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Modeling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model

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  • Pesaran M.H.
  • Schuermann T.
  • Weiner S.M.

Abstract

Financial institutions such as banks are ultimately exposed to macroeconomic fluctuations I the countries to which they have exposure, the most acute example being commercial lending to companies whose fortunes fluctuate with aggregate demand. This risk management need for financial institutions motivated us to build a compact global macroeconomic model capable of generating (point as well as density) forecasts for a core set of macroeconomic factors for a set of regions and countries which explicitly allows for interconnections and interdependencies that exist between national and international factors. This paper provides such a global modeling framework; making use of recent advances in the analysis of cointegrating systems. In an unrestricted VAR(p) model in k endogenous variables covering N countries, the number of unknown parameters will be unfeasibly large, of order p(kN-1), requiring a more parsimonious model specification. We first estimate individual country/region specific vector error correcting models, where the domestic macroeconomic variables are related to corresponding foreign variable constructed exclusively to match the international trade pattern of the country under consideration. The individual country models are then combined in a consistent and cohesive manner to generate forecasts for all the variables in the world economy simultaneously. We estimate the model for 26 countries grouped into 11 regions using quarterly data from 1970Q1 to 1999Q1 and shed light on the degree of regional interdependencies by investigating the time profile of the transmission of shocks to one variable in a given country/region to the rest of the world. We then use the estimated global model as the economic engine for generating a conditional loss distribution of a credit portfolio and illustrate the effects of various global risk scenarios on the loss distribution.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 22 (2004)
Issue (Month): (April)
Pages: 129-162

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Handle: RePEc:bes:jnlbes:v:22:y:2004:p:129-162

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