Arbeitspapier
Heteroskedasticity-robust tests for structural change
It is remarkably easy to test for structural change, of the type that the classic F or "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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Queen's Economics Department Working Paper ; No. 717
- Klassifikation
-
Wirtschaft
- Thema
-
Chow test
HCCME
heteroskedasticity
artificial regression
Gauss-Newton regression
GNR
structural break
- Ereignis
-
Geistige Schöpfung
- (wer)
-
MacKinnon, James G.
- Ereignis
-
Veröffentlichung
- (wer)
-
Queen's University, Department of Economics
- (wo)
-
Kingston (Ontario)
- (wann)
-
1988
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- MacKinnon, James G.
- Queen's University, Department of Economics
Entstanden
- 1988
Ähnliche Objekte (12)
Heteroskedasticity-Robust Tests in Regression Directions
Thirty years of heteroskedasticity-robust inference
Structural Vector Autoregressions with Heteroskedasticity
Robust inference in models identified via heteroskedasticity
On combining evidence from heteroskedasticity robust panel unit root tests in pooled regressions
Heteroskedasticity-robust unit root testing for trending panels
Simultaneously Modelling Conditional Heteroskedasticity and Scale Change
Simultaneously Modelling Conditional Heteroskedasticity and Scale Change
Testing identification via heteroskedasticity in structural vector autoregressive models
Testing identification via heteroskedasticity in structural vector autoregressive models
Testing identification via heteroskedasticity in structural vector autoregressive models
Testing identification via heteroskedasticity in structural vector autoregressive models
Heteroskedasticity-Robust Tests in Regression Directions
Thirty years of heteroskedasticity-robust inference
Structural Vector Autoregressions with Heteroskedasticity
Robust inference in models identified via heteroskedasticity
On combining evidence from heteroskedasticity robust panel unit root tests in pooled regressions
Heteroskedasticity-robust unit root testing for trending panels
Simultaneously Modelling Conditional Heteroskedasticity and Scale Change
Simultaneously Modelling Conditional Heteroskedasticity and Scale Change
Testing identification via heteroskedasticity in structural vector autoregressive models
Testing identification via heteroskedasticity in structural vector autoregressive models
Testing identification via heteroskedasticity in structural vector autoregressive models
Testing identification via heteroskedasticity in structural vector autoregressive models
Heteroskedasticity-Robust Tests in Regression Directions
Thirty years of heteroskedasticity-robust inference
Structural Vector Autoregressions with Heteroskedasticity
Robust inference in models identified via heteroskedasticity
On combining evidence from heteroskedasticity robust panel unit root tests in pooled regressions
Heteroskedasticity-robust unit root testing for trending panels
Simultaneously Modelling Conditional Heteroskedasticity and Scale Change
Simultaneously Modelling Conditional Heteroskedasticity and Scale Change
Testing identification via heteroskedasticity in structural vector autoregressive models
Testing identification via heteroskedasticity in structural vector autoregressive models
Testing identification via heteroskedasticity in structural vector autoregressive models