Developing Composite Leading Indicators to Forecast Industrial Business Cycles in Iran

Nasiri, H.; Taghizadeh, K.; Amiri, B.; Shaghaghi Shahri, V.

doi:10.22105/riej.2017.48026

Document Type : Research Paper

Authors

¹ Department of Economic Science, Boushehr Branch, Islamic Azad University, Boushehr, Iran

² Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

³ Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran

⁴ Department of Economics, University of Economic Sciences, Tehran, Iran

https://doi.org/10.22105/riej.2017.48026

Abstract

Economic cycles are referred as repeatable movement of economic indicators with different domain and duration. Detection of these cycles may help in forecasting the contraction period or expansions of important parts of the economy specifically industry. In this regard, many economist and researchers have focused on composite leading indicators which are growing in terms of diversity of econometric methods. This paper studies 13 macro economical time series in order to develop the best composite indicator, reflecting business cycles of Iran’s industry. Number of established licenses, production index of large industrial units, producer price index and import value index are the chosen variables that make the proposed composite leading indicator. The result of comparison between composite leading indicator and value added time series in the studied period (1997 up to 2012) showed a good correlation between the fluctuations of composite indicators and value added of industry. The result also showed that the proposed leading indicator has the ability to predict the business cycle for maximum 4 and minimum 1 period ahead and in average the forecasted period is equal to 3.2 seasons.

Keywords

Main Subjects

Forecasting, production planning, and control

References

[1] Burns, A. F. and Mitchell, W. C. (1946). Measuring Business Cycles. National Bareaue of Economic Research, New York.

[2] Koopmans, T. (1947). Measurement Without Theory. Review of Economics and Statistics, Vol. 29, No. 1, pp. 161-179.

[3] Valentine, L. M. (1987). Business Cycles and Forcasting. South-Western Publishing co., Cincinati, Ohaio.

[4] Zarnowitz, V. and Boschan, C. (1975). Cyclical indicators: an evaluation and new leading indexes. Business Condition Digest, pp. 185-198.

[5] Geweke, J. (1977). The dynamics factor analysis of economics time series. In: Aigner,

D. J. and Goldberger, A. S. (Eds), Latent Variables in Socio-Economic Models. North-Holland, Amsterdam, Chapter 19.

[6] Sargent, T. J. and Sims, C. A. (1977). Business cycle modeling without pretending to have too much a priori economic theory. In: Sims, C. et al. (Eds.), New Methods in Business Cycle Research. Federal Reserve Bank of Minneapolis.

[7] Stock, J. H. and Watson, M. W. (1989). New Indexes of coincident and leading economic indicators. In: Blanchard, O. and Fischer, S. (Eds.), NBER Macroeconomics Anuual. MIT Press, Cambridge.

[8] Proietti, T. and Moauro, F. (2004). Dynamic factor analysis with nonlinear temporal aggregation constraints. Econometrics 0401003, Economics Working Paper Archive at WUSTL.

[9] Carriero, A. and Marcellino, M. (2005). Building composite coincident indicators for European countries. Mimeo, Bocconi University.

[10] Sims, C. A. (1989). Comment on Stock and Watson (1989). In: Blanchard, O.; Fischer, S.; (Eds), NBER Macroeconomics Annual. MIT Press, Cambridge, MA, pp. 395-397.

[11] Mitchell, W. C. and Burns, A. F. (1938). Statistical indicators of cyclical revivals. National Bureau of Economical Research, New York.

[12] Bandura, R. (2008). A Survey of Composite Indices Measuring Country Performance: Update. United Nations Development Programme-Office of Development Studies.

[13] Saltelli, A. (2007). Composite indicators between analysis and advocacy. Social Indicators Research, Vol. 81, pp. 65-77.

[14] Schumacher, C. and Breitung, J. (2008). Real time forcasting of german GDP based on a large factor model with mountly and quarterly data. International Journal of Forcasting, Vol. 24, No. 10, pp. 386-398.

[15] Dueker, M. J. (2005). Dynamic forcasts of qualitative variables: A Qual VAR model of US recessions. Journal of Business and Economic Statistics, Vol. 23, No. 1, pp. 96- 104.

[16] Stock, J. H. and Watson, M. W. (2003). Forcastingoutput and inflation: the role of asset prices. Journal of Economic Literature, Vol. 41, No. 3, pp. 788-829.

[17] Filardo, A. J. (2002). The 2001 US recession: what did recession prediction models tell us?. Paper prepared for a book honoring Geoffrey H. Moore, Bank of International Settlements.

[18] Drechsel, k. and Maurin, L. (2011). Flow on conjunctural information and forecast of euro area economic activity. Journal of Forecasting, Vol. 30, No. 3, pp. 336-354.

[19] Gertler, M. and Lown, C. S. (2000). The information in the high yield bond spread for the business cycle: Evidence and some implications. NBER Working Paper Series No. 7549.

[20] Hamilton, J, D. and Kim, D. H. (2002). A re-examination of the predictability of economic activity using the yield speard. Journal of Money, Credit and Banking, Vol. 34, No. 2, pp. 340-360.

[21] Layton, A. P. and Katsuura, M. (2001). Comparison of regime switching, probit and logit models in dating and forcating US business cycles. International Journal of Forcasting, Vol. 17, No. 5, pp. 403-417.

[22] Granger, C. W. J. and Machina, M. J. (2006). Forcasting and decision theory. In: Elliotth, G., Granger, C. W. J. and Timmermann, A. (Eds.), The Handbook of Economic Forcasting. Elsevier, Amsterdam. pp. 81-98.

[23] Banerjee, A. and Marcellino, M. (2006). Are there any reliable leading indicators for US Inflation and GDP growth?. International Journal of Forcasting, Vol. 17, No. 3, pp. 137-151.

[24] Jalali Naeini, S. (1996). Analyzing business cycles of Iran.Institute for Research in Planning and Development of Iran. Tehran, Iran.

[25] Nili, M. and Dargahi, H. (1998). Analyzing the recession of Iran economy based on business cycles and its solution. 8^thAnnual Conference on Monetary and Exchange rate Policy, Iran.

[26] Bastanzadeh, H. and Moghadam Zanjani, M., V. (1999). Business cycles of Iran economy. Ravand, No. 36 and 37.

[27] Sohrabzadeh, J. (2000). Business cycles of Iran. Master thesis, Faculty Of Economics, University Of Tehran.

[28] Khatayi, M. and Danesh Jafari, (2001). Business cycles of Iran. Iranian Journal Of Trade Studies, Vol. 5, No. 18, pp. 1-28.

[29] Tabatabei Yazdi, R. (2001). Forecasting through strategic indicators. Rahbord, No. 19, pp. 238-344.

[30] Tabatabei Yazdi, R. (2001). Forecasting through strategic indicators, application of leading indicators. Rahbord, No. 20, pp. 344-352.

[31] Tabatabei Yazdi, R. and Malek, F. (2002). Forecasting of Iran economy in 2002 through strategic indicators, application of leading indicators. Rahbord, No. 22, pp. 343-352.

[32] Tabatabei Yazdi, R. and Malek, F. (2003). Iran economy in 2003 through leading indicators, application of leading indicators. Rahbord, No. 29, pp. 111-120.

[33] Tabatabei Yazdi, R. and Malek, F. (2003). Iran economy in 2004. Rahbord, No. 32, pp. 223-234.

[34] Tabatabei Yazdi, R. and Malek, F. (2005). Iran economy in 2005. Rahbord, No. 35, pp. 183-202.

[35] Rafal, W. and Michal, Z. (2015). A note on using the Hodrick–Prescott filter in electricity markets. Energy Economics, Vol. 48, pp. 1-6.

International Journal of Research in Industrial Engineering

Developing Composite Leading Indicators to Forecast Industrial Business Cycles in Iran

References

References

Volume 6, Issue 1 - Serial Number 1
March 2017
Pages 69-89

Developing Composite Leading Indicators to Forecast Industrial Business Cycles in Iran

References

References

Volume 6, Issue 1 - Serial Number 1March 2017Pages 69-89

Volume 6, Issue 1 - Serial Number 1
March 2017
Pages 69-89