# Estimating a common deterministic time trend break in large panels with cross sectional dependence

Research output: Contribution to journalArticle

21 Citations (Scopus)

### Abstract

This paper develops an estimation procedure for a common deterministic time trend break in large panels. The dependent variable in each equation consists of a deterministic trend and an error term. The deterministic trend is subject to a change in the intercept, slope or both, and the break date is common for all equations. The estimation method is simply minimizing the sum of squared residuals for all possible break dates. Both serial and cross sectional correlations are important factors that decide the rate of convergence and the limiting distribution of the break date estimate. The rate of convergence is faster when the errors are stationary than when they have a unit root. When there is no cross sectional dependence among the errors, the rate of convergence depends on the number of equations and thus is faster than the univariate case. When the errors have a common factor structure with factor loadings correlated with the intercept and slope change parameters, the rate of convergence does not depend on the number of equations and thus reduces to the univariate case. The limiting distribution of the break date estimate is also provided. Some Monte Carlo experiments are performed to assess the adequacy of the asymptotic results. A brief empirical example using the US GDP price index is offered.

Original language English 310-330 21 Journal of Econometrics 164 2 https://doi.org/10.1016/j.jeconom.2011.06.018 Published - 2011 Oct 1 Yes

### Fingerprint

Date
Rate of Convergence
Deterministic Trend
Intercept
Limiting Distribution
Univariate
Slope
Factor Structure
Common factor
Unit Root
Monte Carlo Experiment
Error term
Estimate
Trends
Rate of convergence
Trend breaks
Break dates
Time trends
Cross-sectional dependence
Cross-sectional Dependence

### Keywords

• Deterministic trend
• Panel data
• Structural break

### ASJC Scopus subject areas

• Economics and Econometrics
• Applied Mathematics
• History and Philosophy of Science

### Cite this

In: Journal of Econometrics, Vol. 164, No. 2, 01.10.2011, p. 310-330.

Research output: Contribution to journalArticle

@article{9bc5514480f0452fbea28ec8d453f754,
title = "Estimating a common deterministic time trend break in large panels with cross sectional dependence",
abstract = "This paper develops an estimation procedure for a common deterministic time trend break in large panels. The dependent variable in each equation consists of a deterministic trend and an error term. The deterministic trend is subject to a change in the intercept, slope or both, and the break date is common for all equations. The estimation method is simply minimizing the sum of squared residuals for all possible break dates. Both serial and cross sectional correlations are important factors that decide the rate of convergence and the limiting distribution of the break date estimate. The rate of convergence is faster when the errors are stationary than when they have a unit root. When there is no cross sectional dependence among the errors, the rate of convergence depends on the number of equations and thus is faster than the univariate case. When the errors have a common factor structure with factor loadings correlated with the intercept and slope change parameters, the rate of convergence does not depend on the number of equations and thus reduces to the univariate case. The limiting distribution of the break date estimate is also provided. Some Monte Carlo experiments are performed to assess the adequacy of the asymptotic results. A brief empirical example using the US GDP price index is offered.",
keywords = "Deterministic trend, Panel data, Structural break",
author = "Dukpa Kim",
year = "2011",
month = "10",
day = "1",
doi = "10.1016/j.jeconom.2011.06.018",
language = "English",
volume = "164",
pages = "310--330",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier BV",
number = "2",

}

TY - JOUR

T1 - Estimating a common deterministic time trend break in large panels with cross sectional dependence

AU - Kim, Dukpa

PY - 2011/10/1

Y1 - 2011/10/1

N2 - This paper develops an estimation procedure for a common deterministic time trend break in large panels. The dependent variable in each equation consists of a deterministic trend and an error term. The deterministic trend is subject to a change in the intercept, slope or both, and the break date is common for all equations. The estimation method is simply minimizing the sum of squared residuals for all possible break dates. Both serial and cross sectional correlations are important factors that decide the rate of convergence and the limiting distribution of the break date estimate. The rate of convergence is faster when the errors are stationary than when they have a unit root. When there is no cross sectional dependence among the errors, the rate of convergence depends on the number of equations and thus is faster than the univariate case. When the errors have a common factor structure with factor loadings correlated with the intercept and slope change parameters, the rate of convergence does not depend on the number of equations and thus reduces to the univariate case. The limiting distribution of the break date estimate is also provided. Some Monte Carlo experiments are performed to assess the adequacy of the asymptotic results. A brief empirical example using the US GDP price index is offered.

AB - This paper develops an estimation procedure for a common deterministic time trend break in large panels. The dependent variable in each equation consists of a deterministic trend and an error term. The deterministic trend is subject to a change in the intercept, slope or both, and the break date is common for all equations. The estimation method is simply minimizing the sum of squared residuals for all possible break dates. Both serial and cross sectional correlations are important factors that decide the rate of convergence and the limiting distribution of the break date estimate. The rate of convergence is faster when the errors are stationary than when they have a unit root. When there is no cross sectional dependence among the errors, the rate of convergence depends on the number of equations and thus is faster than the univariate case. When the errors have a common factor structure with factor loadings correlated with the intercept and slope change parameters, the rate of convergence does not depend on the number of equations and thus reduces to the univariate case. The limiting distribution of the break date estimate is also provided. Some Monte Carlo experiments are performed to assess the adequacy of the asymptotic results. A brief empirical example using the US GDP price index is offered.

KW - Deterministic trend

KW - Panel data

KW - Structural break

UR - http://www.scopus.com/inward/record.url?scp=84860390653&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84860390653&partnerID=8YFLogxK

U2 - 10.1016/j.jeconom.2011.06.018

DO - 10.1016/j.jeconom.2011.06.018

M3 - Article

AN - SCOPUS:84860390653

VL - 164

SP - 310

EP - 330

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 2

ER -