Email this article Financial and Real Latent factors in Forecasting Economic Time Series
Abstract
In this paper we want to assess the impact of real and financial variables in estimating smoothed GDP. We implement the generalized dynamic factor model, on which Eurocoin indicator is based. We can assess that the impact of real and financial variables in estimating smoothed GDP, during the structural break in 2008, shows that the role of real data as industrial production, foreign trade, employment indexes, becomes particularly relevant in relation to that concerning financial data as money supply, spreads.
References
Altissimo, Cristadoro, Forni, Lippi, Veronese (2009) New Eurocoin: tracking economic growth in real time”, CEPR Discussion Paper 5633, 2007.
Bai, J., S. Ng (2002). Determining the number of factors in approximate factor models. Econometrica 70, 191-221.
Baxter, A., King, R.G. (1999). Measuring Business Cycle Approximate Band-Pass filters for Economic Time Series. The Review of Economics and Statistics 81, 575-93.
Brillinger, D. (1981). Time series – Data Analysis and Theory, expanded ed., San Francisco. Christiano, L.J., Fitzgerald, T.J. (2003). The Band-Pass Filter. International Economic Review 84, 435-65.
Chamberlain, G., Rothschild, M. (1983). Arbitrage, Factor Structure and Mean-Variance
Analysis in Large Asset Markets. Econometrica, 70, 191–221.
Cristadoro, R., Forni, M., Reichlin, L., Veronese, G. (2005). A core inflation indicator for the euro area, Journal of Money Credit and Banking 37(3), 539-560.
Forni, M., M. Hallin, M. Lippi, L. Reichlin (2004). The generalized dynamic factor model: consistency and rates. Journal of Econometrics 119, 231-255.
Geweke J. (1977). The Dynamic Factor Analysis of Economic Time Series Models, in Latent
Variables in Socioeconomic Models, North Holland, pp.365-383.
D. Giannone, M. Lenza, L. Reichlin (2009). Business cycles in the euro area. European Central Bank Working Paper Series-
Hallin, M., R. Liska (2007). Determining the Number of Factors in the Generalized Factor Model, Journal of the American Statistical Association, 102, No. 478, 603-617.
Sargent, T. J., C. A. Sims (1977). Business Cycle Modeling Without Pretending to have too Much a priori Economic Theory. New Methods in Business Cycles Research, Federal Reserve Bank of Minneapolis, pp. 45-110.
Stock, J.H., M.W. Watson (2002). Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association 97, 1167-79.
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