Econometric Analysis of Panel Data Models with Multifactor Error Structures

Hande Karabiyik, Franz C. Palm*, Jean-Pierre Urbain

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

Abstract

Econometric panel data exhibit cross-sectional dependence, even after conditioning on appropriate explanatory variabes. Two approaches to modeling cross-sectional dependence in economic panel data are often used: the spatial dependence approach, which explains cross-sectional dependence in terms of distance among units. and the residual multifactor approach, which explains cross-sectional dependence by common factors that affect individuals to a different extent.
This article reviews the theory on estimation and statistical inference for stationary and non-stationary panel data with cross-sectional dependence, particularly for models with a multifactor error structure. Tests and diagnostics for testing for unit roots, slope homogeneity, cointegration, and the number of factors are provided. We discuss issues such as estimating common factors, dealing with parameter plethora in practice, testing for structural stability and nonlinearity, and dealing with model and parameter uncertainty. Finally, we address issues related to the use of these economic panel models.
Original languageEnglish
Title of host publicationANNUAL REVIEW OF ECONOMICS, VOL 11, 2019
EditorsP Aghion, H Rey
PublisherAnnual Reviews Inc.
Pages495-522
Number of pages28
Volume11
DOIs
Publication statusPublished - 2019

Publication series

SeriesAnnual Review of Economics
Volume11
ISSN1941-1383

Keywords

  • panel data
  • cross-sectional dependence
  • factor-augmented panel regression
  • common correlated effects
  • principal components
  • stationary panels
  • nonstationary panels
  • CROSS-SECTIONAL DEPENDENCE
  • UNIT-ROOT TESTS
  • MAXIMUM-LIKELIHOOD-ESTIMATION
  • BAYESIAN SHRINKAGE
  • REGRESSION-MODELS
  • SLOPE HOMOGENEITY
  • CCE ESTIMATION
  • LARGE NUMBER
  • TIME-SERIES
  • COINTEGRATION

Cite this

Karabiyik, H., Palm, F. C., & Urbain, J-P. (2019). Econometric Analysis of Panel Data Models with Multifactor Error Structures. In P. Aghion, & H. Rey (Eds.), ANNUAL REVIEW OF ECONOMICS, VOL 11, 2019 (Vol. 11, pp. 495-522). Annual Reviews Inc.. Annual Review of Economics, Vol.. 11 https://doi.org/10.1146/annurev-economics-063016-104338
Karabiyik, Hande ; Palm, Franz C. ; Urbain, Jean-Pierre. / Econometric Analysis of Panel Data Models with Multifactor Error Structures. ANNUAL REVIEW OF ECONOMICS, VOL 11, 2019. editor / P Aghion ; H Rey. Vol. 11 Annual Reviews Inc., 2019. pp. 495-522 (Annual Review of Economics, Vol. 11).
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abstract = "Econometric panel data exhibit cross-sectional dependence, even after conditioning on appropriate explanatory variabes. Two approaches to modeling cross-sectional dependence in economic panel data are often used: the spatial dependence approach, which explains cross-sectional dependence in terms of distance among units. and the residual multifactor approach, which explains cross-sectional dependence by common factors that affect individuals to a different extent. This article reviews the theory on estimation and statistical inference for stationary and non-stationary panel data with cross-sectional dependence, particularly for models with a multifactor error structure. Tests and diagnostics for testing for unit roots, slope homogeneity, cointegration, and the number of factors are provided. We discuss issues such as estimating common factors, dealing with parameter plethora in practice, testing for structural stability and nonlinearity, and dealing with model and parameter uncertainty. Finally, we address issues related to the use of these economic panel models.",
keywords = "panel data, cross-sectional dependence, factor-augmented panel regression, common correlated effects, principal components, stationary panels, nonstationary panels, CROSS-SECTIONAL DEPENDENCE, UNIT-ROOT TESTS, MAXIMUM-LIKELIHOOD-ESTIMATION, BAYESIAN SHRINKAGE, REGRESSION-MODELS, SLOPE HOMOGENEITY, CCE ESTIMATION, LARGE NUMBER, TIME-SERIES, COINTEGRATION",
author = "Hande Karabiyik and Palm, {Franz C.} and Jean-Pierre Urbain",
note = "data source: No data used",
year = "2019",
doi = "10.1146/annurev-economics-063016-104338",
language = "English",
volume = "11",
series = "Annual Review of Economics",
pages = "495--522",
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publisher = "Annual Reviews Inc.",
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Karabiyik, H, Palm, FC & Urbain, J-P 2019, Econometric Analysis of Panel Data Models with Multifactor Error Structures. in P Aghion & H Rey (eds), ANNUAL REVIEW OF ECONOMICS, VOL 11, 2019. vol. 11, Annual Reviews Inc., Annual Review of Economics, vol. 11, pp. 495-522. https://doi.org/10.1146/annurev-economics-063016-104338

Econometric Analysis of Panel Data Models with Multifactor Error Structures. / Karabiyik, Hande; Palm, Franz C.; Urbain, Jean-Pierre.

ANNUAL REVIEW OF ECONOMICS, VOL 11, 2019. ed. / P Aghion; H Rey. Vol. 11 Annual Reviews Inc., 2019. p. 495-522 (Annual Review of Economics, Vol. 11).

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

TY - CHAP

T1 - Econometric Analysis of Panel Data Models with Multifactor Error Structures

AU - Karabiyik, Hande

AU - Palm, Franz C.

AU - Urbain, Jean-Pierre

N1 - data source: No data used

PY - 2019

Y1 - 2019

N2 - Econometric panel data exhibit cross-sectional dependence, even after conditioning on appropriate explanatory variabes. Two approaches to modeling cross-sectional dependence in economic panel data are often used: the spatial dependence approach, which explains cross-sectional dependence in terms of distance among units. and the residual multifactor approach, which explains cross-sectional dependence by common factors that affect individuals to a different extent. This article reviews the theory on estimation and statistical inference for stationary and non-stationary panel data with cross-sectional dependence, particularly for models with a multifactor error structure. Tests and diagnostics for testing for unit roots, slope homogeneity, cointegration, and the number of factors are provided. We discuss issues such as estimating common factors, dealing with parameter plethora in practice, testing for structural stability and nonlinearity, and dealing with model and parameter uncertainty. Finally, we address issues related to the use of these economic panel models.

AB - Econometric panel data exhibit cross-sectional dependence, even after conditioning on appropriate explanatory variabes. Two approaches to modeling cross-sectional dependence in economic panel data are often used: the spatial dependence approach, which explains cross-sectional dependence in terms of distance among units. and the residual multifactor approach, which explains cross-sectional dependence by common factors that affect individuals to a different extent. This article reviews the theory on estimation and statistical inference for stationary and non-stationary panel data with cross-sectional dependence, particularly for models with a multifactor error structure. Tests and diagnostics for testing for unit roots, slope homogeneity, cointegration, and the number of factors are provided. We discuss issues such as estimating common factors, dealing with parameter plethora in practice, testing for structural stability and nonlinearity, and dealing with model and parameter uncertainty. Finally, we address issues related to the use of these economic panel models.

KW - panel data

KW - cross-sectional dependence

KW - factor-augmented panel regression

KW - common correlated effects

KW - principal components

KW - stationary panels

KW - nonstationary panels

KW - CROSS-SECTIONAL DEPENDENCE

KW - UNIT-ROOT TESTS

KW - MAXIMUM-LIKELIHOOD-ESTIMATION

KW - BAYESIAN SHRINKAGE

KW - REGRESSION-MODELS

KW - SLOPE HOMOGENEITY

KW - CCE ESTIMATION

KW - LARGE NUMBER

KW - TIME-SERIES

KW - COINTEGRATION

U2 - 10.1146/annurev-economics-063016-104338

DO - 10.1146/annurev-economics-063016-104338

M3 - Chapter

VL - 11

T3 - Annual Review of Economics

SP - 495

EP - 522

BT - ANNUAL REVIEW OF ECONOMICS, VOL 11, 2019

A2 - Aghion, P

A2 - Rey, H

PB - Annual Reviews Inc.

ER -

Karabiyik H, Palm FC, Urbain J-P. Econometric Analysis of Panel Data Models with Multifactor Error Structures. In Aghion P, Rey H, editors, ANNUAL REVIEW OF ECONOMICS, VOL 11, 2019. Vol. 11. Annual Reviews Inc. 2019. p. 495-522. (Annual Review of Economics, Vol. 11). https://doi.org/10.1146/annurev-economics-063016-104338