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Using VARs and TVP-VARs with Many Macroeconomic Variables

  • Gary, Koop

This paper discusses the challenges faced by the empirical macroeconomist and methods for surmounting them. These challenges arise due to the fact that macroeconometric models potentially include a large number of variables and allow for time variation in parameters. These considerations lead to models which have a large number of parameters to estimate relative to the number of observations. A wide range of approaches are surveyed which aim to overcome the resulting problems. We stress the related themes of prior shrinkage, model averaging and model selection. Subsequently, we consider a particular modelling approach in detail. This involves the use of dynamic model selection methods with large TVP-VARs. A forecasting exercise involving a large US macroeconomic data set illustrates the practicality and empirical success of our approach.

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File URL: http://hdl.handle.net/10943/443
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Paper provided by Scottish Institute for Research in Economics (SIRE) in its series SIRE Discussion Papers with number 2013-35.

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Date of creation: 2013
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Handle: RePEc:edn:sirdps:443
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  22. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "Forecasting using a large number of predictors: Is Bayesian regression a valid alternative to principal components?," Working Paper Series 0700, European Central Bank.
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