Evaluation of Mixed-Frequency Regressions in Forecasting Seasonal Inflation Rate of Iran

Document Type : Original Article

Authors

1 Department of Economics, Graduate School of Management and Economics, Sharif University of Technology

2 MA in Economics, Graduate School of Management and Economics, Sharif University of Technology

Abstract

This paper evaluates the predictive ability of mixed-frequency autoregressive models in forecasting seasonal inflation rate of IRAN economy. For this, forecasting accuracy of models with monthly lags of inflation rate are compared with a benchmark model which uses seasonal lags. Results indicate incorporating monthly lags, instead of seasonal lags, improves the accuracy of seasonal forecasts, especially for 1-step ahead forecasts. Among mixed-frequency models, MIDAS regressions are more accurate than the benchmark model in 1-step, 3-step and 4-step ahead forecasts. Step-weighting model which features proliferation of parameters outperforms the MIDAS regression which is non-linear and parameter efficient in estimation.

Keywords


-    Andreou, E., Ghysels, E., & Kourtellos, A. (2010). Regression Models with Mixed Sampling Frequencies. Journal of Econometrics, 158(2), 246-261.
-     Andreou, E., Ghysels, E., & Kourtellos, A. (2013). Should macroeconomic forecasters use daily financial data and how? Journal of Business & Economic Statistics, 31(2), 240-251.
-     Armesto, M. T., Engemann, K. M., & Owyang, M. T. (2010). Forecasting with Mixed Frequencies. Federal Reserve Bank of St. Louis Review, 92(6), 521-536.
-     Atrianfar, H., & Barakchian, S. M. (2014). Evaluation of the Performance of Combined Methods in Real-time Forecasting of Inflation in Iran. Money and Banking Research, 6(18), 23-57. (In Persian)
-     Barakchian, S. M., & Rezaei, M. (2015). Introduction and Performance Comparison of some Common Multi-period VaR Forecasting Methods: A Case Study of the Tehran Stock Exchange. Iranian Journal of Economic Research(60), 1-35. (In Persian)
-     Barakchian, S. M., Karami, H., & Bayat, S. (2014). Forecasting Inflation Rate of Iran Using Phillips Curve. MBRI Working Paper(93016). (In Persian)
-     Bayat, M., & Noferesti, M. (2015). Applied Time Series Econometrics: Mixed Frequency Data Sampling Model. Hamedan: Noor-e-Elm. (In Persian)
-     Bayat, S., & Barakchian, S. M. (2014). Inflation Forecasting Using Disaggregation of CPI Component. Journal of Monetary and Banking Research, 7(19), 44-59. (In Persian)
-     Ghysels, E., Rubia, A., & Valkanov, R. (2009). Multi-Period Forecasts of Volatility: Direct, Iterated, and Mixed-Data Approaches. SSRN Working Paper(1344742).
-     Ghysels, E., Santa-Clara, P., & Valkanov, R. (2005). There Is A Risk-Return Tradeoff After All. Journal of Financial Economics, 76, 509-548.
-     Ghysels, E., Santa-Clara, P., & Valkanov, R. (2006). Predicting volatility: getting the most out of return data sampled at different frequencies. Journal of Econometrics, 131, 59-95.
-     Ghysels, E., Sinko, A., & Valkanov, R. (2007). MIDAS Regressions: Further results and new directions. Econometric Reviews, 26(1), 53-90.
-     Harvey, D., Leybourne, S., & Newbold, P. (1997). Testing the equality of prediction mean squard errors. International Journal of forecasting, 13, 281-291.
-     Heidari, H. (2011). An Alternative VAR Model for Forecasting Iranian Inflation: An Application of Bewley Transformation. Iranian Journal of Economic Research, 16(46), 77-96. (In Persian)
-     Karami, H., & Barakchian, S. M. (2014). Evaluation of Autoregressive Models in Forecasting Inflation Rate of Iran. MBRI Working Paper(9212). (In Persian)
-     Molabahrami, A., Khodavaisi, H., & Hossaini, R. (2013). Forecasting Inflation based on Stochastic Differential Equations and Alternative Models (A Comparative Study). The Economic Research (Scientific Research Quarterly), 13(1), 25-46. (In Persian)
-     Moshiri, S. (2000-2001). Forecasting Iranian Inflation Rates Using Structural, Time Series, and Artificial Neural Networks Models. Tahghighat-e-eghtesadi(58), 147-184. (In Persian)
-     Nijman, T., & Palm, F. (1990). Parameter identification in ARMA processes in the presence of regular but incomplete sampling. Journal of Time Series Analysis, 11(3), 239-248.
-     Noferesti, M., & Bayat, M. (2015). Forecasting Iranian's Economic Growth Using Mixed Frequency Data Sampling Technique. Quarterly Journal of Economics and Modeling Shahid Beheshti University, 4(14 & 15). (In Persian)
-     Shahikitash, M., Molaee, S., & Hallajzadeh, Z. (2014). Forecasting Inflation and Price Index with Neural Network. Quarterly Journal of The Macro and Strategic Policies, 1(4), 51-67. (In Persian)
-     Silvstrini, A., & Veredas, D. (2008). Temporal aggregation of unive- riate and multivariate time series models: A survey. Journal of Economic Surveys, 22(3), 458-497.
-     Taiebnia, A., Amiri, H., & Ravishi, F. (2014). The New Keynesian Phillips Curve and Forecasting Inflation. Journal of Planning and Budgeting, 18(4), 3-26. (In Persian)
-     Zarra-Nezhad, M., & Hamid, S. (2009). Prediction of Inflation Rates in Iran Using Dynamic Artificial Neural Network (Time Series Approach). Quarterly Journal of Quantitative Economics, 6(1), 145-167. (In Persian)