In this paper we propose a jackknife method to determine individual and time e⁄ects in linear panel data models. We rst show that when both the serial and cross-sectional correlation among the idiosyncratic error terms are weak, our jackknife method can pick up the correct model with probability approaching one (w.p.a.1). In the presence of moderate or strong degree of serial correlation, we modify our jackknife criterion function and show that the modied jackknife method can also select the correct model w.p.a.1. We conduct Monte Carlo simulations to show that our new methods perform remarkably well in nite samples. We apply our methods to study (i) the crime rates in North Carolina, (ii) the determinants of saving rates across countries, and (iii) the relationship between guns and crime rates in the U.S.
Consistency, Cross-validation, Dynamic panel, Information Criterion, Jackknife, Individual e⁄ect, Time e⁄ect.
LU, Xun and SU, Liangjun.
Determining individual or time effects in panel data models. (2017). 1-58. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/2070
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