Panel data segmentation under finite time horizon

Torgovitski, Leonid

doi:10.1016/j.jspi.2015.05.007

Cited by 2 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regarding testing for change points, several test statistics have been proposed by Emerson and Kao (2001, 2002), de Wachter and Tzavalis (2012), and Horváth and Hušková (2012), among others. Regarding estimation of multiple change points, one part of the literature is concerned with heterogeneous panels where the slope parameters are allowed to change across individuals or other panel units (see, e.g., Bai, 2010; Baltagi, Feng, & Kao, 2016; Chan, Horváth, & Hušková, 2013; Cho & Fryzlewicz, 2015; Cho, 2016; Horváth & Hušková, 2012; Kim, 2011; Okuy & Wang, 2018; Torgovitski, 2015). The other part, including this paper, focuses on homogeneous panels, where the slope parameters are constant across individuals.…”

Section: Introductionmentioning

confidence: 99%

“…Regarding estimation of multiple change points, one part of the literature is concerned with heterogeneous panels where the slope parameters are allowed to change across individuals or other panel units (see, e.g. Bai 2010;Kim 2011;Horváth and Hušková 2012;Chan, Horváth and Hušková 2013;Torgovitski 2015;Cho and Fryzlewicz 2015;Cho 2016;Baltagi, Feng and Kao 2016;Okuy and Wang 2018). The other part, including this paper, focuses on homogenous panels, where the slope parameters are constant across individuals.…”

Section: Introductionmentioning

confidence: 99%

“…Only a few papers develop methods for change point estimation in short panels (N → ∞, T fixed) while at the same time considering unobserved individual effects. Bai (2010) and Torgovitski (2015) do so, but treat the unobserved individual effects as individual means with potential change points, and estimate these change points in the mean without including other regressors in the model; Bai (2010) proposes an OLS method and Torgovitski (2015) focuses on non-parametric estimation. Qian and Su (2016) consider long and short panels, and estimate a panel regression model with fixed effects, where they rely on first-differenced data for consistent estimation of multiple change points.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Change point estimation in panel data with time‐varying individual effects

Boldea

Drepper²,

Gan

2020

J of Applied Econometrics

View full text Add to dashboard Cite

This paper proposes a method for estimating multiple change points in panel data models with unobserved individual effects via ordinary least-squares (OLS). Typically, in this setting, the OLS slope estimators are inconsistent due to the unobserved individual effects bias. As a consequence, existing methods remove the individual effects before change point estimation through data transformations such as first-differencing. We prove that under reasonable assumptions, the unobserved individual effects bias has no impact on the consistent estimation of change points. Our simulations show that since our method does not remove any variation in the dataset before change point estimation, it performs better in small samples compared to first-differencing methods. We focus on short panels because they are commonly used in practice, and allow for the unobserved individual effects to vary over time. Our method is illustrated via two applications: the environmental Kuznets curve and the U.S. house price expectations after the financial crisis. * We are grateful to

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Change point estimation in panel data with time‐varying individual effects

Boldea

Drepper²,

Gan

2020

J of Applied Econometrics

View full text Add to dashboard Cite

show abstract

Structural breaks in panel data: Large number of panels and short length time series

Antoch

Hanousek

Horváth

et al. 2018

Econometric Reviews

View full text Add to dashboard Cite

The detection of (structural) breaks or the so called change point problem has drawn increasing attention from the theoretical, applied economic and financial fields. Much of the existing research concentrates on the detection of change points and asymptotic properties of their estimators in panels when N, the number of panels, as well as T , the number of observations in each panel are large. In this paper we pursue a different approach, i.e., we consider the asymptotic properties when N → ∞ while keeping T fixed. This situation is typically related to large (firm-level) data containing financial information about an immense number of firms/stocks across a limited number of years/quarters/months. We propose a general approach for testing for break(s) in this setup. In particular, we obtain the asymptotic behavior of test statistics. We also propose a wild bootstrap procedure that could be used to generate the critical values of the test statistics. The theoretical approach is supplemented by numerous simulations and by an empirical illustration. We demonstrate that the testing procedure works well in the framework of the four factors CAPM model. In particular, we estimate the breaks in the monthly returns of US mutual funds during the period January 2006 to February 2010 which covers the subprime crises.

show abstract

Panel data segmentation under finite time horizon

Cited by 2 publications

References 16 publications

Change point estimation in panel data with time‐varying individual effects

Change point estimation in panel data with time‐varying individual effects

Structural breaks in panel data: Large number of panels and short length time series

Contact Info

Product

Resources

About