Fixed T dynamic panel data estimators with multifactor errors

Artūras Juodis, Vasilis Sarafidis

Research output: Contribution to journalArticleResearchpeer-review

Abstract

This article analyzes a growing group of fixed T dynamic panel data estimators with a multifactor error structure. We use a unified notational approach to describe these estimators and discuss their properties in terms of deviations from an underlying set of basic assumptions. Furthermore, we consider the extendability of these estimators to practical situations that may frequently arise, such as their ability to accommodate unbalanced panels and common observed factors. Using a large-scale simulation exercise, we consider scenarios that remain largely unexplored in the literature, albeit being of great empirical relevance. In particular, we examine (i) the effect of the presence of weakly exogenous covariates, (ii) the effect of changing the magnitude of the correlation between the factor loadings of the dependent variable and those of the covariates, (iii) the impact of the number of moment conditions on bias and size for GMM estimators, and finally (iv) the effect of sample size. We apply each of these estimators to a crime application using a panel data set of local government authorities in New South Wales, Australia; we find that the results bear substantially different policy implications relative to those potentially derived from standard dynamic panel GMM estimators. Thus, our study may serve as a useful guide to practitioners who wish to allow for multiplicative sources of unobserved heterogeneity in their model.

Original languageEnglish
Pages (from-to)893-929
Number of pages37
JournalEconometric Reviews
Volume37
Issue number8
DOIs
Publication statusPublished - 2018

Keywords

  • Dynamic panel data
  • factor model
  • fixed T consistency
  • maximum likelihood
  • Monte Carlo simulation

Cite this

@article{042e507878114b9581c5889eb88e2ff7,
title = "Fixed T dynamic panel data estimators with multifactor errors",
abstract = "This article analyzes a growing group of fixed T dynamic panel data estimators with a multifactor error structure. We use a unified notational approach to describe these estimators and discuss their properties in terms of deviations from an underlying set of basic assumptions. Furthermore, we consider the extendability of these estimators to practical situations that may frequently arise, such as their ability to accommodate unbalanced panels and common observed factors. Using a large-scale simulation exercise, we consider scenarios that remain largely unexplored in the literature, albeit being of great empirical relevance. In particular, we examine (i) the effect of the presence of weakly exogenous covariates, (ii) the effect of changing the magnitude of the correlation between the factor loadings of the dependent variable and those of the covariates, (iii) the impact of the number of moment conditions on bias and size for GMM estimators, and finally (iv) the effect of sample size. We apply each of these estimators to a crime application using a panel data set of local government authorities in New South Wales, Australia; we find that the results bear substantially different policy implications relative to those potentially derived from standard dynamic panel GMM estimators. Thus, our study may serve as a useful guide to practitioners who wish to allow for multiplicative sources of unobserved heterogeneity in their model.",
keywords = "Dynamic panel data, factor model, fixed T consistency, maximum likelihood, Monte Carlo simulation",
author = "Artūras Juodis and Vasilis Sarafidis",
year = "2018",
doi = "10.1080/00927872.2016.1178875",
language = "English",
volume = "37",
pages = "893--929",
journal = "Econometric Reviews",
issn = "0747-4938",
publisher = "Taylor & Francis",
number = "8",

}

Fixed T dynamic panel data estimators with multifactor errors. / Juodis, Artūras; Sarafidis, Vasilis.

In: Econometric Reviews, Vol. 37, No. 8, 2018, p. 893-929.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Fixed T dynamic panel data estimators with multifactor errors

AU - Juodis, Artūras

AU - Sarafidis, Vasilis

PY - 2018

Y1 - 2018

N2 - This article analyzes a growing group of fixed T dynamic panel data estimators with a multifactor error structure. We use a unified notational approach to describe these estimators and discuss their properties in terms of deviations from an underlying set of basic assumptions. Furthermore, we consider the extendability of these estimators to practical situations that may frequently arise, such as their ability to accommodate unbalanced panels and common observed factors. Using a large-scale simulation exercise, we consider scenarios that remain largely unexplored in the literature, albeit being of great empirical relevance. In particular, we examine (i) the effect of the presence of weakly exogenous covariates, (ii) the effect of changing the magnitude of the correlation between the factor loadings of the dependent variable and those of the covariates, (iii) the impact of the number of moment conditions on bias and size for GMM estimators, and finally (iv) the effect of sample size. We apply each of these estimators to a crime application using a panel data set of local government authorities in New South Wales, Australia; we find that the results bear substantially different policy implications relative to those potentially derived from standard dynamic panel GMM estimators. Thus, our study may serve as a useful guide to practitioners who wish to allow for multiplicative sources of unobserved heterogeneity in their model.

AB - This article analyzes a growing group of fixed T dynamic panel data estimators with a multifactor error structure. We use a unified notational approach to describe these estimators and discuss their properties in terms of deviations from an underlying set of basic assumptions. Furthermore, we consider the extendability of these estimators to practical situations that may frequently arise, such as their ability to accommodate unbalanced panels and common observed factors. Using a large-scale simulation exercise, we consider scenarios that remain largely unexplored in the literature, albeit being of great empirical relevance. In particular, we examine (i) the effect of the presence of weakly exogenous covariates, (ii) the effect of changing the magnitude of the correlation between the factor loadings of the dependent variable and those of the covariates, (iii) the impact of the number of moment conditions on bias and size for GMM estimators, and finally (iv) the effect of sample size. We apply each of these estimators to a crime application using a panel data set of local government authorities in New South Wales, Australia; we find that the results bear substantially different policy implications relative to those potentially derived from standard dynamic panel GMM estimators. Thus, our study may serve as a useful guide to practitioners who wish to allow for multiplicative sources of unobserved heterogeneity in their model.

KW - Dynamic panel data

KW - factor model

KW - fixed T consistency

KW - maximum likelihood

KW - Monte Carlo simulation

UR - http://www.scopus.com/inward/record.url?scp=84978123717&partnerID=8YFLogxK

U2 - 10.1080/00927872.2016.1178875

DO - 10.1080/00927872.2016.1178875

M3 - Article

AN - SCOPUS:84978123717

VL - 37

SP - 893

EP - 929

JO - Econometric Reviews

JF - Econometric Reviews

SN - 0747-4938

IS - 8

ER -