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Estimation and Forecasting of Industrial Production Index

Author

Listed:
  • Muhammad Ejaz

    (Joint director of the Monetary Policy Department at the State Bank of Pakistan)

  • Javed Iqbal

    (Joint director of the Research Department at the State Bank of Pakistan)

Abstract

It is essential for policy makers to timely consider the cyclical changes in output. Monthly industrial production is one of the most important and commonly used macroeconomic indicators for this purpose. In Pakistan monthly estimates of industrial production are not available. Alternatively, policy makers rely on Large Scale Manufacturing (LSM) index which accounts for only 10% of the GDP. Another limitation of LSM is that it mainly accounts for private sector industry thus leaving out direct public sector presence in industrial production. LSM is relied upon heavily by economic policy makers to gauge economic activity in Pakistan. In this paper, we present a new Industrial Production Index (IPI), which covers whole of industrial sector in Pakistan. The advantage of this IPI index is that it provides additional information that LSM misses out. Post estimation, we built seven econometrics models reflecting conditions in real, financial and external sectors to estimate YoY changes in the proposed Instrial Production Index (IPI). Our results show that the root mean square error of the ARDL model reflecting financial conditions is lowest across all horizons
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Muhammad Ejaz & Javed Iqbal, 2021. "Estimation and Forecasting of Industrial Production Index," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 26(1), pages 1-30, Jan-June.
    • Handle: RePEc:lje:journl:v:26:y:2021:i:1:p:1-30
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    References listed on IDEAS

    as
    1. Dilip Dutta & Nasiruddin Ahmed, 2004. "Trade liberalization and industrial growth in Pakistan: a cointegration analysis," Applied Economics, Taylor & Francis Journals, vol. 36(13), pages 1421-1429.
    2. Giuseppe Parigi & Roberto Golinelli & Giorgio Bodo, 2000. "Forecasting industrial production in the Euro area," Empirical Economics, Springer, vol. 25(4), pages 541-561.
    3. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    4. Muhammad Farooq Arby & Irem Batool, 2007. "Estimating Quarterly Gross Fixed Capital Formation," SBP Working Paper Series 17, State Bank of Pakistan, Research Department.
    5. Arby, Muhammad Farooq, 2008. "Some Issues in the National Income Accounts of Pakistan (Rebasing, Quarterly and Provincial Accounts and Growth Accounting)," MPRA Paper 32048, University Library of Munich, Germany.
    6. Muhammad Nadim Hanif & Muhammad Jahanzeb Malik, 2013. "Quarterisation of National Income Accounts of Pakistan," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 9, pages 1-61.
    7. Giancarlo Bruno & Claudio Lupi, 2004. "Forecasting industrial production and the early detection of turning points," Empirical Economics, Springer, vol. 29(3), pages 647-671, September.
    8. Bodo, Giorgio & Signorini, Luigi Federico, 1987. "Short-term forecasting of the industrial production index," International Journal of Forecasting, Elsevier, vol. 3(2), pages 245-259.
    9. Jiranyakul, Komain, 2006. "The Impact of International Oil Prices on Industrial Production: The Case of Thailand," MPRA Paper 47035, University Library of Munich, Germany.
    10. Muhammad Nadim Hanif & Muhammad Jahanzeb Malik, 2015. "Evaluating the Performance of Inflation Forecasting Models of Pakistan," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 11, pages 43-78.
    11. Abdullah Tahir & Jameel Ahmed & Waqas Ahmed, 2018. "Robust Quarterization of GDP and Determination of Business Cycle Dates for IGC Partner Countries," SBP Working Paper Series 97, State Bank of Pakistan, Research Department.
    12. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
    13. Hettige, Hemamala & Lucas, Robert E B & Wheeler, David, 1992. "The Toxic Intensity of Industrial Production: Global Patterns, Trends, and Trade Policy," American Economic Review, American Economic Association, vol. 82(2), pages 478-481, May.
    14. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    15. Adel Mohsen & Soo Chua & Che Sab, 2015. "Determinants of industrial output in Syria," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 4(1), pages 1-12, December.
    16. Louis H. Bean, 1946. "International Industrialization and Per Capita Income," NBER Chapters, in: Studies in Income and Wealth, Volume 8, pages 119-143, National Bureau of Economic Research, Inc.
    17. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
    18. Fida Hussain & Kalim Hayder & Muhammad Rehman, 2018. "Nowcasting LSM Growth in Pakistan," SBP Working Paper Series 98, State Bank of Pakistan, Research Department.
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    Cited by:

    1. Bilge Pekçaglayan, 2021. "Determinants of Industrial Production in Turkey: ARDL Model," Istanbul Journal of Economics-Istanbul Iktisat Dergisi, Istanbul University, Faculty of Economics, vol. 71(71-2), pages 435-456, December.

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    More about this item

    Keywords

    Economic indicator; industry studies; econometric forecasting; Pakistan;
    All these keywords.

    JEL classification:

    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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