In this paper, we consider the estimation of common breaks for linear panel data models by means of screening and ranking algorithm. For static and dynamic paneldatamodels, we estimate the regression coefficients us...
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In this paper, we consider the estimation of common breaks for linear panel data models by means of screening and ranking algorithm. For static and dynamic paneldatamodels, we estimate the regression coefficients using covariance estimation and generalized method of moments, respectively, and apply a screening and ranking algorithm on this basis. The possible break points are first screened by constructing local statistics based on the coefficient estimators, then further screened by the thresholding rule, and finally the final break points are screened by the information criterion. Monte Carlo simulations demonstrate that the proposed methods work well in finite samples. We apply the screening and ranking algorithm to study the influence of rural residents' consumption demand on China's economic growth using a panel of 31 provinces from 2005 to 2023 and find a break point in the model.
In econometric analysis of paneldata, one always doesn't have enough information to assure the existence/absence of time effects, which can lead to wrong conclusions in statistical inference such as moment estima...
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In econometric analysis of paneldata, one always doesn't have enough information to assure the existence/absence of time effects, which can lead to wrong conclusions in statistical inference such as moment estimation and hypothesis testing. In this paper, estimation of second and fourth order moments of the individual effects and the errors are studied for linear panel data models without information on the existence/absence of time effects. With differences of the residuals over the individual index, the orthogonality-based moment estimators of the random individual effects and the errors are respectively obtained without affecting each other. These moment estimators are robust on the potential existence of time effects. Their asymptotic normalities are obtained under some moment conditions. Monte Carlo simulations are carried out for illustration. (C) 2010 Elsevier B.V. All rights reserved.
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