IDEAS home Printed from https://ideas.repec.org/a/fip/fedmqr/y1983isprnv.7no.2x1.html
   My bibliography  Save this article

Using vector autoregressions to measure the uncertainty in Minnesota's revenue forecasts

Author

Listed:
  • Robert B. Litterman
  • Thomas M. Supel

Abstract

No abstract is available for this item.

Suggested Citation

  • Robert B. Litterman & Thomas M. Supel, 1983. "Using vector autoregressions to measure the uncertainty in Minnesota's revenue forecasts," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 7(Spr).
    • Handle: RePEc:fip:fedmqr:y:1983:i:spr:n:v.7no.2:x:1
    as

    Download full text from publisher

    File URL: https://www.minneapolisfed.org/research/qr/qr722.pdf
    File Function: Full Text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Thomas J. Sargent, 1979. "Estimating vector autoregressions using methods not based on explicit economic theories," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 3(Sum).
    2. Robert B. Litterman, 1979. "Techniques of forecasting using vector autoregressions," Working Papers 115, Federal Reserve Bank of Minneapolis.
    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. Teresa Leal & Javier J. Pérez & Mika Tujula & Jean-Pierre Vidal, 2008. "Fiscal Forecasting: Lessons from the Literature and Challenges," Fiscal Studies, Institute for Fiscal Studies, vol. 29(3), pages 347-386, September.
    2. M. Mokliak, P. Chernov, A. Vdovychenko, A. Zubritskyi, 2015. "Spatial approach in forecasting tax revenues," Economy and Forecasting, Valeriy Heyets, issue 2, pages 7-20.
    3. Chimilila, Cyril, 2017. "Forecasting Tax Revenue and its Volatility in Tanzania," African Journal of Economic Review, African Journal of Economic Review, vol. 5(01), January.
    4. Sandrine Lardic & Auguste Mpacko Priso, 1999. "Une comparaison des prévisions des experts à celles issues des modèles B VAR," Économie et Prévision, Programme National Persée, vol. 140(4), pages 161-180.
    5. Sayim, Mustafa & Rahman, Hamid, 2015. "An examination of U.S. institutional and individual investor sentiment effect on the Turkish stock market," Global Finance Journal, Elsevier, vol. 26(C), pages 1-17.
    6. Feenberg, Daniel R, 1989. "Testing the Rationality of State Revenue Forecasts," The Review of Economics and Statistics, MIT Press, vol. 71(2), pages 300-308, May.
    7. Ford, Stephen A., 1986. "A Beginner'S Guide To Vector Autoregression," Staff Papers 13527, University of Minnesota, Department of Applied Economics.
    8. Pablo Calafiore & Gökçe Soydemir & Rahul Verma, 2010. "The Impact of Business and Consumer Sentiment on Stock Market Returns: Evidence from Brazil," Chapters, in: Brian Bruce (ed.), Handbook of Behavioral Finance, chapter 18, Edward Elgar Publishing.
    9. Richard M. Todd, 1984. "Improving economic forecasting with Bayesian vector autoregression," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 8(Fall).

    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. Preston J. Miller & Thomas M. Supel & Thomas H. Turner, 1980. "Estimating the effects of the oil-price shock," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 4(Win).
    2. Ford, Stephen A., 1986. "A Beginner'S Guide To Vector Autoregression," Staff Papers 13527, University of Minnesota, Department of Applied Economics.
    3. John H. Kareken, 1983. "Deposit insurance reform or deregulation is the cart, not the horse," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 7(Spr).
    4. Thomas F. Cargill & Steven A. Morus, 1988. "A vector autoregression model of the Nevada economy," Economic Review, Federal Reserve Bank of San Francisco, issue Win, pages 21-32.
    5. Terrence Kinal & Jonathan Ratner, 1986. "A VAR Forecasting Model of a Regional Economy: Its Construction and Comparative Accuracy," International Regional Science Review, , vol. 10(2), pages 113-126, August.
    6. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    7. Starck, Christian, 1991. "Specifying a Bayesian vector autoregression for short-run macroeconomic forecasting with an application to Finland," Research Discussion Papers 4/1991, Bank of Finland.
    8. Jarociński, Marek & Marcet, Albert, 2019. "Priors about observables in vector autoregressions," Journal of Econometrics, Elsevier, vol. 209(2), pages 238-255.
    9. Espinosa Acuña, Óscar A. & Vaca González, Paola A. & Avila Forero, Raúl A., 2013. "Elasticidades de demanda por electricidad e impactos macroecon_omicos del precio de la energía eléctrica en Colombia || Elasticity of Electricity Demand and Macroeconomics Impacts of Electricity Price," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 16(1), pages 216-249, December.
    10. Ricco, Giovanni & Callegari, Giovanni & Cimadomo, Jacopo, 2014. "Signals from the Government: Policy Uncertainty and the Transmission of Fiscal Shocks," MPRA Paper 56136, University Library of Munich, Germany.
    11. Benati, Luca, 2021. "Leaning against house prices: A structural VAR investigation," Journal of Monetary Economics, Elsevier, vol. 118(C), pages 399-412.
    12. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
    13. Moratis, George, 2021. "Quantifying the spillover effect in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 38(C).
    14. Angela Capolongo & Claudia Pacella, 2021. "Forecasting inflation in the euro area: countries matter!," Empirical Economics, Springer, vol. 61(5), pages 2477-2499, November.
    15. Mr. Matteo Ciccarelli & Mr. Alessandro Rebucci, 2003. "Bayesian Vars: A Survey of the Recent Literature with An Application to the European Monetary System," IMF Working Papers 2003/102, International Monetary Fund.
    16. Samuel N. Cohen & Silvia Lui & Will Malpass & Giulia Mantoan & Lars Nesheim & 'Aureo de Paula & Andrew Reeves & Craig Scott & Emma Small & Lingyi Yang, 2023. "Nowcasting with signature methods," Papers 2305.10256, arXiv.org.
    17. Ricco, Giovanni & Callegari, Giovanni & Cimadomo, Jacopo, 2016. "Signals from the government: Policy disagreement and the transmission of fiscal shocks," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 107-118.
    18. Bessler, David A., 1985. "The Forecast In Risk Analysis," Regional Research Projects > 1985: S-180 Annual Meeting, March 24-27, 1985, Charleston, South Carolina 271795, Regional Research Projects > S-180: An Economic Analysis of Risk Management Strategies for Agricultural Production Firms.
    19. Maged Shawky Sourial, 2002. "The Future of the Stock Market Channel In Egypt," Finance 0204002, University Library of Munich, Germany.
    20. Barraza, Santiago & Civelli, Andrea, 2020. "Economic policy uncertainty and the supply of business loans," Journal of Banking & Finance, Elsevier, vol. 121(C).

    More about this item

    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:fip:fedmqr:y:1983:i:spr:n:v.7no.2:x:1. 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: Kate Hansel (email available below). General contact details of provider: https://edirc.repec.org/data/cfrbmus.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.