Abstract
References
1.
Armah, N.A. and Swanson, N.R. (2008). Seeing Inside the Black Box: Using Diffusion Index Methodology to Construct Factor Proxies in Large Scale Macroeconomic Time Series Environments. Federal Reserve Bank of Philadelphia Working Paper No. 08-25.
2.
Aruoba, S.B., Diebold, F.X., and Scotti, C. (2009). Real-Time Measurement of Business Conditions. Journal of Business and Economic Statistics, 27, 417-427.
3.
Baffigi, A., Golinelli, R., and Parigi, G. (2004). Bridge Model to Forecast the Euro Area GDP. International Journal of Forecasting, 20, 447-460.
4.
Bloem, A.M., Dippelsman, R.J., and Maehle, N.O. (2001). Manual de Cuentas Nacionales Trimestrales. Conceptos, fuentes de datos y compilación. Washington, D.C. International Monetary Fund.
5.
Clements, M.P. and Galvao, A.B. (2008). Macroeconomic Forecasting with Mixed- Frequency Data: Forecasting Output Growth in the United States. Journal of Business and Economic Statistics, 26, 546-554.
6.
Diebold, F.X. (2001). Elements of Forecasting, (2nd Edition). Cincinnati: South-Western.
7.
Diron, M. (2006). Short-Term Forecasts of Euro Area Real GDP Growth. An Assessment of Real-Time Performance Based on Vintage Data. European Central Bank Working Paper No. 622.
8.
Enders, W. (2003). Applied Econometric Time Series, (2nd Edition). New York: Wiley.
9.
Fair, R.C. and Shiller, J. (1990). Comparing Information in Forecasts from Econometric Models. The American Economic Review, 80, 375-389.
10.
Forni, M. and Reichlin, L. (1998). Let’s Get Real: A Dynamic Factor Analytical Approach to Disaggregated Business Cycle. Review of Economic Studies, 65, 453-474.
11.
Forni, M., Hallin, M., Lippi, M., and Reichlin, L. (2005). The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting. Journal of the American Statistical Association, 100, 830-840.
12.
Geweke, J. (1977). The Dynamic Factor Analysis of Economic Time Series. Latent Variables in Socio-Economic Models, D.J. Aigner and A.S. Goldberger (eds). Amsterdam: North-Holland.
13.
Ghysels, E., Santa-Clara, P., and Valkanov, R. (2004). The MIDAS Touch: Mixed Data Sampling Regression Models. Unpublished manuscript, University of North Carolina. Available at: http://www.unc.edu/eghysels (accessed April, 2013).
14.
Ghysels, E. (2012). Macroeconomics and the Reality of Mixed Frequency Data. Chapel Hill: Manuscript, University of North Carolina.
15.
Granger, C.W.J. (1996). Can We Improve the Perceived Quality of Economic Forecasts? Journal of Applied Econometrics, 11, 455-473.
16.
Guerrero, V.M. (1993). Time Series Analysis Supported by Power Transformations. Journal of Forecasting, 12, 37-48.
17.
INEGI (2007). Sistema de Clasificación Industrial de América del Norte, México. SCIAN 2007, (Third edition). México: Instituto Nacional de Estadística, Geografıá e Informática.
18.
Katz, A.J. (2006). An Overview of BEA’s Source Data and Estimating Methods for Quarterly GDP. Paper prepared for the 10th OECD-NBS Workshop on National Accounts. Paris, France, November 6-10, 2006.
19.
Kitchen, J. and Monaco, R. (2003). Real-Time Forecasting in Practice: The U.S. Treasury Staff’s Real-Time GDP Forecast System. Business Economics, October, 10-19.
20.
Klein, L.R. and Sojo, E. (1989). Combinations of High and Low Frequency Data in Macroeconometric Models. Economics in Theory and Practice: An Eclectic Approach, L.R. Klein and J. Marquez (eds). Norwell, MA: Kluwer Academic Publishers.
21.
Koenig, E.F., Dolmas, S., and Piger, J. (2003). The Use and Abuse of Real-Time Data in Economic Forecasting. The Review of Economics and Statistics, 85, 618-628.
22.
Kuzin, V., Marcellino, M., and Schumacher, Ch. (2010). Pooling Versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP. Presented at the 6th Colloquium on Modern Tools for Business Cycle Analysis. Luxembourg, 26-29 September. Available at: http://epp.eurostat.ec.europa.eu/portal/page/portal/euroindicators_conferences/6th_colloquium/program_papers (accessed April 2013).
23.
Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Berlin: Springer-Verlag.
24.
Maravall, A. (1993). Stochastic Linear Trends, Models and Estimators. Journal of Econometrics, 56, 5-37.
25.
Mazzi, G.L. and Montana, G. (2009). A System of Rapid Estimates to Improve Real Time Monitoring of the Economic Situation: The Case of the Euro Area. Presented at the Seminar on Timeliness, methodology and comparability of rapid estimates of economic trends. Ottawa, 27-29 May. Available at: http://unstats.un.org/unsd/nationalaccount/workshops/2009/ottawa/ac188-2.asp (accessed April 2013).
26.
Mazzi, G.L., Mitchell, J., Mouratidis, K., and Weale, M. (2009). The Euro-Area Recession and Nowcasting GDP Growth Using Statistical Models. Presented at the International Seminar on Early Warning and Business Cycle Indicators. Scheveningen, 14-16
27.
December. Available at: http://unstats.un.org/unsd/nationalaccount/workshops/2009/netherlands/ac202-2.asp (accessed April 2013).
28.
Mustapha, N. and Djolov, G. (2010). The Development and Production of GDP Flash Estimates in a Newly Industrialised Country: The Case of South Africa. Presented at the 6th Colloquium on Modern Tools for Business Cycle Analysis. Luxembourg, 26-29 September. Available at: http://epp.eurostat.ec.europa.eu/portal/page/portal/euroindicators_conferences/6th_colloquium/program_papers (accessed April 2013).
29.
Newey, W.K. and West, K. (1987). A Simple Positive Semi-Definite Heteroscedasticity and Autocorrelation Consistent Covariance Matrix. Econometrica, 55, 703-708.
30.
Rünstler, G. and Sádillot, F. (2003). Short-Term Estimates of Euro Area Real GDP by Means of Monthly Data. European Central Bank Working Paper No. 276.
31.
Sánchez, I. and Peña, D. (2001). Properties of Predictors in Over differenced Nearly Nonstationary Autoregression. Journal of Time Series Analysis, 22, 45-66.
32.
Stock, J.H. and Watson, M.W. (2002). Macroeconomic Forecasting Using Diffusion Indices. Journal of Business and Economic Statistics, 20, 147-162.
33.
UNECE Secretariat (2009). Rapid Estimates of GDP in CIS and Western Balkan Countries. Presented at the Seminar on Timeliness, methodology and comparability of rapid estimates of economic trends. Ottawa, 27-29 May. Available at: http://unstats.un.org/unsd/nationalaccount/workshops/2009/ottawa/ac188-2.asp (accessed April 2013).
34.
Zadrozny, P.A. (1990). Estimating a Multivariate ARMA Model with Mixed-Frequency Data: An Application to Forecasting U.S. GNP at Monthly Intervals. Federal Reserve Bank of Atlanta Working Paper No. 90-6.
35.
Zheng, I.Y. and Rossiter, J. (2006). Using Monthly Indicators to Predict Quarterly GDP. Bank of Canada Working Paper 2006-26.