IDEAS home Printed from https://ideas.repec.org/p/cde/cdewps/74.html
   My bibliography  Save this paper

Macroeconometric Policy Modeling for India: A Review of Some Analytical Issues

Author

Listed:
  • V. Pandit

    (Delhi School of Economics)

Abstract

No abstract is available for this item.

Suggested Citation

  • V. Pandit, 2000. "Macroeconometric Policy Modeling for India: A Review of Some Analytical Issues," Working papers 74, Centre for Development Economics, Delhi School of Economics.
  • Handle: RePEc:cde:cdewps:74
    as

    Download full text from publisher

    File URL: http://www.cdedse.org/pdf/work74.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Francis X. Diebold, 1998. "The Past, Present, and Future of Macroeconomic Forecasting," Journal of Economic Perspectives, American Economic Association, vol. 12(2), pages 175-192, Spring.
    2. Klein, Lawrence R., 1986. "Economic policy formation: Theory and implementation (applied econometrics in the public sector)," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 3, chapter 35, pages 2057-2093, Elsevier.
    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. Gopakumar K.U. & V. Pandit, 2014. "Production, Procurement And Inflation-A Market Model For Food Grains," Working papers 238, Centre for Development Economics, Delhi School of Economics.
    2. Gopakumar K.U. & V. Pandit, 2014. "Production, Procurement and Inflation: A Market Model for Foodgrains," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 8(4), pages 431-456, November.
    3. Yoshino, Naoyuki & Paramanik, Rajendra N & Gopakumar, K U & Taghizadeh-Hesary, Farhad & Revilla, Ma. Laarni & Seetha Ram, K E, 2020. "An Aggregate-Level Macro Model for the Indian Economy," ADBI Working Papers 1201, Asian Development Bank Institute.
    4. Rajbhushan J NAYAK & Vishwanath PANDIT & Gopakumar K. U, 2020. "Structural modeling of fiscal structure for policy analysis: A case study of India," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(3(624), A), pages 139-160, Autumn.

    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. Adnan Haider Bukhari & Safdar Ullah Khan, 2008. "A Small Open Economy DSGE Model for Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 47(4), pages 963-1008.
    2. KAMKOUM, Arnaud Cedric, 2023. "The Federal Reserve’s Response to the Global Financial Crisis and its Effects: An Interrupted Time-Series Analysis of the Impact of its Quantitative Easing Programs," Thesis Commons d7pvg, Center for Open Science.
    3. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    4. Heilemann, Ullrich & Stekler, Herman, 2007. "Introduction to "The future of macroeconomic forecasting"," International Journal of Forecasting, Elsevier, vol. 23(2), pages 159-165.
    5. Martinez-Martin Jaime & Morris Richard & Onorante Luca & Piersanti Fabio Massimo, 2024. "Merging Structural and Reduced-Form Models for Forecasting," The B.E. Journal of Macroeconomics, De Gruyter, vol. 24(1), pages 399-437, January.
    6. Skrypnik, Dmitriy, 2016. "A Macroeconomic Model of the Russian Economy," MPRA Paper 93506, University Library of Munich, Germany.
    7. Michael Klien & Andrea Kunnert, 2016. "Baubewilligungen für neue Wohneinheiten in Österreich. Prognose Herbst 2016," WIFO Studies, WIFO, number 65638.
    8. Javier Andrés & Fernando Restoy, 2007. "Macroeconomic modelling in EMU: how relevant is the change in regime?," Working Papers 0718, Banco de España.
    9. repec:lan:wpaper:470 is not listed on IDEAS
    10. Warne, Anders & Coenen, Günter & Christoffel, Kai, 2010. "Forecasting with DSGE models," Working Paper Series 1185, European Central Bank.
    11. Diebold, Francis X., 2001. "Econometrics: Retrospect and prospect," Journal of Econometrics, Elsevier, vol. 100(1), pages 73-75, January.
    12. Croushore, D., 2002. "Comments on 'The state of macroeconomic forecasting'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 483-489, December.
    13. Morck, Randall & Yeung, Bernard, 2011. "Economics, History, and Causation," Business History Review, Cambridge University Press, vol. 85(1), pages 39-63, April.
    14. Heilemann, Ullrich & Stekler, H. O., 2003. "Has the accuracy of German macroeconomic forecasts improved?," Technical Reports 2003,31, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    15. Yunmi Kim & Lijuan Huo & Tae-Hwan Kim, 2020. "Dealing with Markov-Switching Parameters in Quantile Regression Models," Working papers 2020rwp-166, Yonsei University, Yonsei Economics Research Institute.
    16. Dan S. Rickman, 2010. "Modern Macroeconomics And Regional Economic Modeling," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 23-41, February.
    17. Tao Zha, 1998. "A dynamic multivariate model for use in formulating policy," Economic Review, Federal Reserve Bank of Atlanta, vol. 83(Q 1), pages 16-29.
    18. Michael Klien & Andrea Kunnert, 2016. "Baubewilligungen für neue Wohneinheiten in Österreich. Prognose Winter 2016," WIFO Studies, WIFO, number 65637.
    19. Andrea Kunnert, 2013. "Baubewilligungen für Wohneinheiten in Österreich: Prognose 2012/13 und regionale Entwicklung 2006/2011. Teilbericht 2," WIFO Studies, WIFO, number 67110.
    20. D. Tutberidze & D. Japaridze, 2017. "Macroeconomic Forecasting Using Bayesian Vector Autoregressive Approach," Вестник Киевского национального университета имени Тараса Шевченко. Экономика., Socionet;Киевский национальный университет имени Тараса Шевченко, vol. 2(191), pages 42-49.
    21. Slanicay Martin, 2014. "Some Notes on Historical, Theoretical, and Empirical Background of DSGE Models," Review of Economic Perspectives, Sciendo, vol. 14(2), pages 145-164, June.

    More about this item

    Keywords

    Structural Macro Models; Identification; VAR;
    All these keywords.

    JEL classification:

    • E1 - Macroeconomics and Monetary Economics - - General Aggregative Models
    • E6 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook

    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:cde:cdewps:74. 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: Sanjeev Sharma (email available below). General contact details of provider: https://edirc.repec.org/data/cdudein.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.