Finite sample performance of small versus large scale dynamic factor models
AbstractWe examine the finite-sample performance of small versus large scale dynamic factor models. Our Monte Carlo analysis reveals that small scale factor models out-perform large scale models in factor estimation and forecasting for high levels of cross-correlation across the idiosyncratic errors of series belonging to the same category, for oversampled categories and, especially, for high persistence in either the common factor series or the idiosyncratic errors. Using a panel of 147 US economic indicators, which are classified into 13 economic categories, we show that a small scale dynamic factor model that uses one representative indicator of each category yields satisfactory or even better forecasting results than a large scale dynamic factor model that uses all the economic indicator
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Bibliographic InfoPaper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 8867.
Date of creation: Mar 2012
Date of revision:
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Other versions of this item:
- Rocio Alvarez & Maximo Camacho & Gabriel Perez-Quiros, 2012. "Finite sample performance of small versus large scale dynamic factor models," Banco de Espaï¿½a Working Papers 1204, Banco de Espa�a.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- E27 - Macroeconomics and Monetary Economics - - Macroeconomics: Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-03-28 (All new papers)
- NEP-ECM-2012-03-28 (Econometrics)
- NEP-FOR-2012-03-28 (Forecasting)
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