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An application of data-rich environment for policy analysis of the Indian economy

Author

Listed:
  • Haroon Mumtaz
  • Nitin Kumar
Abstract
A good set of forecasts of key macroeconomic indicators is useful for making informed policy decisions. This analysis undertakes to perform joint forecasting of vital time series in large Bayesian VAR framework for an emerging economy such as India, where policy decisions are further complicated by consideration such as poverty, illiteracy and health. A rich information set which is known to obtain systematic improvements to the forecast accuracy is constructed. Compare to an array of contemporary completing univariate and multivariate modelling techniques, the large Bayesian VAR provides satisfactory results with significant gains in predictions. The methodology displays consistent improvements over the longer forecast horizon.Â

Suggested Citation

  • Haroon Mumtaz & Nitin Kumar, 2012. "An application of data-rich environment for policy analysis of the Indian economy," Joint Research Papers 2, Centre for Central Banking Studies, Bank of England.
    • Handle: RePEc:ccb:jrpapr:2
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    References listed on IDEAS

    as
    1. Pesaran, M. Hashem & Timmermann, Allan, 2005. "Small sample properties of forecasts from autoregressive models under structural breaks," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 183-217.
    2. Ajit R. Joshi & Debashis Acharya, 2011. "Inflation Model for India in the Context of Open Economy," South Asia Economic Journal, Institute of Policy Studies of Sri Lanka, vol. 12(1), pages 39-59, March.
    3. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    4. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    5. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components?," Discussion Paper Series 1: Economic Studies 2006,32, Deutsche Bundesbank.
    6. Carriero, A. & Kapetanios, G. & Marcellino, M., 2009. "Forecasting exchange rates with a large Bayesian VAR," International Journal of Forecasting, Elsevier, vol. 25(2), pages 400-417.
    7. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    8. Bernanke, Ben S. & Boivin, Jean, 2003. "Monetary policy in a data-rich environment," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 525-546, April.
    9. Harvey, A C & Jaeger, A, 1993. "Detrending, Stylized Facts and the Business Cycle," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(3), pages 231-247, July-Sept.
    10. Swarnjit Arora & Mohsen Bahmani-Oskooee & Gour Goswami, 2003. "Bilateral J-curve between India and her trading partners," Applied Economics, Taylor & Francis Journals, vol. 35(9), pages 1037-1041.
    11. Rudra P. Pradhan, 2011. "Forecasting Inflation in India: An Application of ANN Model," International Journal of Asian Business and Information Management (IJABIM), IGI Global, vol. 2(2), pages 64-73, April.
    12. Salvador Barrios & Sven Langedijk & Lucio Pench, 2010. "EU fiscal consolidation after the financial crisis. Lessons from past experiences," European Economy - Economic Papers 2008 - 2015 418, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    13. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
    14. Pami Dua & Nishita Raje & Satyananda Sahoo, 2004. "Interest Rate Modeling and Forecasting in India," Occasional papers 3, Centre for Development Economics, Delhi School of Economics.
    15. repec:ulb:ulbeco:2013/13388 is not listed on IDEAS
    16. 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.
    17. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    18. Refet S Gürkaynak & Brian Sack & Eric Swanson, 2005. "Do Actions Speak Louder Than Words? The Response of Asset Prices to Monetary Policy Actions and Statements," International Journal of Central Banking, International Journal of Central Banking, vol. 1(1), May.
    19. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    20. Guillermo A. Calvo & Frederic S. Mishkin, 2003. "The Mirage of Exchange Rate Regimes for Emerging Market Countries," Journal of Economic Perspectives, American Economic Association, vol. 17(4), pages 99-118, Fall.
    21. Sebastian Hauptmeier & Martin Heipertz & Ludger Schuknecht, 2007. "Expenditure Reform in Industrialised Countries: A Case-Study Approach," Fiscal Studies, Institute for Fiscal Studies, vol. 28(3), pages 293-342, September.
    22. 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.
    23. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?," Journal of Econometrics, Elsevier, vol. 146(2), pages 318-328, October.
    24. Glenn D. Rudebusch, 2006. "Monetary Policy Inertia: Fact or Fiction?," International Journal of Central Banking, International Journal of Central Banking, vol. 2(4), December.
    25. Hamilton, James D., 1986. "State-space models," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 50, pages 3039-3080, Elsevier.
    26. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, vol. 84(Q1), pages 4-18.
    27. Kadiyala, K Rao & Karlsson, Sune, 1997. "Numerical Methods for Estimation and Inference in Bayesian VAR-Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 99-132, March-Apr.
    28. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    29. 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.
    30. Mohsen Bahmani-Oskooee & Rajarshi Mitra, 2010. "How sensitive is commodity trade flows between US and India to currency depreciation?," Applied Economics, Taylor & Francis Journals, vol. 42(3), pages 267-277.
    31. R. F. Engle & D. McFadden (ed.), 1986. "Handbook of Econometrics," Handbook of Econometrics, Elsevier, edition 1, volume 4, number 4.
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