IDEAS home Printed from https://ideas.repec.org/p/sbp/wpaper/103.html
   My bibliography  Save this paper

Estimation and Forecasting of Industrial Production Index

Author

Listed:
  • Muhammad Ejaz

    (State Bank of Pakistan)

  • Javed Iqbal

    (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

Suggested Citation

  • Muhammad Ejaz & Javed Iqbal, 2019. "Estimation and Forecasting of Industrial Production Index," SBP Working Paper Series 103, State Bank of Pakistan, Research Department.
    • Handle: RePEc:sbp:wpaper:103
    as

    Download full text from publisher

    File URL: http://www.sbp.org.pk/publications/wpapers/2018/wp103.pdf
    File Function: First version, 2019
    Download Restriction: no
    ---><---

    Other versions of this item:

    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Arby, Muhammad Farooq & Batool, Irem, 2007. "Estimating Quarterly Gross Fixed Capital Formation," MPRA Paper 4956, University Library of Munich, Germany.
    9. Giuseppe Parigi & Roberto Golinelli & Giorgio Bodo, 2000. "Forecasting industrial production in the Euro area," Empirical Economics, Springer, vol. 25(4), pages 541-561.
    10. Fida Hussain & Kalim Hayder & Muhammad Rehman, 2018. "Nowcasting LSM Growth in Pakistan," SBP Working Paper Series 98, State Bank of Pakistan, Research Department.
    11. 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.
    12. Jiranyakul, Komain, 2006. "The Impact of International Oil Prices on Industrial Production: The Case of Thailand," MPRA Paper 47035, University Library of Munich, Germany.
    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. 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.
    16. 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.
    17. 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.
    18. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez, 2001. "Comparing dynamic equilibrium economies to data," FRB Atlanta Working Paper 2001-23, Federal Reserve Bank of Atlanta.
    2. Tomasz Woźniak, 2016. "Bayesian Vector Autoregressions," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 49(3), pages 365-380, September.
    3. Leeper, Eric M. & Zha, Tao, 2003. "Modest policy interventions," Journal of Monetary Economics, Elsevier, vol. 50(8), pages 1673-1700, November.
    4. Jarociński, Marek & Marcet, Albert, 2019. "Priors about observables in vector autoregressions," Journal of Econometrics, Elsevier, vol. 209(2), pages 238-255.
    5. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    6. Demeshev, Boris & Malakhovskaya, Oxana, 2016. "BVAR mapping," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 43, pages 118-141.
    7. Warne, Anders & Coenen, Günter & Christoffel, Kai, 2010. "Forecasting with DSGE models," Working Paper Series 1185, European Central Bank.
    8. Marek Jarocinski, 2010. "Responses to monetary policy shocks in the east and the west of Europe: a comparison," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 833-868.
    9. Kaabia, Olfa & Abid, Ilyes & Guesmi, Khaled, 2013. "Does Bayesian shrinkage help to better reflect what happened during the subprime crisis?," Economic Modelling, Elsevier, vol. 31(C), pages 423-432.
    10. Florian Huber & Luca Rossini, 2020. "Inference in Bayesian Additive Vector Autoregressive Tree Models," Papers 2006.16333, arXiv.org, revised Mar 2021.
    11. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    12. Dan S. Rickman, 2010. "Modern Macroeconomics And Regional Economic Modeling," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 23-41, February.
    13. Higgins, Patrick & Zha, Tao & Zhong, Wenna, 2016. "Forecasting China's economic growth and inflation," China Economic Review, Elsevier, vol. 41(C), pages 46-61.
    14. Fady Barsoum, 2013. "The Effects of Monetary Policy Shocks on a Panel of Stock Market Volatilities: A Factor-Augmented Bayesian VAR Approach," Working Paper Series of the Department of Economics, University of Konstanz 2013-15, Department of Economics, University of Konstanz.
    15. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
    16. Billio, Monica & Casarin, Roberto & Rossini, Luca, 2019. "Bayesian nonparametric sparse VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 97-115.
    17. Bekiros, Stelios & Cardani, Roberta & Paccagnini, Alessia & Villa, Stefania, 2016. "Dealing with financial instability under a DSGE modeling approach with banking intermediation: A predictability analysis versus TVP-VARs," Journal of Financial Stability, Elsevier, vol. 26(C), pages 216-227.
    18. Silvia Miranda-Agrippino & Giovanni Ricco, 2021. "Bayesian local projections," Working Papers hal-03373574, HAL.
    19. Hanck, Christoph & Prüser, Jan, 2016. "House prices and interest rates: Bayesian evidence from Germany," Ruhr Economic Papers 620, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    20. Stelios D. Bekiros & Alessia Paccagnini, 2016. "Policy‐Oriented Macroeconomic Forecasting with Hybrid DGSE and Time‐Varying Parameter VAR Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(7), pages 613-632, November.

    More about this item

    Keywords

    Economic Indicator; Industry Studies; Econometric Forecasting;
    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sbp:wpaper:103. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Faisal Saleem (email available below). General contact details of provider: https://edirc.repec.org/data/sbpgvpk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.