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- W2573733156 abstract "Testing for structural change (t-heterogeneity of parameters) is of great interest to researchers both in econometrics and statistics. In this paper we develop an approach to testing the t-invariance of the model parameters, by using the information in the marginal and joint distributions of the stochastic processes involved. The proposed procedure differs from the testing procedures suggested in the literature in two important ways: First, it is designed to detect t-heterogeneity that is a smooth function of time (t) rather than a discrete shift. Second it is based on rolling window estimates of the moments of the variables, rather than the residuals or the coefficients from the regression. Focusing on marginal and joint moments provides a more general perspective because it encompasses changes in the regression coefficients and/or the residual variance. The Maximum Entropy (ME) density Bootstrap of Vinod (2004) is an essential component of our procedure because it provides a reliable resampling algorithm for short nonstationary time series. We carry out a number of Monte Carlo simulations to evaluate the performance of the proposed testing procedure when the t-heterogeneity comes in the form of smooth functions of t, such as the Linear, Quadratic, Exponential and the Logistic. Results show that the testing procedure is effective even for small samples. Furthermore, it clearly distinguishes whether the model t-heterogeneity arises from changes in the mean or variance of the process. While this procedure is designed to capture smooth trends, the experimental results indicate that it can also be successfully applied to testing discrete single breaks." @default.
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- W2573733156 date "2005-01-01" @default.
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- W2573733156 title "Testing for Structural Change: A Misspecification Testing Perspective" @default.
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