Functional Coefficient Panel Modeling with Communal Smoothing Covariates

Peter Phillips and Ying Wang
Additional contact information
Ying Wang: The University of Auckland

No 2193, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: Behavior at the individual level in panels or at the station level in spatial models is often influenced by aspects of the system in aggregate. In particular, the nature of the interaction between individual-specific explanatory variables and an individual dependent variable may be affected by `global' variables that are relevant in decision making and shared communally by all individuals in the sample. To capture such behavioral features, we employ a functional coefficient panel model in which certain communal covariates may jointly influence panel interactions by means of their impact on the model coefficients. Two classes of estimation procedures are proposed, one based on station averaged data the other on the full panel, and their asymptotic properties are derived. Inference regarding the functional coefficient is also considered. The finite sample performance of the proposed estimators and tests are examined by simulation. An empirical spatial model illustration is provided in which the climate sensitivity of temperature to atmospheric CO_2 concentration is studied at both station and global levels.

Keywords: Climate modeling; Communal covariates; Fixed effects; Functional coefficients; Panel data; Spatial modeling (search for similar items in EconPapers)
JEL-codes: C14 C23 (search for similar items in EconPapers)
Pages: 81 pages
Date: 2019-05
New Economics Papers: this item is included in nep-ecm and nep-env
Note: Includes Supplimental Material
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in Journal of Econometrics (April 2022), 227(2): 371-407

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Journal Article: Functional coefficient panel modeling with communal smoothing covariates (2022)
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