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FORECASTING WITH MIXED-FREQUENCY FACTOR MODELS IN THE PRESENCE OF COMMON TRENDS

Published online by Cambridge University Press:  14 November 2013

Peter Fuleky  and
Carl S. Bonham
Peter Fuleky*
Affiliation:
University of Hawaii
Carl S. Bonham
Affiliation:
University of Hawaii
*
Address correspondence to: Peter Fuleky, UHERO and Department of Economics, University of Hawaii, 542 Saunders Hall, 2424 Maile Way, Honolulu, HI 96822, USA; e-mail: fuleky@hawaii.edu.
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Abstract

We analyze the forecasting performance of small mixed-frequency factor models when the observed variables share stochastic trends. The indicators are observed at various frequencies and are tied together by cointegration so that valuable high-frequency information is passed to low-frequency series through the common factors. Differencing the data breaks the cointegrating link among the series and some of the signal leaks out to the idiosyncratic components, which do not contribute to the transfer of information among indicators. We find that allowing for common trends improves forecasting performance over a stationary factor model based on differenced data. The “common-trends factor model” outperforms the stationary factor model at all analyzed forecast horizons. Our results demonstrate that when mixed-frequency variables are cointegrated, modeling common stochastic trends improves forecasts.

Keywords

Dynamic Factor ModelMixed-Frequency SamplesCommon TrendsForecasting
Type
Articles
Information
Macroeconomic Dynamics , Volume 19 , Special Issue 4: Empirical Analysis of Business Cycles, Financial Markets, and Inflation: Essays in Honor of Charles Nelson , June 2015 , pp. 753 - 775
Copyright
Copyright © Cambridge University Press 2013 

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References

REFERENCES

Aruoba, S., Diebold, F., and Scotti, C. (2009) Real-time measurement of business conditions. Journal of Business and Economic Statistics 27 (4), 417–427.CrossRefGoogle Scholar
Azevedo, J.V.e., Koopman, S.J., and Rua, A. (2006) Tracking the business cycle of the euro area: A multivariate model-based bandpass filter. Journal of Business and Economic Statistics 24 (3), 278–290.CrossRefGoogle Scholar
Bai, J., Ghysels, E., and Wright, J. (2013) State space models and MIDAS regressions. Econometric Reviews 32 (7), 779–813.CrossRefGoogle Scholar
Camacho, M. and Perez-Quiros, G. (2010) Introducing the euro-sting: Short-term indicator of euro area growth. Journal of Applied Econometrics 25 (4), 663–694.CrossRefGoogle Scholar
Chow, G. and Lin, A. (1971) Best linear unbiased interpolation, distribution, and extrapolation of time series by related series. Review of Economics and Statistics 53 (4), 372–375.CrossRefGoogle Scholar
Christoffersen, P. and Diebold, F. (1998) Cointegration and long-horizon forecasting. Journal of Business and Economic Statistics 16 (4), 450–458.Google Scholar
Clements, M. and Galvão, A. (2008) Macroeconomic forecasting with mixed-frequency data. Journal of Business and Economic Statistics 26 (4), 546–554.CrossRefGoogle Scholar
Clements, M. and Galvão, A. (2009) Forecasting us output growth using leading indicators: An appraisal using midas models. Journal of Applied Econometrics 24 (7), 1187–1206.CrossRefGoogle Scholar
Dempster, A.P., Laird, N.M., and Rubin, D.B. (1977) Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39 (1), 1–38.CrossRefGoogle Scholar
Dickey, D. and Fuller, W. (1979) Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74 (366), 427–431.Google Scholar
Diebold, F. and Mariano, R. (1995) Comparing predictive accuracy. Journal of Business and Economic Statistics 13 (3), 253–263.Google Scholar
Doornik, J. (2007) Object-Oriented Matrix Programming Using Ox, 3rd ed.London: Timberlake Consultants Press.Google Scholar
Evans, M. (2005) Where are we now? Real-time estimates of the macroeconomy. International Journal of Central Banking 1 (2), 127–176.Google Scholar
Friedman, M. (1962) The interpolation of time series by related series. Journal of the American Statistical Association 57 (300), 729–757.CrossRefGoogle Scholar
Ghysels, E., Santa-Clara, P., and Valkanov, R. (2004) The MIDAS Touch: Mixed Data Sampling Regression Models. Working paper, Anderson Graduate School of Management, UCLA.Google Scholar
Ghysels, E., Sinko, A., and Valkanov, R. (2007) MIDAS regressions: Further results and new directions. Econometric Reviews 26 (1), 53–90.Google Scholar
Giannone, D., Reichlin, L., and Small, D. (2008) Nowcasting: The real-time informational content of macroeconomic data. Journal of Monetary Economics 55 (4), 665–676.CrossRefGoogle Scholar
Götz, T., Hecq, A., and Urbain, J. (2012) Forecasting Mixed Frequency Time Series with ECM-MIDAS Models. Working paper, DP Maastricht University.Google Scholar
Harvey, A. (1989) Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge, UK: Cambridge University Press.Google Scholar
Hyung, N. and Granger, C. (2008) Linking series generated at different frequencies. Journal of Forecasting 27 (2), 95–108.CrossRefGoogle Scholar
Johansen, S. (1988) Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control 12 (2), 231–254.CrossRefGoogle Scholar
Koopman, S. and Lucas, A. (2005) Business and default cycles for credit risk. Journal of Applied Econometrics 20 (2), 311–323.CrossRefGoogle Scholar
Koopman, S., Shephard, N., and Doornik, J. (1999) Statistical algorithms for models in state space using SsfPack 2.2. Econometrics Journal 2 (1), 107–160.CrossRefGoogle Scholar
Little, R.J. and Rubin, D.B. (1987) Statistical Analysis with Missing Data. New York: Wiley.Google Scholar
Macho, F.J.F., Harvey, A.C., and Stock, J.H. (1987) Forecasting and interpolation using vector autoregressions with common trends. Annales d'Economie et de Statistique 6 (7), 279–287.CrossRefGoogle Scholar
Marcellino, M. (1999) Some consequences of temporal aggregation in empirical analysis. Journal of Business and Economic Statistics 17 (1), 129–136.Google Scholar
Mariano, R. and Murasawa, Y. (2003) A new coincident index of business cycles based on monthly and quarterly series. Journal of Applied Econometrics 18 (4), 427–443.CrossRefGoogle Scholar
Nunes, L. (2005) Nowcasting quarterly GDP growth in a monthly coincident indicator model. Journal of Forecasting 24 (8), 575–592.CrossRefGoogle Scholar
Palm, F.C. and Nijman, T.E. (1984) Missing observations in the dynamic regression model. Econometrica 52 (6), 1415–1435.CrossRefGoogle Scholar
Proietti, T. and Moauro, F. (2006) Dynamic factor analysis with non-linear temporal aggregation constraints. Journal of the Royal Statistical Society Series C 55 (2), 281–300.CrossRefGoogle Scholar
Seong, B., Ahn, S., and Zadrozny, P. (2013) Estimation of vector error correction models with mixed-frequency data. Journal of Time Series Analysis 34 (2), 194–205.CrossRefGoogle Scholar
Stock, J. and Watson, M. (1992) A probability model of the coincident economic indicators. In Lahiri, K. and Moore, G. H. (eds.), Leading Economic Indicators: New Approaches and Forecasting Records. Cambridge, UK: Cambridge University Press.Google Scholar