TESTING STRUCTURAL CHANGE IN PARTIALLY LINEAR MODELS

Testing for Structural Change in Conditional Models

In the past decade, we have seen the development of a new set of tests for structural change of unknown timing in regression models, most notably the SupF statistic of Andrews (1993, Econometrica 61, 825}856), the ExpF and AveF statistics of AndrewsPloberger (1994, Econometrica 62, 1383}1414), and the ̧ statistic of Nyblom (1989, Journal of American Statistical Association 84, 223}230). The dis...

strucchange: Testing for Structural Change in Linear Regression Relationships

i.e., that the regression coefficients remain constant, against the alternative that the coefficient vector varies over time. These tests can be divided in two classes, which are differently suitable for certain patterns of deviation from the null hypothesis. The first class are the tests from the generalized fluctuation test framework (Kuan and Hornik, 1995) that can detect various types of st...

Estimation and Testing for Partially Linear Single-index Models.

In partially linear single-index models, we obtain the semiparametrically efficient profile least-squares estimators of regression coefficients. We also employ the smoothly clipped absolute deviation penalty (SCAD) approach to simultaneously select variables and estimate regression coefficients. We show that the resulting SCAD estimators are consistent and possess the oracle property. Subsequen...

strucchange: An R Package for Testing for Structural Change in Linear Regression Models

This paper introduces ideas and methods for testing for structural change in linear regression models and presents how these have been realized in an R package called strucchange. It features tests from the generalized fluctuation test framework as well as from the F test (Chow test) framework. Extending standard significance tests it contains methods to fit, plot and test empirical fluctuation...

Partially Linear Models