International Journal of Nonlinear Analysis and Applications

Fluctuations in macroeconomic variables on the funding of pension funds in Iran (combined data pattern method with different frequency (MIDAS))

Document Type : Research Paper

Authors

1 Department of Economics, Central Tehran Branch, Islamic Azad University, Tehran, Iran

2 Department of Economics, Allameh University, Tehran, Iran

3 Department of Environmental Engineering, Parand Branch, Islamic Azad University, Parand, Iran

Abstract

The current paper investigates the fluctuations of macroeconomic variables on the resilience of pension funds in Iran. Thus, using the combined data model method with different frequencies (Midas), the present issue was investigated for different seasonal and annual time periods. In the forecasted model, the annual data of government debt, unemployment, and total Social Security expenditures and quarterly data of oil price fluctuations, exchange rate jumps, money supply and consumer price index for the years 1991-2016 have been used. Data related to 2019 were not used in the initial estimation of the relationship in order to test the predictive power of the model outside the estimation range. Based on the results; inflation rate, money supply, exchange rate jumps and crude oil price fluctuations have a statistically significant effect on the Social Security expenditures. In other words, in terms of economic structure and according to the principles of economics, a steady increase in the exchange rate causes economic prosperity in society, but if this increase is temporary, economic prosperity can not be observed. A floating and managed exchange rate increase will cause relative stability in the foreign exchange market, which in turn will increase the total Social Security expenditures in the long run. Exchange rate fluctuations lead to an increase in debt by companies and the government, and an increase in debt will lead to a general lack of liquidity, which in general, the lack of liquidity of enterprises has a negative effect on the situation of the Social Security and increase the total Social Security expenditures. Also, by comparing the predicted values with the realized values, the prediction accuracy of the model is higher and is closer to the real values.

Keywords

[1] W. Anders, Time Series Econometrics with A Practical Approach, Translator: M. Sadeghi and S. Shavalpour, Imam Sadegh University Press, Tehran, 2012.
[2] R.F. Engle, Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica 50 (1982), no. 4, 987–1007.
[3] E. Ghysels, Macroeconomics and the reality of mixed-frequency data, J. Econometrics 193 (2016), no. 2, 294–314.
[4] E. Ghysels, P. Santa-Clara and R. Valkanov, Predicting volatility: getting the most out of return data sampled at different frequencies, J. Econometrics 131 (2006), no. 1-2, 59–95.
[5] E. Ghysels, A. Sinko and R. Valkanov, MIDAS regressions: Further results and new directions, Econ. Rev. 26 (2007), no. 1, 53–90.
[6] D.N. Gujarati, Basic Econometrics, McGraw Hill, 1999.
[7] H. He, L. Ning and D. Zhu, The impact of rapid aging and pension reform on savings and the labor supply, IMF Working Papers 19 (2019), no. 61, 1.
[8] H. Izadbakhsh, A. Soleymanzadeh, H. Davari Ardakani and M. Zarinbal, A systematic approach for asset liability management of pension funds in a fuzzy environment, J. Econ. Model. Res. 8 (2017), no. 29, 201–239.
[9] H. Jafari, Macroeconomic impacts of structural reforms in the Iranian pension system, Econ. Model. Quart. 12 (2018), no. 4.
[10] L.R. Klein and E. Sojo, Combinations of high and low frequency data in macroeconomic models, In Economics in theory and practice: An eclectic approach, Springer, Dordrecht, (1989) 3–16.
[11] S.A. Mousavian Anaraki, M. Tamassoki Bidgoli and F. Rahimi, The impact of macroeconomic variables on pension funds and proper asset management strategy, The Second Int. Conf. Innov. Bus. Econ. Manag. 2020.
[12] M. Noferesti and M. Bayat, Applied Econometric of Time Series of Synthetic Data Patterns with Different Frequency, Noore-e-Elm, 2016.
[13] C. Sims, Macroeconomics and reality, Econometrica 48 (1980), 1–49.
[14] A. Suri, Econometrics (Volume 2) with The Use of Eviews and Stata, Cultural Studies Publishing, 2014.
[15] F.D. Vinuya, Testing for market integration and the law of one price in world shrimp markets, Aquacul. Econ. Manag. 11 (2007), no. 3, 243–265.
International Journal of Nonlinear Analysis and Applications
Volume 13, Issue 2
July 2022
Pages 31-43
  • Receive Date: 21 April 2021
  • Revise Date: 21 May 2021
  • Accept Date: 21 June 2021