Estimation of Country-Pair Data Models Controlling for Clustered Errors: with International Trade Applications
AbstractWe consider cross-section regression models for country-pair data, such as gravity models for trade volume between countries or models of exchange rate volatility, allowing for the presence of country-specific errors. This induces clustered errors in a nonstandard setting. OLS standard errors that ignore this clustering are greatly underestimated. Under the assumption of random country-specific effects we provide analytical results that permit more efficient GLS estimation even in settings where the number of unique country-pairs is very large. We include applications to international data on real exchange rates and on bilateral trade that provided the motivation for this paper. The results are more generally applicable to regression with paired data.
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Bibliographic InfoPaper provided by University of California, Davis, Department of Economics in its series Working Papers with number 613.
Date of creation: 31 Jan 2005
Date of revision:
clustered errors; random effects; country-pair data; international trade data; exchange rate data;
Find related papers by JEL classification:
- C29 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Other
- F14 - International Economics - - Trade - - - Empirical Studies of Trade
- F31 - International Economics - - International Finance - - - Foreign Exchange
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