![Sample our Economics, Finance,Business & Industry journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5931/TOC-Subject-Sample-2021-Economics-Finance-Business-Industry.jpg"})
ABSTRACT
A general dynamic panel data model is considered that incorporates individual and interactive fixed effects allowing for contemporaneous correlation in model innovations. The model accommodates general stationary or nonstationary long-range dependence through interactive fixed effects and innovations, removing the necessity to perform a priori unit-root or stationarity testing. Moreover, persistence in innovations and interactive fixed effects allows for cointegration; innovations can also have vector-autoregressive dynamics; deterministic trends can be featured. Estimations are performed using conditional-sum-of-squares criteria based on projected series by which latent characteristics are proxied. Resulting estimates are consistent and asymptotically normal at standard parametric rates. A simulation study provides reliability on the estimation method. The method is then applied to the long-run relationship between debt and GDP. Supplementary materials for this article are available online.
ACKNOWLEDGMENTS
The author is grateful to Prof. Rong Chen, an associate editor, and two anonymous referees whose helpful suggestions and constructive comments have led to an improved version of the article. The author also thanks Carlos Velasco, Manuel Arellano, Yoosoon Chang, Miguel Delgado, Juan José Dolado, Jesús Gonzalo, Niels Haldrup, Javier Hualde, Serena Ng, Bent Nielsen, Peter M. Robinson, Enrique Sentana, Abderrahim Taamouti, the participants in CREATES Seminar 2015, RES Meeting 2015, and NBER-NSF Time Series Conference 2014 for their helpful comments and discussions. Financial support from the Spanish Plan Nacional de I+D+I (ECO2012-31748 and ECO2014-57007-P) is gratefully acknowledged. The author also acknowledges support from CREATES - Center for Research in Econometric Analysis of Time Series (DNRF78), funded by the Danish National Research Foundation.