The Dynamics of Inflation and GDP Growth: A Mixed Frequency Model Approach

Franco, Ray John Gabriel and Mapa, Dennis S. (2014): The Dynamics of Inflation and GDP Growth: A Mixed Frequency Model Approach.

Abstract

Frequency mismatch has been a problem in econometrics for quite some time. Many monthly economic and financial indicators are normally aggregated to match quarterly macroeconomic series such as GDP when analysed in a statistical model. However, temporal aggregation, although widely accepted, is prone to information loss. To address this issue, mixed frequency modelling was employed by using state space models with time-varying parameters. Quarter-on-quarter growth rate of GDP estimates were first treated as a monthly series with missing observation. Using Kalman filter algorithm, state space models were estimated with eleven monthly economic indicators as exogenous variables. A one-step-ahead predicted value for GDP growth rates was generated and as more indicators were included in the equation, the predicted values came closer to the actual data. Further evaluation revealed that among the group competing models, using Consumer Price Index (CPI), growth rates of PSEi, exchange rate, real money supply, WPI and merchandise exports are the more important determinants of GDP growth and generated the most desirable forecasts (lower forecast errors).

• Item Type: MPRA Paper
• Language: English
• Keywords: Multi-frequency models, state space model, Kalman filter, GDP forecast
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications

References

Armesto, M., Engemann, K. & Owyang, M. (2010). Forecasting with Mixed Frequencies. Federal Reserve Bank of St. Louis Review, November/December 2010, 92(6), pp. 521-36.

Aruoba, S., Diebold, F. and Scotti, C. (2008). Real-Time Measurement of Business Conditions. Journal of Business and Economic Statistics, 27(4), pp. 417-27.

Asimakopoulos, S. et. al. (2013). Forecasting Fiscal Time Series Using Mixed Frequency Data. Working Paper Series No. 1550, European Central Bank.

Camacho, M. and Perez-Quiros, G. (2008). Introducing the Euro-Sting: Short-Term Indicator of Euro Area Growth. Journal of Applied Econometrics, 25(4), pp. 663-94.

Clements, M., Galvão, A. (2008). Macroeconomic Forecasting With Mixed-Frequency Data: Forecasting Output Growth in the United States. Journal of Business & Economic Statistics. October 2008, Vol. 26, No. 4. pp. 546-54.

Fernández, R. (August 1981). A Methodological Note on the Estimation of Time Series. The Review of Economics and Statistics, Vol. 63, No. 3, pp. 471-476.

Fulton, J., Bitmead, R. and 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.

Ghysels, E., Santa-Clara, P. and Valkanov, R. (June 2004). “The MIDAS Touch: Mixed Data Sampling Regression Models.” Working paper, <http://docentes.fe.unl.pt/~psc/MIDAS.pdf>.

Giannone D., Reichlin L. and Small, D. Nowcasting. (May 2008). The Real-Time Informational Content of Macroeconomic Data. Journal of Monetary Economics, Vol. 55(4), pp. 665-76.

Götz, T. and Hecq, A. (2013). Nowcasting causality in mixed frequency Vector Autoregressive models. Maastricht University School of Business and Economics, the Netherlands.

Kuzin, V., Marcellino, M. and Schumacher, C. (2009). MIDAS versus Mixed-Frequency VAR: Nowcasting GDP in the Euro Area. Discussion Paper No. 07/2009, Deutsche Bundesbank, 2009; <www.econstor.eu/bitstream/10419/27661/1/200907dkp.pdf.>

Mittnik, S and Zadrozny, P. (2003). Forecasting Quarterly German GDP at Monthly Intervals Using Monthly IFO Business Condition Data. CESIFO Working Paper No. 1203. <www.SSRN.com>

Qian, H. (2010). Vector autoregression with varied frequency data. Iowa State University.

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.

Stock, J and Watson, M. (2010). Dynamic Factor Models. Oxford Handbook of Economic Forecasting, Oxford University Press.

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; <https://mercury.smu.edu.sg/rsrchpubupload/7469/Tay_2006.pdf>.