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Full-Text Articles in Social and Behavioral Sciences
Determining The Number Of Groups In Latent Panel Structures With An Application To Income And Democracy, Xun Lu, Liangjun Su
Determining The Number Of Groups In Latent Panel Structures With An Application To Income And Democracy, Xun Lu, Liangjun Su
Research Collection School Of Economics
We consider a latent group panel structure as recently studied by Su, Shi, and Phillips (2014), where the number of groups is unknown and has to be determined empirically. We propose a testing procedure to determine the number of roups. Our test is a residualbased LM-type test. We show that after being appropriately standardized, our test is asymptotically normally distributed under the null hypothesis of a given number of groups and has power to detect deviations from the null. Monte Carlo simulations show that our test performs remarkably well in finite samples. We apply our method to study the effect …
Lag Length Selection In Panel Autoregression, Chirok Han, Peter C. B. Phillips, Donggyu Sul
Lag Length Selection In Panel Autoregression, Chirok Han, Peter C. B. Phillips, Donggyu Sul
Research Collection School Of Economics
Model selection by BIC is well known to be inconsistent in the presence of incidental parameters. This article shows that, somewhat surprisingly, even without fixed effects in dynamic panels BIC is inconsistent and overestimates the true lag length with considerable probability. The reason for the inconsistency is explained, and the probability of overestimation is found to be 50% asymptotically. Three alternative consistent lag selection methods are considered. Two of these modify BIC, and the third involves sequential testing. Simulations evaluate the performance of these alternative lag selection methods in finite samples.
Determining Individual Or Time Effects In Panel Data Models, Xun Lu, Liangjun Su
Determining Individual Or Time Effects In Panel Data Models, Xun Lu, Liangjun Su
Research Collection School Of Economics
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 …