Introduction to econometrics II eco 356 faculty of social sciences course guide course Developers: Dr. Adesina-Uthman

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Introduction to Econometrics ECO 356 Course Guide and Course Material

INTRODUCTION TO ECONOMETRICS II ECO 306 NOUN

INTRODUCTION TO ECONOMETRICS II

ECO 306

NOUN
131 externally. Thus in the present case,p and ware both endogenous, and U is exogenous. The exogenous variables and the disturbance terms ultimately determine the values of the endogenous variables, once the complexity is cleared. The mathematical relationships expressing the endogenous variables regarding the exogenous variables and disturbance terms are known as the reduced form equations. The original equations that we wrote down when specifying the model are described as the structural equations. We will derive the reduced form equations for p
and w. To obtain that for p, we take the structural equation for p and substitute for w
from the second equation
(
)
…[5.03] Hence,
(
)
…[5.04] and so we have the reduced form equation for p;
(
)
…[5.05] Similarly we obtain the reduced form equation for w:
(
)
…[5.06] Hence
(
)
…[5.07] and sob Simultaneous Equations Bias
In almost all simultaneous equations models, the reduced form equations express the endogenous variables regarding all of the exogenous variables and all of the

INTRODUCTION TO ECONOMETRICS II

ECO 306

NOUN
132 disturbance terms. You can see that this is the case with the price inflation/wage inflation model. In this model, there is only one exogenous variable, U.
wdepends on it directly p does not depend on it directly but does so indirectly because
w determines it. Similarly, both p and wdepend on
, p directly and w indirectly. And both depend on
, w directly and p indirectly. The dependence of w on means that OLS would yield inconsistent estimates if used to fit equation [5.01], the structural equation for p. w is a stochastic regressor and its random component is not distributed independently of the disturbance term Similarly the dependence of p on

means that OLS would yield inconsistent estimates if used to fit [5.02]. Since [5.01] is a simple regression equation, it is easy to analyze the large-sample bias in the OLS estimator of

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