Forecasting Iran's Economy Inflation with DSGE-VAR Model (Theory and Technique)

Document Type : Research Paper

Authors

1 Ph.D. Candidate in Economics, Allameh Tabataba'i University

2 Assistant Professor of Economics, Allameh Tabataba'i University

Abstract

Inflation is one of the key macroeconomic variables that its precise forecasting is the goal of policy makers and in particular the central bank.VAR model has a long history as a tool for forecasting and policy analysis; but the problem with this method is that it uses a little theoretical information about the relationships between variables. In addition, in VAR models, many parameters need to be estimated, some of them may be meaningless. Following this, the idea of hybrid models was introduced. One of the hybrid models is the DSGE-VAR model. This model combines DSGE, which is a structural model and more reliant on theory, with a VAR that provides better fitting data. In this study at the beginning, the theoretical structure and results of the estimation of the DSGE-VAR model for Iran's Economic Data are presented. Further, the forecasting of this method are compared with other models such as unrestricted VAR and Minnesota VAR. All three models are estimated recursively from the period 1991:1 to 2012:4 and then used to forecast inflation for one to eight-quarters-ahead over an out-of sample from 2011:1 to 2015:4. Comparison of the accuracy of forecasting of the above methods using the RMSE index shows a better performance of the DSGE-VAR approach compared to other models.

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  1. Adolfson, M., Lassen, S., Linde, J., & Villani, M. (2007). Bayesian estimation of an open economy DSGE model with incomplete pass-through. Journal of International Economics, 72, 481–511.
  2. Afshari, Z., & Bayat, M.  )2014(.  Comparison of the predictive power of the hybrid Phillips curve and the ARIMA model of inflation. Journal of economics, 8 (26), 1-12 (In Persian).
  3. Altug, S. )1989(. Time to build and aggregate fluctuations: Some new evidence. International Economic Review, 30 (4), 889-920.
  4. Bayat, S., & Karami, H. )2013(. History of economic variables. Monetary and Banking Institute, Central Bank of the Islamic Republic of Iran (In Persian).
  5. Bekiros, S., & Paccagnini, A. )2015(. Estimating point and density forecasts for the US economy with a factor-augmented vector autoregressive DSGE model. Studies in Nonlinear Dynamics & Econometrics, 19, 107-136.
  6. Bekiros, S., & Paccagnini, A. )2014(. Forecasting the US economy with a factor-augmented vector autoregressive DSGE model. Business School, Working paper.
  7. Bekiros, S., & Paccagnini, A. )2014(. Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models. Computational Statistics and Data Analysis, 71, 298–323.
  8. Consolo, A., Favero, C., & Paccagnini, A. )2009(. On the statistical identication of DSGE models. Journal of Econometrics, 150, 99-115.
  9. Del Negro, M., & Schorfheide, F. )2004(. Priors from general equilibrium models for VARS. International Economic Review, 45(2), 643–673.
  10. Del Negro, M., & Schorfheide, F. )2003(. Take your model bowling: Forecasting with general equilibrium models. Economic Review, Federal Reserve Bank of Atlanta.
  11. 11.   Fair, R.C. )1984(. Specification, estimation and analysis of macroeconometric models. Cambridge, Mass: Harvard University Press.
  12. Golestani, Sh.,  Gregini, M., & Haj Abbasi, F. )2012(. Comparison of the ability to predict VAR, ARIMA and neural network models: Global oil demand. Journal of environmental and natural resource economics, (4), 145-168 (In Persian).
  13. Gupta, R., & Steinbach, R. )2013(. A DSGE-VAR for forecasting key South African macroeconomic variables. Economic Modeling, 33, 19-33.
  14. Heidari, H., & Johari Salmasi, P. )2015(. Performance of different models of bayesian vector regression for estimation of Iran's macroeconomic variables: Application of Gibbs sampling. Journal of Economics Research, (62), 57-79 (In Persian).
  15. Hodge, A., Robinson, T., & Stuart, R. )2008(. A small BVAR-DSGE for forecasting the Australian economy. Reserve Bank of Australia, Research Discussion Paper 2008/04.
  16. Ireland, P. )2004(. A method for taking models to the data. Journal of Economic Dynamics and Control, 28, 1205-1226.
  17. Ireland, P. )1997(. A small, structural, quarterly model for monetary policy evaluation. Carnegie-Rochester Conference Series on Public Policy, 47, 83–108.
  18.  Khiabani, N., & Amiri, H. )2014(. The position of monetary and fiscal policies with emphasizing on oil sector with DSGE models (the case of Iran). Journal of Economics Research, 14(54), 133-173 (In Persian)
  19.  Klein, L.R., & Goldberger, A.S. )1955(. An econometric model of the United States: 1929-1953. Amsterdam: North-Holland.
  20. Komijani, A., & Tavakolian, H. )2012(. Monetary policy under fiscal dominance and implicit inflation target in Iran: A DSGE approach. Journal of Economic Modeling Research, 2(8), 87-117 (In Persian).
  21. 21.   Leeper, E., & Sims, C. )1994(. Toward a modern macroeconomic model usable for policy analysis, NBER macroeconomics annual. Cambridge and London: MIT Press.
  22. Lees, K., Matheson, T.  & Smith, C. )2011(. Open economy forecasting with a DSGE-VAR: Head to head with the RBNZ published forecasts. International Journal of Forecasting, 27, 512-528.
  23. Lucas, R.E. )1976(. Econometric policy evaluation: A critique. Amsterdam: North-Holand.
  24. Paccagnini, A. )2011(. DSGE models evaluation and hybrid models: A comparison. Working paper, European University Institute, Florence.
  25. Pop, R. )2016(. A small-scale DSGE-VAR model for the Romanian economy. Economic Modeling, 67, 1-9.
  26. Salehi Sarbiyan, M. )2016(. Modeling and predicting of Iran’s economic growth using ANFIS, markov switching and ARIMA models. Quarterly Journal of Economic growth and development research, 6(24), 51-64 (In Persian).
  27. Sargent, T. )1989(. Two models of measurements and the investment accelerator. Journal of Political Economy, 97 (2), 251-287.
  28. Schorfheide, F. )2000(. Loss function-based evaluation of DSGE models. Journal of Applied Econometric, 15 , 645–670.
  29. Sims, C.A. )1980(. Macroeconomics and reality. Econometrica, 1-48.
  30. Slutsky, E.) 1927(. The summation of  random causes as the source of cyclic processes. Econometrica, 105-146.
  31. Smets, F., & Wouters, R. )2003(. An estimated stochastic dynamic general equilibrium model of the Euro Area. Journal of the European Economic Association, 1, 1123-1175.
  32. Taylor, J. )1993(. Macroeconomic policy in a world economy: Form econometric design to practical operation. New York: North.
  33. Yule, G.U. )1921(. On the time-correlation problem, with special reference to the variate-difference correlation method. Journal of the Royal Statistical Society, 84, 497–526.
  34. Yule, G.U. )1926(. Why do we sometimes get nonsense correlations between time series? A study in sampling and the nature of time series. Journal of the Royal Statistical Society, 89, 1–64.
  35. Yule, G.U. )1927(. On a method of investigating periodicities in disturbed series with special reference to Wolfer’s sunspot numbers. Philosophical Transactions of the Royal Society of London, 226, 98–267.
  36. Central Bank of Iran (www.cbi.ir).