Sieve estimation of time-varying panel data models with latent structures
We propose a heterogeneous time-varying panel data model with a latent group structure that allows the coefficients to vary over both individuals and time. We assume that the coefficients change smoothly over time and form different unobserved groups. When treated as smooth functions of time, the in...
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sg-smu-ink.soe_research-31902021-03-16T06:32:47Z Sieve estimation of time-varying panel data models with latent structures SU, Liangjun WANG, Xia JIN, Sainan We propose a heterogeneous time-varying panel data model with a latent group structure that allows the coefficients to vary over both individuals and time. We assume that the coefficients change smoothly over time and form different unobserved groups. When treated as smooth functions of time, the individual functional coefficients are heterogeneous across groups but homogeneous within a group. We propose a penalized-sieve-estimation-based classifier-Lasso (C-Lasso) procedure to identify the individuals’ membership and to estimate the group-specific functional coefficients in a single step. The classification exhibits the desirable property of uniform consistency. The C-Lasso estimators and their post-Lasso versions achieve the oracle property so that the group-specific functional coefficients can be estimated as well as if the individuals’ membership were known. Several extensions are discussed. Simulations demonstrate excellent finite sample performance of the approach in both classification and estimation. We apply our method to study the heterogeneous trending behavior of GDP per capita across 91 countries for the period 1960-2012 and find four latent groups. 2019-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/2191 info:doi/10.1080/07350015.2017.1340299 https://ink.library.smu.edu.sg/context/soe_research/article/3190/viewcontent/Sieve_estimation_TVPDM_latent_structure20170523.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Classifier-Lasso Functional coefficient Heterogeneity Latent structure Panel data Penalized sieve estimation Polynomial splines Time-varying coefficients Econometrics |
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Classifier-Lasso Functional coefficient Heterogeneity Latent structure Panel data Penalized sieve estimation Polynomial splines Time-varying coefficients Econometrics SU, Liangjun WANG, Xia JIN, Sainan Sieve estimation of time-varying panel data models with latent structures |
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We propose a heterogeneous time-varying panel data model with a latent group structure that allows the coefficients to vary over both individuals and time. We assume that the coefficients change smoothly over time and form different unobserved groups. When treated as smooth functions of time, the individual functional coefficients are heterogeneous across groups but homogeneous within a group. We propose a penalized-sieve-estimation-based classifier-Lasso (C-Lasso) procedure to identify the individuals’ membership and to estimate the group-specific functional coefficients in a single step. The classification exhibits the desirable property of uniform consistency. The C-Lasso estimators and their post-Lasso versions achieve the oracle property so that the group-specific functional coefficients can be estimated as well as if the individuals’ membership were known. Several extensions are discussed. Simulations demonstrate excellent finite sample performance of the approach in both classification and estimation. We apply our method to study the heterogeneous trending behavior of GDP per capita across 91 countries for the period 1960-2012 and find four latent groups. |
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SU, Liangjun WANG, Xia JIN, Sainan |
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SU, Liangjun WANG, Xia JIN, Sainan |
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SU, Liangjun |
title |
Sieve estimation of time-varying panel data models with latent structures |
title_short |
Sieve estimation of time-varying panel data models with latent structures |
title_full |
Sieve estimation of time-varying panel data models with latent structures |
title_fullStr |
Sieve estimation of time-varying panel data models with latent structures |
title_full_unstemmed |
Sieve estimation of time-varying panel data models with latent structures |
title_sort |
sieve estimation of time-varying panel data models with latent structures |
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Institutional Knowledge at Singapore Management University |
publishDate |
2019 |
url |
https://ink.library.smu.edu.sg/soe_research/2191 https://ink.library.smu.edu.sg/context/soe_research/article/3190/viewcontent/Sieve_estimation_TVPDM_latent_structure20170523.pdf |
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