Econometrics aims at estimating observables in the economy and their inter-dependencies and testing the estimates against the economic reality. A quantitative approach to express these inter-dependencies appear as simultaneous equations, an i.e. system of linear equations, this is a mathematical structure of economic relationships that were made possible with the pioneering work of Nobel prize winning economist Trygve Haavelmo [1,2]. This approach and its dynamic variants are now used routinely in dynamic modelling of econometric systems. From a computational perspective, R-project provides efficient and very rich computational environment and the large set of extensions for econometrics in general .
The simplest relationship that can be constructed with two arbitrary economic variables, or instruments, $X(t)$ and $Y(t)$ is shown by Haavelmo . For example, these variables could be unemployment rate and Gross Domestic Product (GDP), as in Okun’s law. Hence, the simplest bi-variate simultaneous system of equations looks as follows,
$c(t)= \alpha y(t) + \beta + u(t)$,
$r(t)= \mu (c(t)+x(t)) +\nu + w(t)$,
c(t) : personal consumption expenditures,
y(t) : personal disposable income,
r(t) : gross business savings,
x(t) : gross investment.
However, this model considered to be static while all the relationships are given at the same time point. Zellner-Palm  provided a dynamic version of the Haavelmo’s model. Here we write down a version of it,
$c(t)= \alpha Dy(t) + \beta + u(t)$,
$r(t)= \mu D(c(t)+x(t)) +\nu + w(t)$,
where difference operator means, $Dy(t) = y(t)-y(t-1)$.
Krokozhia Case Study
Krokozhia is a fictional country depicted in Steven Spielberg’s movie The Terminal. Let’s generate a fictional data for our dynamic Haavelmo model’s economic instruments for this country from 1950 to present in R,
# KH: Krokozhia Haavelmo Model
KH.df <- data.frame(Year=seq(1950,2013),
# Add lag data for difference
KH.df$y.lag <- c(NA, KH.df$y[1:63])
KH.df$c.lag <- c(NA, KH.df$c[1:63])
KH.df$x.lag <- c(NA, KH.df$x[1:63])
# Two-stage least squares
# Eq1: $c(t)= \alpha Dy(t) + \beta + u(t)$,
# \beta is the intercept and u(t) is not used
# Eq2: $r(t)= \mu D(c(t)+x(t)) +\nu + w(t)$,
# \nu is the intercept and w(t) is not used
KH.eq1 <- tsls(c~I(y-y.lag), ~c+y+r, data=KH.df)
KH.eq2 <- tsls(r~I(c-c.lag+x-x.lag), ~c+y+r, data=KH.df)
coef(KH.eq1) # alpha=875.414 nu=0.015
coef(KH.eq2) # mu=-0.028 nu=300.675
Here tsls performs two-stage least square analysis.
Propagating a disturbance in the economy
We have not used any disturbance in determining the system coefficients, constants, above. However, we can propagate the values of economic observables using the dynamic model if we set a disturbance value at a given time. Imagine if we set disturbance on the year 2001 as $u=200$ and $w=150$. Hence, the dynamic model will read on year 2001, $t=2001$, $u=200$ and $w=150$
$c = 875.414 (y-2570) + 0.015 + 200$
$r = -0.028(c+x-1281-479) + 300.675 + 150$,
$y = c+x-r$,
Conclusions and outlook
In this post, we have briefly reviewed possible uses of R in simulating dynamic econometric models, in particular simultaneous equation models. A simple demonstration of determining model coefficients of the Haavelmo type toy model with generated synthetic data is provided. One use case of this type of approach in economic scenario analysis and forecasting is to monitor propagation of the econometric instruments over time is also mentioned.
 The Statistical Implications of a System of Simultaneous Equations,
Trygve Haavelmo, Econometrica, Vol. 11, No. 1. (Jan., 1943), pp. 1-12
 Econometrics Analysis, William H. Greene, Prentice Hall (2011)
 Applied Econometrics with R,
Kleiber, Christian, Zeileis, Achim, Springer (2008), Achim Zeileis
 Methods of measuring the marginal propensity to consume,
T. Haavelmo, Journal of the American Statistical Association,
1947 – Taylor & Francis.
 Time series analysis and simultaneous equation econometric models,
Zellner, Arnold and Palm, Franz, Journal of Econometrics,
Vol.2, Num.1, p17-54 (1974)