Determining individual or time effects in panel data models
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 app...
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sg-smu-ink.soe_research-30692017-08-31T02:49:49Z Determining individual or time effects in panel data models LU, Xun SU, Liangjun 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. 2017-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/2070 https://ink.library.smu.edu.sg/context/soe_research/article/3069/viewcontent/Determination_Fixed_Effects20170106.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 Dynamic panel Information Criterion Jackknife Individual e⁄ect Time e⁄ect. Econometrics |
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Consistency Cross-validation Dynamic panel Information Criterion Jackknife Individual e⁄ect Time e⁄ect. Econometrics LU, Xun SU, Liangjun Determining individual or time effects in panel data models |
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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. |
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text |
author |
LU, Xun SU, Liangjun |
author_facet |
LU, Xun SU, Liangjun |
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LU, Xun |
title |
Determining individual or time effects in panel data models |
title_short |
Determining individual or time effects in panel data models |
title_full |
Determining individual or time effects in panel data models |
title_fullStr |
Determining individual or time effects in panel data models |
title_full_unstemmed |
Determining individual or time effects in panel data models |
title_sort |
determining individual or time effects in panel data models |
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Institutional Knowledge at Singapore Management University |
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2017 |
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https://ink.library.smu.edu.sg/soe_research/2070 https://ink.library.smu.edu.sg/context/soe_research/article/3069/viewcontent/Determination_Fixed_Effects20170106.pdf |
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