文章基本信息

标题：Efficient Inference on Fractionally Integrated Panel Data Models with Fixed Effects
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作者：Peter M Robinson ; Carlos Velasco
期刊名称：Japanese Studies Programme Papers
出版年度：2013
出版社：Suntory Toyota International Centres for Economics and Related Disciplines
摘要：A dynamic panel data model is considered that contains possibly stochastic individual com- ponents and a common fractional stochastic time trend. We propose four di¤erent ways of coping with the individual e¤ects so as to estimate the fractional parameter. Like models with autoregressive dynamics, ours nests a unit root, but unlike the nonstandard asymptotics in the autoregressive case, estimates of the fractional parameter can be asymptotically normal. Establishing this property is made di¢ cult due to bias caused by the individual e¤ects, or by the consequences of eliminating them, and requires the number of time series observations T to increase, while the cross-sectional size, N; can either remain xed or increase with T: The biases in the central limit theorem are asymptotically negligible only under stringent conditions on the growth of N relative to T; but these can be relaxed by bias correction. For three of the estimates the biases depend only on the fractional parameter. In hypothesis testing, bias correc- tion of the estimates is readily carried out. We evaluate the biases numerically for a range of T and parameter values, develop and justify feasible bias-corrected estimates, and briey discuss simplied but less e¤ective corrections. A Monte Carlo study of nite-sample performance is included.
关键词：Panel data; Fractional time series; Estimation; Testing; Bias correction