONSUMPTION LEVEL
GROWTH CONSUMPTION GROWTH RATE
ERROR ERROR ESTIMATE BY ITERATION
RK0VAL VALUE OF RK0;
LOOP(ITER,
$INCLUDE DYNAMIC.GEN
SOLVE DYNAMIC USING MCP;
INVEST(TREP,ITER) = I.L(TREP) * I0;
INTRATE(TFIRST,ITER) = RKGDELTA;
INTRATE(T+1,ITER)$PX.L(T+1) = PX.L(T)/PX.L(T+1)  1;
CONSUM(T,ITER) = C.L(T);
* ERROR(ITER) = C.L("1")  (200I0);
* ERROR(ITER) = I.L("1")*I0  I0;
ERROR(ITER) = C.L("1")  (XX.L("1")+YY.L("1")I0);
RK0VAL(ITER) = RKG;
IF (ORD(ITER)1,
RKG = RKG + ERROR(ITER)/(ERROR(ITER1)ERROR(ITER))
* (RK0VAL(ITER)RK0VAL(ITER1));
ELSE
RKG = RKG + 0.01;
);
);
INVEST(TREP,"I0") = I0;
DISPLAY INVEST, INTRATE, CONSUM;
$SETGLOBAL labels TREP
$LIBINCLUDE PLOT INVEST
CONSUM(T,ITER)$(ORD(T) GT CARD(T)5) = NA;
GROWTH(T,ITER)$(CONSUM(T+1,ITER) NE NA)=
CONSUM(T+1,ITER)/CONSUM(T,ITER)1;
INTRATE(T,ITER)$(ORD(T) GT CARD(T)5) = NA;
$SETGLOBAL labels TDECADE
$LIBINCLUDE PLOT CONSUM
$LIBINCLUDE PLOT GROWTH
$LIBINCLUDE PLOT INTRATE
Footnotes:
^{1}The previous version of the document and its Spanish
translation kindly made by Juan Carlos Segura can be found (as of
April 2004) at: http://web.mit.edu/paltsev/www/current.html
^{2}The discussion in this paper is limited to a
Ramsey model. Another approach to introducing dynamics is overlapping
generations (OLG) models. For OLG formulation in MPSGE, see
Rutherford (1998) and Rasmussen and Rutherford (2001).
^{3}To learn more about GAMS,
visit their homepage at http://www.gams.com
^{4}Or,
equivalently, a population of identical households.
^{5}It may sound strange for noneconomists
that there is a single consumer who owns the firms
and purchases the goods produced by these firms. But for economists
with their "economic
way of thinking" it seems natural. The
significance of this assumption is that distributional considerations
between different types of consumers are ignored.
^{6}An expression
written as x^{T} y = 0 (when x ³ 0 and y ³ 0) means x_{i} y_{i} = 0, for all i = 1,...,n. The variables x_{i} and y_{i} are called
a complementary pair and are said to be complements to each
other.
^{7}If one has negative endowments in the
model and an agent's evaluated income is zero, there will likely be
difficulties involved in finding a solution. At best, a solver will
find an equilibrium in which the omitted income is out of balance by
the value of the transfer required to compensate the agent whose
evaluated income is negative. The resulting equilibrium is not
meaningful.
^{8}An ordinary (uncompensated) demand
function depicts the change in the quantity of a commodity (or a
factor) demanded when price changes. It includes both income and
substitution effects. A compensated demand curve shows the quantity
demanded changes when price changes and the level of utility (or
the level of output for factor demands) is fixed. It represents the
substitution effect only. For a profitmaximizing level of output,
the compensated and uncompensated factor demands are the same
values.
^{9}For more information on
Shephard's lemma see, for example, Varian (1992).
^{10}When I saw an MPSGE code for the
first time, I said: "I need to see an underlying algebra". Later on
I have realized that MPSGE representation is much more attractive and
intuitive than its algebraic formulation. So now the knowledgeable
person always smiles when I am saying: "I don't want those ugly MCP
equations, show me an MPSGE code".
^{11}MPSGE syntax as of April 2004 can be found at
http://debreu.colorado.edu/mainpage/mpsge.htm
^{12}See
Appendix 1 for the examples of conversion of different functions into
the MPSGE format.
^{13}In this treatment of simple static and dynamic
models, many MPSGE features are not going to be discussed. For
additional practice, see the Markusen examples (Markusen and
Rutherford, 1995), available as of April 2004 at:
http://debreu.colorado.edu/markusen/markusen.htm
^{14}For more information on
converting inputoutput data into an MPSGE model, see Rutherford and Paltsev
(1999).
^{15}Make sure
to remember that units here represent the value, i.e. price times
quantity.
^{16}However, a separate
block for utility is helpful for an introduction of a consumption tax
and for welfare analysis.
^{17}See
the code of the program move1_2.gms from Appendix 3. You can
run it to compare the results with move1_1.gms.
^{18}See Markusen examples
for practice (Markusen and Rutherford, 1995).
^{19}There are a number of equations in the model which
can never be slack, simply because demand functions go to infinity as
prices go to zero. We list all conditions here for consistency with our
discussion of MCP formulation in Section 2.
^{20}Setting P(t) = 1,
an equation for RK makes perfect economic sense, because if capital
and other loans are perfect substitutes, then households can also
receive interest rate r on lending to other households. But capital
depreciates at the rate d. Then r = RKd, or RK = d+r , which is the same as equation (36).
^{21}
It should be noted that the transversality condition requires that r
> g, otherwise, there is a possibility of a chainletter borrowing
(for detailed discussion see, for example, Barro and SalaiMartin
(1995)).
^{22}A modeller also should be aware of the fact that
approximation of infinite horizon welfare impacts requires an extra
weight on the terminal consumption level. Otherwise, taxation, for
example, may incorrectly lead to welfareimproving results from a
dynamic model.
^{23}Because all quantities grow at the same rate, it is possible to use
a growth of total output, output in a certain production sector, or
consumption (or simply 1+g in a steadystate case) as a
righthand side variable of equation (39).
^{24}Note that K0*(R+DELTA)=VK, so we could have used
Q:VK as a reference quantity for O:RK(T).
^{25}An alternative formulation is TK.L = SUM(TLAST,
K.L(TLAST)); and PKT.L = SUM(TLAST, PREF(TLAST));
^{26}It could be a good practice for a novice in MPSGE to
reproduce the results presented in Table 3
^{27}Another way of
calibrating to a static data set involves given data on a base year
investment, interest rate, and return to capital. The longrun
steadystate interest rate is determined based on assumed labor
growth rate and calibrated utility discount rate.
^{28}Equation (40) could be derived from an
equation of tangent:
[(yy_{n})/(xx_{n})]=f¢(x_{n}). Put y=0 and y_{n}=f(x_{n}), then
x=x_{n} [(f(x_{n}))/(f¢(x_{n})))]. Using
f¢(x_{n}) = [(f(x_{n})f(x_{n1}))/(x_{n}x_{n1})]
we receive equation (40).
^{29}Note that for f(x) we use an
equation for consumption instead of investment in order to explicitly
separate calculated and given values. Usage of ERROR(ITER) =
I.L("1")*I0  I0; instead of ERROR(ITER) = C.L("1")  (200I0);
produces the same result.
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On 08 Jun 2004, 14:14.