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- W621831317 abstract "In the presented master thesis a problem of forecasting U.S. realeconomic variables growth rates by the means of dynamic factor models isconsidered. Forecasting horizons vary from 1 month to 1 year. The research isfocused on different methods of dynamic factors’ estimation. The followingmodifications of the standard approach are investigated. Firstly,implementation of analysis and selection of predictor variables prior to afactors’ estimation step. Secondly, use of principal covariate regression insteadof more standard principal component regression. Thirdly, consecutive use ofvariables selection and principal covariate regression methods. Forecastingaccuracy conclusions are based on comparison of mean squared predictionerrors and recession periods dating. Empirical results stand for introducedmodifications and their combination." @default.
- W621831317 created "2016-06-24" @default.
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- W621831317 date "2010-09-07" @default.
- W621831317 modified "2023-09-26" @default.
- W621831317 title "Using Principal Covariate Regression for Macroeconomic Time Series Forecasting" @default.
- W621831317 hasPublicationYear "2010" @default.
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