Predicting the Country Commodity Imports Using Mixed Frequency Data Sampling (MIDAS) Model


1 Faculty of Economics and Political Sciences, Shahid Beheshti University, Tehran, Iran

2 Faculty of Economics and Political Sciences, Shahid Beheshti University,Tehran,Iran.




redicting the amount of country imports toward assessing trade balance and its effect on the balance of payments (BOP) and finally money supply, general level of prices and the rate of economic growth is of paramount importance. Therefore, economic policymakers seriously need a model which cannot only predict the volume of imports well but also be capable of revising the initial prediction over time as soon as new data for the explanatory variables are available. To this purpose, mixed frequency data sampling model was used which allows time series variables with different annual, seasonal and even daily frequencies to be used in a single regression model. In estimating the model using the software R, annual real imports, real exports and quarterly of real GDP, real exchange rate and the volatilities of the real exchange rate in the range of 1988 to 2014 are used. Information related to 2014 is not used in preliminary estimation of relationship, so that the predictive power of the model outside of the estimated range can be tested. The proposed model predicts that real imports of goods as49948 million dollars for 2014 which is associated with an error of only41 million dollars, or about 8 percent, compared to its real amount achieved of49907 million dollars. The result suggests that the predictive power of the MIDAS model is very satisfactory.


Bahmani-Oskooee, M., Hengerty, S. W., & Zhang, R. )2014(. The Effects of Exchange Rate Volatility on Korean flows: Industry Level Estimates. Economic Paper, 33(1), 76-97.
Bayat, M., & Noferesti, M. (2015). Applied Econometric of Time Series: Mixed Frequency Data Sampling. Tehran: Noor-e-Elm Publication.
Derakhshan, M. (1995). Econometrics; Single Equations with Classical Assumptions. Tehran: Samt Publishing.
Ghysels, E., Kvedaras, V., & Zemlys, M. (2014). Mixed Frequency Data Sampling Regression Models: the Package Midas. Journal of Statistical Software, 10, 18-25.
Klein, L. R., & Sojo, E. (1989). Combinations of High and Low Frequency Data in Macroeconomic Models. Retrieved from
Noferest, M., & Dashtban, S. (2017). Effect of Changes in Age Structure of the Population on Government Tax Revenues and Predicting its Changes: An Approach of Mixed Frequency Data Sampling (MIDAS). Journal of Economic Research, 22(6), 59-75.
Noferesti, M., Varahrami, V., & Javaherdehi, S. (2017). Seasonal Variations and Prediction Revision of Annual Non-oil Export: A Mixed Data Sampling (MIDAS) Approach. Journal of Economic and Modeling, 8(29), 67-88.
Sayadi, F., & Moghadasi, R. (2015). The Effect of Energy Prices on the Price of Grain Using Regression Models with Complex Data. Research Journal of Applied Economics studies in Iran, 15, 149-160.
Sepanlou, H., & Ghanbari, A. (2010). The Factors Affecting the Demand for Imports in Terms of 223 Goods, Capital and Consumer. Journal of Business Research, 57, 209-210.
Souri, A. (2014). Econometrics; along with Eviews 8 Application & Stata 12. Tehran: Publishing Culturology.
Tsui, A. K., Xu, C. Y., & Zhang, Z. Y. (2013). Forecasting Singapore Economic Growth with Mixed-Frequency Data. 20th International Congress on Modeling and Simulation, Retrieved from