Nonparametric Dynamic Panel Data Models with Interactive Fixed Effects: Sieve Estimation and Specification Testing

In this paper we analyze nonparametric dynamic panel data models with interactive fixed effects, where the predetermined regressors enter the models nonparametrically and the common factors enter the models linearly but with individual specific factor loadings. We consider the issues of estimation a...

Full description

Saved in:
Bibliographic Details
Main Authors: SU, Liangjun, ZHANG, Yonghui
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2013
Subjects:
Online Access:https://ink.library.smu.edu.sg/soe_research/1560
https://ink.library.smu.edu.sg/context/soe_research/article/2559/viewcontent/NonparametricDynamic_PanelDataModels_InteractiveFixedEffects_2013_wp.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.soe_research-2559
record_format dspace
spelling sg-smu-ink.soe_research-25592018-08-31T07:06:23Z Nonparametric Dynamic Panel Data Models with Interactive Fixed Effects: Sieve Estimation and Specification Testing SU, Liangjun ZHANG, Yonghui In this paper we analyze nonparametric dynamic panel data models with interactive fixed effects, where the predetermined regressors enter the models nonparametrically and the common factors enter the models linearly but with individual specific factor loadings. We consider the issues of estimation and specification testing when both the cross-sectional dimension and the time dimension are large. We propose sieve estimation for the nonparametric function by extending Bai’s (2009) principal component analysis (PCA) to our nonparametric framework. Based on the asymptotic expansion of the Gaussian quasi-log-likelihood function, we derive the convergence rate for the sieve estimator and establish its asymptotic normality. The sources of asymptotic biases are discussed and a bias-corrected estimator is provided. We also propose a consistent specification test for the linearity of the functional form by comparing the linear and sieve estimators. We establish the asymptotic distributions of the test statistic under both the null hypothesis and a sequence of Pitman local alternatives. A bootstrap procedure is proposed to obtain the bootstrap p-values and its asymptotic validity is justified. Monte Carlo simulations are conducted to investigate the finite sample performance of our estimator and test. We apply our method to an economic growth data set to study the relationship between capital accumulation and real GDP growth rate. 2013-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1560 https://ink.library.smu.edu.sg/context/soe_research/article/2559/viewcontent/NonparametricDynamic_PanelDataModels_InteractiveFixedEffects_2013_wp.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Common factors Cross section dependence Interactive fixed effects Linearity Nonparametric dynamic panel Sieve method Specification test Econometrics Economics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Common factors
Cross section dependence
Interactive fixed effects
Linearity
Nonparametric dynamic panel
Sieve method
Specification test
Econometrics
Economics
spellingShingle Common factors
Cross section dependence
Interactive fixed effects
Linearity
Nonparametric dynamic panel
Sieve method
Specification test
Econometrics
Economics
SU, Liangjun
ZHANG, Yonghui
Nonparametric Dynamic Panel Data Models with Interactive Fixed Effects: Sieve Estimation and Specification Testing
description In this paper we analyze nonparametric dynamic panel data models with interactive fixed effects, where the predetermined regressors enter the models nonparametrically and the common factors enter the models linearly but with individual specific factor loadings. We consider the issues of estimation and specification testing when both the cross-sectional dimension and the time dimension are large. We propose sieve estimation for the nonparametric function by extending Bai’s (2009) principal component analysis (PCA) to our nonparametric framework. Based on the asymptotic expansion of the Gaussian quasi-log-likelihood function, we derive the convergence rate for the sieve estimator and establish its asymptotic normality. The sources of asymptotic biases are discussed and a bias-corrected estimator is provided. We also propose a consistent specification test for the linearity of the functional form by comparing the linear and sieve estimators. We establish the asymptotic distributions of the test statistic under both the null hypothesis and a sequence of Pitman local alternatives. A bootstrap procedure is proposed to obtain the bootstrap p-values and its asymptotic validity is justified. Monte Carlo simulations are conducted to investigate the finite sample performance of our estimator and test. We apply our method to an economic growth data set to study the relationship between capital accumulation and real GDP growth rate.
format text
author SU, Liangjun
ZHANG, Yonghui
author_facet SU, Liangjun
ZHANG, Yonghui
author_sort SU, Liangjun
title Nonparametric Dynamic Panel Data Models with Interactive Fixed Effects: Sieve Estimation and Specification Testing
title_short Nonparametric Dynamic Panel Data Models with Interactive Fixed Effects: Sieve Estimation and Specification Testing
title_full Nonparametric Dynamic Panel Data Models with Interactive Fixed Effects: Sieve Estimation and Specification Testing
title_fullStr Nonparametric Dynamic Panel Data Models with Interactive Fixed Effects: Sieve Estimation and Specification Testing
title_full_unstemmed Nonparametric Dynamic Panel Data Models with Interactive Fixed Effects: Sieve Estimation and Specification Testing
title_sort nonparametric dynamic panel data models with interactive fixed effects: sieve estimation and specification testing
publisher Institutional Knowledge at Singapore Management University
publishDate 2013
url https://ink.library.smu.edu.sg/soe_research/1560
https://ink.library.smu.edu.sg/context/soe_research/article/2559/viewcontent/NonparametricDynamic_PanelDataModels_InteractiveFixedEffects_2013_wp.pdf
_version_ 1770571859285245952