Nonparametric Tests for Poolability in Panel Data Models with Cross Section Dependence
In this paper we propose a nonparametric test for poolability in large dimensional semiparametric panel data models with cross-section dependence based on the sieve estimation technique. To construct the test statistic, we only need to estimate the model under the alternative. We establish the asymp...
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sg-smu-ink.soe_research-22572011-05-05T09:54:05Z Nonparametric Tests for Poolability in Panel Data Models with Cross Section Dependence JIN, Sainan SU, Liangjun In this paper we propose a nonparametric test for poolability in large dimensional semiparametric panel data models with cross-section dependence based on the sieve estimation technique. To construct the test statistic, we only need to estimate the model under the alternative. We establish the asymptotic normal distributions of our test statistic under the null hypothesis of poolability and a sequence of local alternatives, and prove the consistency of our test. We also suggest a bootstrap method as an alternative way to obtain the critical values and justify its validity. A small set of Monte Carlo simulations indicate the test performs reasonably well in finite samples. 2010-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1258 https://ink.library.smu.edu.sg/context/soe_research/article/2257/viewcontent/poolability20100810.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Common factor Cross-section dependence Poolability Semiparametric panel data model Sieve estimation Test Econometrics |
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Common factor Cross-section dependence Poolability Semiparametric panel data model Sieve estimation Test Econometrics JIN, Sainan SU, Liangjun Nonparametric Tests for Poolability in Panel Data Models with Cross Section Dependence |
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In this paper we propose a nonparametric test for poolability in large dimensional semiparametric panel data models with cross-section dependence based on the sieve estimation technique. To construct the test statistic, we only need to estimate the model under the alternative. We establish the asymptotic normal distributions of our test statistic under the null hypothesis of poolability and a sequence of local alternatives, and prove the consistency of our test. We also suggest a bootstrap method as an alternative way to obtain the critical values and justify its validity. A small set of Monte Carlo simulations indicate the test performs reasonably well in finite samples. |
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JIN, Sainan SU, Liangjun |
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JIN, Sainan SU, Liangjun |
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JIN, Sainan |
title |
Nonparametric Tests for Poolability in Panel Data Models with Cross Section Dependence |
title_short |
Nonparametric Tests for Poolability in Panel Data Models with Cross Section Dependence |
title_full |
Nonparametric Tests for Poolability in Panel Data Models with Cross Section Dependence |
title_fullStr |
Nonparametric Tests for Poolability in Panel Data Models with Cross Section Dependence |
title_full_unstemmed |
Nonparametric Tests for Poolability in Panel Data Models with Cross Section Dependence |
title_sort |
nonparametric tests for poolability in panel data models with cross section dependence |
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Institutional Knowledge at Singapore Management University |
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2010 |
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https://ink.library.smu.edu.sg/soe_research/1258 https://ink.library.smu.edu.sg/context/soe_research/article/2257/viewcontent/poolability20100810.pdf |
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