A semiparametric panel model for unbalanced data with application to climate change in the United Kingdom

Atak, Alev, Linton, Oliver and Xiao, Zhijie (2011) A semiparametric panel model for unbalanced data with application to climate change in the United Kingdom. Journal of Econometrics, 164 (1). pp. 92-115. ISSN 0304-4076

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Abstract

This paper is concerned with developing a semiparametric panel model to explain the trend in UK temperatures and other weather outcomes over the last century. We work with the monthly averaged maximum and minimum temperatures observed at the twenty six Meteorological Office stations. The data is an unbalanced panel. We allow the trend to evolve in a nonparametric way so that we obtain a fuller picture of the evolution of common temperature in the medium timescale. Profile likelihood estimators (PLE) are proposed and their statistical properties are studied. The proposed PLE has improved asymptotic property comparing the sequential two-step estimators. Finally, forecasting based on the proposed model is studied. © 2011 Elsevier B.V. All rights reserved.

Item Type:	Article
Official URL:	http://www.elsevier.com/wps/find/journaldescriptio...
Additional Information:	© 2011 Elsevier
Divisions:	Economics STICERD Financial Markets Group
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences H Social Sciences > HC Economic History and Conditions
JEL classification:	C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C13 - Estimation C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C14 - Semiparametric and Nonparametric Methods C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C21 - Cross-Sectional Models; Spatial Models; Treatment Effect Models D - Microeconomics > D2 - Production and Organizations > D24 - Production; Cost; Capital and Total Factor Productivity; Capacity
Date Deposited:	08 Aug 2011 15:09
Last Modified:	11 Dec 2024 23:56
URI:	http://eprints.lse.ac.uk/id/eprint/37766

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