Predicting the Effects of New Sanctions and Evaluating Fiscal Policies in the Context of a Macroeconomic Model with Mixed-Frequency Data Sampling for the Iranian Economy Under Sanctions

Noferesti, Mohamad; Sezavar, Mohamadreza

doi:10.52547/jemr.12.44.85

Volume 12, Issue 44 (7-2021) jemr 2021, 12(44): 85-104 | Back to browse issues page

‎ 10.52547/jemr.12.44.85

‎ 20.1001.1.22286454.1400.12.44.3.0

Mendeley

Zotero

RefWorks

Noferesti M, Sezavar M. Predicting the Effects of New Sanctions and Evaluating Fiscal Policies in the Context of a Macroeconomic Model with Mixed-Frequency Data Sampling for the Iranian Economy Under Sanctions. jemr 2021; 12 (44) :85-104
URL: http://jemr.khu.ac.ir/article-1-2150-en.html

Predicting the Effects of New Sanctions and Evaluating Fiscal Policies in the Context of a Macroeconomic Model with Mixed-Frequency Data Sampling for the Iranian Economy Under Sanctions

Mohamad Noferesti¹

, Mohamadreza Sezavar ^*

1- Shahid Beheshti University
2- Shahid Beheshti University , m_sezavar@sbu.ac.ir

Abstract: (2914 Views)

In the Iranian economy, which has experienced various sanctions, it was necessary to anticipate macroeconomic variables when imposing new sanctions. On the other hand, in the context of sanctions, it is possible to make a more accurate assessment of economic policies in order to be able to respond in a timely manner to these shocks and the need for appropriate planning and security against them. Therefore, in the present study, a macroeconomic model with Mixed-frequency data sampling has been used,While having a high accuracy in prediction, it is possible that when new information about multivariate variables is obtained, based on it, the previous prediction for the dependent variable of the pattern is revised. The model consists of 27 behavioral equations, 8 communication equations and 33 definitional and union relations and the parameters of the model are estimated using time series data in the period 1338 to 1396. Predictive results show that the use of new observations in high frequency variables in the model has led to improved accuracy in predicting the endogenous variables of the model.

Keywords: Midas Macroeconometrics Model, Sanctions, Forecasting, Fiscal Policy

Full-Text [PDF 1415 kb] (929 Downloads)

Type of Study: Applicable | Subject: سایر
Received: 2021/02/21 | Accepted: 2021/11/16 | Published: 2022/01/25

References

1. Andreou, E., Ghysels, E., & Kourtellos, A. (2010). Regression Models with Mixed Sampling Frequencies. Journal of Econometrics, 158(2), 246-261. [DOI:10.1016/j.jeconom.2010.01.004]

2. Andreou, E., Ghysels, E., and Kourtellos, A. (2013). Should macroeconomic forecasters use daily financial data and how? Journal of Business and Economic Statistics, 31(2):240-251. [DOI:10.1080/07350015.2013.767199]

3. Andreou, E., Ghysels, E., Kourtellos, A.(2011). Forecasting with mixed-frequency data. In: Clements, M., Hendry, D. (Eds.), Oxford Handbook of Economic Forecasting. Oxford University Press, Oxford, pp. 225-245 [DOI:10.1093/oxfordhb/9780195398649.013.0009]

4. Armesto, M. (2010); Forecasting with mixed frequencies, Federal Reserve Bank of Saint Louis 92, 521-536. [DOI:10.20955/r.92.521-36]

5. Breitung, J., Roling, C.(2015). Forecasting inflation rates using daily data: a nonparametric MIDAS approach. J. Forecast. 34 (7), 588-603. [DOI:10.1002/for.2361]

6. Chen, X., and E. Ghysels. (2011). News-good or bad-and its impact on predicting future volatility. Review of Financial Studies 24, 1, 46-81 [DOI:10.1093/rfs/hhq071]

7. Clements, M. P., & Galvão, A. B. (2008). Macroeconomic Forecasting with Mixed-Frequency Data: Forecasting Output Growth in the United States. Journal of Business & Economic Statistics, 26(4), 546-554. [DOI:10.1198/073500108000000015]

8. Clements, M. P., A. B. Galvão, and J. H. Kim. (2008). Quantile forecasts of daily exchange rate returns from forecasts of realized volatility. Journal of Empirical Finance 15:729-50. [DOI:10.1016/j.jempfin.2007.12.001]

9. Dargahi, Hassan (2016) Designing a Macroeconomic Model for Macroeconomic Foresight, Institute for management and planning studies(In Persian)

10. Foroni, C., & Marcellino, M. (2014). Mixed-Frequency Structural Models: Identification, Estimation, and Policy Analysis. Journal of Applied Econometrics, 29(7), 1118-1144. [DOI:10.1002/jae.2396]

11. Ghyseles, E., Sinko, A., & Valkano R. (2006) "MIDAS regressions: Further results and new directions". econometric Reviews, 2007, 26 [DOI:10.2139/ssrn.885683]

12. Ghysels, E. (2016). Macroeconomics and the Reality of Mixed Frequency Data. Journal of Econometrics, 193(2), 294-314. [DOI:10.1016/j.jeconom.2016.04.008]

13. Ghysels, E. , Santa-Clara, & Valkano R. (2004). The MIDAS Touch :Mixed Frequency Data Sampling Regressions. Manuscript, University of North Carolina and UCLA

14. Ghysels, E.(2016). Macroeconomics and the reality of mixed frequency data. J. Econ. 193 (2), 294-314. [DOI:10.1016/j.jeconom.2016.04.008]

15. Ghysels, E., & Wright, J. H. (2009). Forecasting Professional Forecasters. Journal of Business & Economic Statistics, 27(4), 504-516. [DOI:10.1198/jbes.2009.06044]

16. Ghysels, E., Marcellino, M. (2018). Applied Economic Forecasting Using Time Series Methods. Oxford University Press, Oxford, New York.

17. Ghysels, E., Qian, H., (2019). Estimating MIDAS regressions via OLS with polynomial parameter profiling. Economet. Stat. 9, 1-16. [DOI:10.1016/j.ecosta.2018.02.001]

18. Gotz, T. B., Hecq, A., and Urbain, J.-P. (2014). Forecasting mixed-frequency time series with ecm-midas models. Journal of Forecasting, 33(3):198-213. [DOI:10.1002/for.2286]

19. Kenneth Katzman(2020)Iran Sanctions,Congressional Research Service, RS20871 VERSION 307 · UPDATED

20. Klein, L.R., Sojo, E. (1989) Combinations of High and Low Frequency Data in Macroeconomic Models. in L.R. Klein and J. Marquez (EDS), Economics in Theory and practice: An Eclectic & Approach. Kluwer Academic Publishers, pp.3- 14 [DOI:10.1007/978-94-009-0463-7_1]

21. Makian, Seyed Nizamuddin, Tavaklian, Hossein and Seyed Mohammad Saleh Najafi Farashah (2019) Investigating the effect of direct tax shock on GDP and inflation in Iran in the framework of a stochastic dynamic general equilibrium model, Financial Economics Quarterly, Volume 13, Number 94, pp. 45-1(In Persian)

22. Noferesti, Mohammad (2019) Macroeconomic Modeling in Iran, Volume One, Shahid Beheshti University, Printing and Publishing Center, First Edition(In Persian)

23. Noferesti, Mohammad (2019) Macroeconomic Modeling in Iran, Volume two, Shahid Beheshti University, Printing and Publishing Center, First Edition(In Persian)

24. Noferesti, Mohammad and Mohammad Reza Sezavar (2020) Constructing of a monthly index for sanctions against Iran, Quarterly Journal of Economic Strategy, Year 10, Issue 3, Series 38, pp. 565-593(In Persian)

25. Seong,Byeongchan (2020), Smoothing and forecasting mixed-frequency time series with vector exponential smoothing models, Economic Modelling, 91:463-468 [DOI:10.1016/j.econmod.2020.06.020]