It is remarkably easy to test for structural change, of the type that the classic For “Chow” test is designed to detect, in a manner that is robust to heteroskedasticity of possibly unknown form. This paper first discusses how to test for structural change in nonlinear regression models by using a variant of the Gauss-Newton regression. It then shows how to make these tests robust to heteroskedasticity of unknown form, and discusses several related procedures for doing so. Finally, it presents the results of a number of Monte Carlo experiments designed to see how well the new tests perform in finite samples.
Linear Regression Model Versus Test Monte Carlo Experiment Nonlinear Regression Model Chow Test
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.
Chesher A, Jewitt I (1987) The bias of a heteroskedasticity consistent covariance matrix estimator. Econometrica 55:1217–1222CrossRefGoogle Scholar
Chow GC (1960) Tests of equality between sets of coefficients in two linear regressions. Econometrica 28:591–605CrossRefGoogle Scholar
Davidson R, MacKinnon JG (1984) Model specification tests based on artificial linear regressions. International Economic Review 25:485–502CrossRefGoogle Scholar
Davidson R, MacKinnon JG (1985) Heteroskedasticity-robust tests in regression directions. Annales de l’INSÉE 59/60:183–218Google Scholar
Eicker F (1963) Asymptotic normality and consistency of the least squares estimators for families of linear regressions. Annals of Mathematical Statistics 34:447–456CrossRefGoogle Scholar
Engle RF (1982a) Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50:987–1007CrossRefGoogle Scholar
Engle RF (1982b) A general approach to Lagrange multiplier model diagnostics. Journal of Econometrics 20:83–104CrossRefGoogle Scholar
Fisher FM (1970) Tests of equality between sets of coefficients in two linear regressions: an expository note. Econometrica 38:361–366CrossRefGoogle Scholar
Honda Y (1982) On tests of equality between sets of coefficients in two linear regressions when disturbance variances are unequal. The Manchester School 49:116–125CrossRefGoogle Scholar
Jayatissa WA (1977) Tests of equality between sets of coefficients in two linear regressions when disturbance variances are unequal. Econometrica 45:1291–1292CrossRefGoogle Scholar
MacKinnon JG, White H (1985) Some heteroskedasticity consistent covariance matrix estimators with improved finite sample properties. Journal of Econometrics 29:305–325CrossRefGoogle Scholar
Ohtani K, Toyoda T (1985) Small sample properties of tests of equality between sets of coefficients in two linear regressions under heteroskedasticity. International Economic Review 26:37–44CrossRefGoogle Scholar
Phillips GDA, McCabe BP (1983) The independence of tests for structural change in regression models. Economics Letters 12:283–287CrossRefGoogle Scholar
Schmidt P, Sickles R (1977) Some further evidence on the use of the Chow test under heteroskedasticity. Econometrica 45:1293–1298CrossRefGoogle Scholar
Toyoda T, Ohtani K (1986) Testing equality between sets of coefficients after a preliminary test for equality of disturbance variances in two linear regressions. Journal of Econometrics 31:67–80CrossRefGoogle Scholar
Watt PA (1979) Tests of equality between sets of coefficients in two linear regressions when disturbance variances are unequal: some small sample properties. The Manchester School 47:391–396Google Scholar
Weerahandi S (1987) Testing regression equality with unequal variances. Econometrica 55:1211–1215CrossRefGoogle Scholar
White H (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48:817–838CrossRefGoogle Scholar