Unconditional Transformed Likelihood Estimation of Time-Space Dynamic Panel Data Models

Konferenzbeitrag

I derive the unconditional transformed likelihood function and its derivatives for a fixed-effects panel data model with time lags, spatial lags, and spatial time lags that encompasses the pure time dynamic and pure space dynamic models as special cases. In addition, the model can accommodate spatial dependence in the error term. I demonstrate that the model-consistent representation of the initial-period distribution involves higher-order spatial lag polynomials. Their order is linked to the minimal polynomial of the spatial weights matrix and, in general, tends to infinity with increasing sample size. Consistent estimation requires an appropriate truncation of these lag polynomials unless the spatial weights matrix has a regular structure. The finite sample evidence from Monte Carlo simulations shows that a misspecification of the spatial structure for the initial observations results in considerable biases while the correctly specified estimator behaves well. As an application, I estimate a time-space dynamic wage equation allowing for peer effects within households.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2014: Evidenzbasierte Wirtschaftspolitik - Session: Spatial and Panel Econometrics ; No. C17-V1

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Estimation: General ; Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models ; Wage Level and Structure; Wage Differentials

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Kripfganz, Sebastian
• Ereignis: Veröffentlichung
• (wer): ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften, Leibniz-Informationszentrum Wirtschaft
• (wo): Kiel und Hamburg
• (wann): 2014

Weitere Informationen

• Handle: http://hdl.handle.net/10419/100604
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ