Identification and estimation of nonseparable single-index models in panel data with correlated random effects

Pavel Cizek, Jinghua Lei

Research output: Contribution to journalArticleScientificpeer-review

Abstract

The identification in a nonseparable single-index models with correlated random effects is considered in panel data with a fixed number of time periods. The identification assumption is based on the correlated random effects structure. Under this assumption, the parameters of interest are identified up to a multiplicative constant and could be estimated by an average difference of derivatives estimator based on the local polynomial smoothing. We suggest to linearly combine the estimators obtained for different orders of differences and derive the variance-minimizing weighting scheme. The asymptotic distribution of the proposed estimators is derived both for stationary and non-stationary explanatory variables along with a test of the stationarity. Finally, Monte Carlo experiments reveal finite sample properties of the proposed estimator.
Original languageEnglish
Pages (from-to)113-128
JournalJournal of Econometrics
Volume203
Issue number1
DOIs
Publication statusPublished - Mar 2018

Fingerprint

Random effects
Estimator
Panel data
Index model
Derivatives
Asymptotic distribution
Finite sample properties
Monte Carlo experiment
Stationarity
Weighting
Smoothing
Local polynomial

Keywords

  • average derivative estimation
  • correlated random effects
  • local polynomia smoothing
  • nonlinear panel data
  • nonseparable models

Cite this

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title = "Identification and estimation of nonseparable single-index models in panel data with correlated random effects",
abstract = "The identification in a nonseparable single-index models with correlated random effects is considered in panel data with a fixed number of time periods. The identification assumption is based on the correlated random effects structure. Under this assumption, the parameters of interest are identified up to a multiplicative constant and could be estimated by an average difference of derivatives estimator based on the local polynomial smoothing. We suggest to linearly combine the estimators obtained for different orders of differences and derive the variance-minimizing weighting scheme. The asymptotic distribution of the proposed estimators is derived both for stationary and non-stationary explanatory variables along with a test of the stationarity. Finally, Monte Carlo experiments reveal finite sample properties of the proposed estimator.",
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Identification and estimation of nonseparable single-index models in panel data with correlated random effects. / Cizek, Pavel; Lei, Jinghua.

In: Journal of Econometrics, Vol. 203, No. 1, 03.2018, p. 113-128.

Research output: Contribution to journalArticleScientificpeer-review

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AB - The identification in a nonseparable single-index models with correlated random effects is considered in panel data with a fixed number of time periods. The identification assumption is based on the correlated random effects structure. Under this assumption, the parameters of interest are identified up to a multiplicative constant and could be estimated by an average difference of derivatives estimator based on the local polynomial smoothing. We suggest to linearly combine the estimators obtained for different orders of differences and derive the variance-minimizing weighting scheme. The asymptotic distribution of the proposed estimators is derived both for stationary and non-stationary explanatory variables along with a test of the stationarity. Finally, Monte Carlo experiments reveal finite sample properties of the proposed estimator.

KW - average derivative estimation

KW - correlated random effects

KW - local polynomia smoothing

KW - nonlinear panel data

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