• Ray John Gabriel FRANCO School of Statistics, University of the Philippines Diliman, Philippines
  • Dennis S. MAPA School of Statistics, University of the Philippines Diliman, Philippines


Frequency mismatch has been a problem in time series econometrics. Many monthly economic and financial indicators are normally aggregated to match quarterly macroeconomic series such as Gross Domestic Product when performing econometric analysis. However, temporal aggregation, although widely accepted, is prone to information loss. To address this issue, mixed frequency modelling is employed by using state space models with time-varying parameters. Quarter-on-quarter growth rate of GDP estimates are treated as monthly series with missing observation. Using Kalman filter algorithm, state space models are estimated with eleven monthly economic indicators as explanatory variables. A one-step-ahead forecast for GDP growth rates is generated and as more indicators are included in the model, the predicted values became closer to the actual data. Further evaluation revealed that among the group competing models, using Consumer Price Index (CPI), growth rates of Philippine Stock Exchange Index (PSEi), Exchange Rate, Real Money Supply, Wholesale Price Index (WPI) and Merchandise Exports are the more important determinants of GDP growth and generated the most desirable forecasts (lower forecast errors).


[1] Armesto, M., Engemann, K., Owyang, M. (2010). Forecasting with Mixed Frequencies. Federal Reserve Bank of St. Louis Review, 92(6): 521-36.
[2] Aruoba, S., Diebold, F., Scotti, C. (2008). Real-Time Measurement of Business Conditions. Journal of Business and Economic Statistics, 27(4): 417-27.
[3] Asimakopoulos, S.,et al. (2013). Forecasting Fiscal Time Series Using Mixed Frequency Data. Working Paper Series No. 1550, European Central Bank.
[4] Camacho, M., Perez-Quiros, G. (2008). Introducing the Euro-sting: short-term indicator of Euro area growth. Journal of Applied Econometrics, 25(4): 663-694.
[5] Clements, M., Galvão, A. (2008). Macroeconomic forecasting with mixed-frequency data. Forecasting output growth in the United States, Journal of Business & Economic Statistics, 26(4): 546-54.
[6] Fernández, R. (1981). A Methodological note on the estimation of time series.The Review of Economics and Statistics, 63(3): 471-476.
[7] Fulton, J., Bitmead, R., Williamson, R. (2001). Smoothing Approaches to Reconstruction of Missing Data in Array Processing, in Defence Applications of Signal Processing. Proceedings of the US/Australia Joint Workshop on Defence Applications of Signal Processing. New York: Elsevier.
[8] Ghysels, E., Santa-Clara, P., Valkanov, R. (2004). The MIDAS touch: Mixed Data Sampling Regression Models. Working paper,
[9] Giannone D., Reichlin, L., Small, D., Nowcasting (2008). The Real-Time Informational Content of Macroeconomic Data. Journal of Monetary Economics, 55(4): 665-76.
[10] Götz, T., Hecq, A. (2013). Nowcasting causality in mixed frequency Vector Autoregressive models. Maastricht University School of Business and Economics, the Netherlands.
[11] Kuzin, V., Marcellino, M., Schumacher, C. (2009). MIDAS versus Mixed-Frequency VAR: Nowcasting GDP in the Euro Area. Discussion Paper No. 07/2009, Deutsche Bundesbank, 2009;
[12] Mittnik, S and Zadrozny, P. (2003). Forecasting Quarterly German GDP at Monthly Intervals Using Monthly IFO Business Condition Data. CESIFO Working Paper No. 1203,
[13] Qian, H. (2010). Vector autoregression with varied frequency data. Iowa State University.
[14] Shiskin, J., et al. (1967). The X-11 variant of the Census method II seasonal adjustment program. Economic Research and Analysis Division, Bureau of the census.
[15] Stock, J., Watson, M. (2010). Dynamic Factor Models. Oxford Handbook of Economic Forecasting, Oxford University Press.
[16] Tay, A. (2006). Mixing Frequencies: Stock Returns as a Predictor of Real Output Growth. Working Paper No. 34-2006, Singapore Management University, Economics and Statistics Working Paper Series, December 2006,
How to Cite
FRANCO, Ray John Gabriel; MAPA, Dennis S.. ANALYZING THE DYNAMICS OF GROSS DOMESTIC PRODUCT GROWTH. A MIXED FREQUENCY MODEL APPROACH. Theoretical and Practical Research in the Economic Fields, [S.l.], v. 5, n. 2, p. 117-141, june 2017. ISSN 2068-7710. Available at: <>. Date accessed: 23 jan. 2022. doi: