186—Chapter 23. Specification and Diagnostic Tests

Applications

For illustrative purposes, we provide a demonstration of how to carry out some other specification tests in EViews. For brevity, the discussion is based on commands, but most of these procedures can also be carried out using the menu system.

A Wald Test of Structural Change with Unequal Variance

The F-statistics reported in the Chow tests have an F-distribution only if the errors are independent and identically normally distributed. This restriction implies that the residual variance in the two subsamples must be equal.

Suppose now that we wish to compute a Wald statistic for structural change with unequal subsample variances. Denote the parameter estimates and their covariance matrix in sub-

sample i as bi and Vi for i = 1, 2 . Under the assumption that b1 and b2 are independent normal random variables, the difference b1 – b2 has mean zero and variance

V1 + V2 . Therefore, a Wald statistic for the null hypothesis of no structural change and independent samples can be constructed as:

which has an asymptotic x2 distribution with degrees of freedom equal to the number of estimated parameters in the b vector.

188—Chapter 23. Specification and Diagnostic Tests

posed by Davidson and MacKinnon (1989, 1993), which carries out the test by running an auxiliary regression.

The following equation in the “Basics.WF1” workfile was estimated by OLS:

Dependent Variable: LOG(M1)

Method: Least Squares

Date: 08/10/09 Time: 16:08

Sample (adjusted): 1959M02 1995M04

Included observations: 435 after adjustments

Suppose we are concerned that industrial production (IP) is endogenously determined with money (M1) through the money supply function. If endogeneity is present, then OLS estimates will be biased and inconsistent. To test this hypothesis, we need to find a set of instrumental variables that are correlated with the “suspect” variable IP but not with the error term of the money demand equation. The choice of the appropriate instrument is a crucial step. Here, we take the unemployment rate (URATE) and Moody’s AAA corporate bond yield (AAA) as instruments.

To carry out the Hausman test by artificial regression, we run two OLS regressions. In the first regression, we regress the suspect variable (log) IP on all exogenous variables and instruments and retrieve the residuals:

equation eq_test.ls log(ip) c dlog(ppi) tb3 log(m1(-1)) urate aaa eq_test.makeresid res_ip

Then in the second regression, we re-estimate the money demand function including the residuals from the first regression as additional regressors. The result is:

Applications—189

Dependent Variable: LOG(M1)

Method: Least Squares

Date: 08/10/09 Time: 16:11

Sample (adjusted): 1959M02 1995M04

Included observations: 435 after adjustments

If the OLS estimates are consistent, then the coefficient on the first stage residuals should not be significantly different from zero. In this example, the test rejects the hypothesis of consistent OLS estimates at conventional levels.

Note that an alternative form of a regressor endogeneity test may be computed using the Regressor Endogeneity Test view of an equation estimated by TSLS or GMM (see “Regressor Endogeneity Test” on page 79).

Non-nested Tests

Most of the tests discussed in this chapter are nested tests in which the null hypothesis is obtained as a special case of the alternative hypothesis. Now consider the problem of choosing between the following two specifications of a consumption function:

H1: CSt = a1 + a2GDPt + a3GDPt – 1 + et

(23.48)

H2: CSt = b1 + b2GDPt + b3CSt – 1 + et

for the variables in the workfile “Coef_test2.WF1”. These are examples of non-nested models since neither model may be expressed as a restricted version of the other.

The J-test proposed by Davidson and MacKinnon (1993) provides one method of choosing between two non-nested models. The idea is that if one model is the correct model, then the fitted values from the other model should not have explanatory power when estimating that model. For example, to test model H1 against model H2 , we first estimate model H2 and retrieve the fitted values:

equation eq_cs2.ls cs c gdp cs(-1)

190—Chapter 23. Specification and Diagnostic Tests

eq_cs2.fit(f=na) cs2

The second line saves the fitted values as a series named CS2. Then estimate model H1 including the fitted values from model H2 . The result is:

Dependent Variable: CS

Method: Least Squares

Date: 08/10/09 Time: 16:17

Sample (adjusted): 1947Q2 1994Q4

Included observations: 191 after adjustments

The fitted values from model H2 enter significantly in model H1 and we reject model H1 .

We may also test model H2 against model H1 . First, estimate model H1 and retrieve the fitted values:

equation eq_cs1a.ls cs gdp gdp(-1) eq_cs1a.fit(f=na) cs1

Then estimate model H2 including the fitted values from model H1 . The results of this “reverse” test regression are given by: