Testing Structural Change in Time-Series Nonparametric Regression Models
Checking parameter stability of econometric models is a long-standing problem. Almost all existing structural change tests in econometrics are designed to detect abrupt breaks. Little attention has been paid to smooth structural changes, which may be more realistic in economics. We propose a consistent test for smooth structural changes as well as abrupt structural breaks with known or unknown change points. The idea is to estimate smooth time-varying parameters by local smoothing and compare the fitted values of the restricted constant parameter model and the unrestricted time-varying parameter model. The test is asymptotically pivotal and does not require prior information about the alternative. A simulation study highlights the merits of the proposed test relative to a variety of popular tests for structural changes. In an application, we strongly reject the stability of univariate and multivariate stock return prediction models in the postwar and post-oil-shocks periods.
Kernel, model stability, nonparametric regression, parameter constancy, smooth structural change
Statistics and Its Interface
SU, Liangjun and XIAO, Z..
Testing Structural Change in Time-Series Nonparametric Regression Models. (2009). Statistics and Its Interface. 1, (2), 347-366. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/542
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