Lecture2

# Lecture2 - File Lecture2.DOC MODEL MIS-SPECIFICATION AND...

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1 File: Lecture2.DOC MODEL MIS-SPECIFICATION AND MIS-SPECIFICATION TESTING We have four main objectives. (1) To examine what is meant by the misspecification of an econometric model. (2) To identify the consequences of estimating a misspecified econometric model. (3) To present a testing framework that can be used to detect the presence of model misspecification. (4) To discuss appropriate responses a researcher could make when confronted by evidence of model misspecification.

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2 (1) To examine what is meant by the misspecification of an econometric model. We shall denote a regression model as statistically well specified for a given estimator if each one of the set of assumptions which makes that estimator optimal is satisfied. The regression model will be called statistically misspecified for that particular estimator (or just misspecified) if one or more of the assumptions is not satisfied.
3 TABLE 4.1 THE ASSUMPTIONS OF THE LINEAR REGRESSION MODEL WITH STOCHASTIC REGRESSORS The k variable regression model is t 1 2 2t k kt t Y = + X + . .. + X + u t = 1,. ..,T β β β (1) The assumptions of the CLRM are: (1) The dependent variable is a linear function of the selected set of possibly stochastic, covariance stationary regressor variables and a random disturbance term as specified in Equation (1). In other words, the model specification is correct. (2) The set of regressors is not perfectly collinear. This means that no regressor variable can be obtained as an exact linear combination of any subset of the other regressor variables. (3) The error process has zero mean. (4) The errors terms, u t , t=1,. .,T, are serially uncorrelated. (5) The errors have a constant variance. (6)

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Lecture2 - File Lecture2.DOC MODEL MIS-SPECIFICATION AND...

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