Evaluating the Performance of the Financial Condition Index in Forecasting Iran's Macroeconomic Variables: Time-varying Parameter Patterns Approach

Document Type : Scientific paper

Authors

1 Ph.D. Student, Department of Economics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

2 Associate Professor, Department of Economics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran,

3 Assistant Professor, Department of Economics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

Abstract

Today, the financial condition index is introduced as a tool for forecasting macroeconomic variables in many countries of the world. The aims of the present study are, first, to construct the financial condition index in different patterns of time-varying parameters and then, to analyze the performance of the financial conditions index in order to forecast the macroeconomic variables using the mean squared forecasting error approach (MSFE) and the cumulative sum of log-predictive likelihoods.
The results show that the constructed financial conditions index can adapt to different states of Iran's economy and its fluctuations increase over time. Based on the obtained results, the time-varying parameter factor-augmented vector autoregressive model (TVP-FAVAR) shows less forecast error in some macroeconomic variables compared to the factor-augmented vector autoregressive model (FAVAR) and also the vector autoregressive time-varying parameter factor-augmented model (FA-TVP-VAR). The use of combined time-varying parameter factor-augmented vector autoregressive models when the coefficients and selected variables of the financial conditions index change depending on the economic condition, improve the performance of the financial conditions index in forecasting macroeconomic variables. The cumulative sum of log-predictive likelihoods shows that using variable parameter patterns does not have a great effect in reducing the forecast error of some macro variables.

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References

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Macroeconomics Research Letter
Volume 16, Issue 32
December 2021
Pages 271-299

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History

  • Receive Date: 13 December 2022
  • Revise Date: 03 March 2023
  • Accept Date: 05 March 2023

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