Determination of different types of fixed effects in three-dimensional panels

In this paper we propose a jackknife method to determine the type of fixed effects in three-dimensional panel data models. We show that with probability approaching 1, the method can select the correct type of fixed effects in the presence of only weak serial or cross-sectional dependence among the...

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Main Authors: LU, Xun, MIAO, Ke, SU, Liangjun
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/soe_research/2186
https://ink.library.smu.edu.sg/context/soe_research/article/3185/viewcontent/3D_determination_of_fixed_effects20180423B_.pdf
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spelling sg-smu-ink.soe_research-31852020-04-02T06:59:14Z Determination of different types of fixed effects in three-dimensional panels LU, Xun MIAO, Ke SU, Liangjun In this paper we propose a jackknife method to determine the type of fixed effects in three-dimensional panel data models. We show that with probability approaching 1, the method can select the correct type of fixed effects in the presence of only weak serial or cross-sectional dependence among the error terms. In the presence of strong serial correlation, we propose a modified jackknife method and justify its selection consistency. Monte Carlo simulations demonstrate the excellent finite sample performance of our method. Applications to two datasets in macroeconomics and international trade reveal the usefulness of our method. 2018-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/2186 https://ink.library.smu.edu.sg/context/soe_research/article/3185/viewcontent/3D_determination_of_fixed_effects20180423B_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Consistency Cross-validation Fixed effect Individual effect Jackknife Three-dimensional panel. Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Consistency
Cross-validation
Fixed effect
Individual effect
Jackknife
Three-dimensional panel.
Econometrics
spellingShingle Consistency
Cross-validation
Fixed effect
Individual effect
Jackknife
Three-dimensional panel.
Econometrics
LU, Xun
MIAO, Ke
SU, Liangjun
Determination of different types of fixed effects in three-dimensional panels
description In this paper we propose a jackknife method to determine the type of fixed effects in three-dimensional panel data models. We show that with probability approaching 1, the method can select the correct type of fixed effects in the presence of only weak serial or cross-sectional dependence among the error terms. In the presence of strong serial correlation, we propose a modified jackknife method and justify its selection consistency. Monte Carlo simulations demonstrate the excellent finite sample performance of our method. Applications to two datasets in macroeconomics and international trade reveal the usefulness of our method.
format text
author LU, Xun
MIAO, Ke
SU, Liangjun
author_facet LU, Xun
MIAO, Ke
SU, Liangjun
author_sort LU, Xun
title Determination of different types of fixed effects in three-dimensional panels
title_short Determination of different types of fixed effects in three-dimensional panels
title_full Determination of different types of fixed effects in three-dimensional panels
title_fullStr Determination of different types of fixed effects in three-dimensional panels
title_full_unstemmed Determination of different types of fixed effects in three-dimensional panels
title_sort determination of different types of fixed effects in three-dimensional panels
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/soe_research/2186
https://ink.library.smu.edu.sg/context/soe_research/article/3185/viewcontent/3D_determination_of_fixed_effects20180423B_.pdf
_version_ 1770574210281766912