Short T dynamic panel data models with individual, time and interactive effects

4Citations
Citations of this article
12Readers
Mendeley users who have this article in their library.

Abstract

This paper proposes a transformed quasi-maximum likelihood (TQML) estimator for short (Figure presented.) dynamic fixed effects panel data models allowing for interactive effects through a multifactor error structure. The proposed estimator is robust to the heterogeneity of the initial values and common unobserved effects, while at the same time allowing for standard fixed and time effects. It is applicable to both stationary and unit root cases. The order condition for identification of the number of interactive effects is established, and conditions are derived under which the parameters are locally identified. It is shown that global identification in the presence of the lagged dependent variable cannot be guaranteed. The TQML estimator is proven to be consistent and asymptotically normally distributed. A sequential multiple testing likelihood ratio procedure is also proposed for estimation of the number of factors which is shown to be consistent. Finite sample results obtained from Monte Carlo simulations show that the proposed procedure for determining the number of factors performs very well, and the TQML estimator has small bias and root mean square error (RMSE) and correct empirical size in most settings. The practical use of the TQML approach is demonstrated by means of two empirical illustrations from the literature on cross county crime rates and cross country growth regressions.

References Powered by Scopus

Some tests of specification for panel data:monte carlo evidence and an application to employment equations

19047Citations
N/AReaders
Get full text

Initial conditions and moment restrictions in dynamic panel data models

15542Citations
N/AReaders
Get full text

Another look at the instrumental variable estimation of error-components models

11812Citations
N/AReaders
Get full text

Cited by Powered by Scopus

How does digital economy development affect renewable energy innovation?

53Citations
N/AReaders
Get full text

DEVELOPMENT OF RETAIL BANKING SERVICES IN THE CONTEXT OF DIGITAL TRANSFORMATION

4Citations
N/AReaders
Get full text

Econometric Aspects of Convergence: A Survey

2Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Hayakawa, K., Pesaran, M. H., & Smith, L. V. (2023). Short T dynamic panel data models with individual, time and interactive effects. Journal of Applied Econometrics, 38(6), 940–967. https://doi.org/10.1002/jae.2981

Readers' Seniority

Tooltip

Professor / Associate Prof. 4

44%

PhD / Post grad / Masters / Doc 3

33%

Lecturer / Post doc 2

22%

Readers' Discipline

Tooltip

Economics, Econometrics and Finance 8

80%

Computer Science 1

10%

Business, Management and Accounting 1

10%

Article Metrics

Tooltip
Mentions
News Mentions: 1

Save time finding and organizing research with Mendeley

Sign up for free