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


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).

Author Biographies

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


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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, oct. 2016. ISSN 2068-7710. Available at: <>. Date accessed: 23 jan. 2022.
Theoretical and Practical Research in the Economic Fields


Multi-frequency models, state space model, Kalman filter, GDP forecast