IDEAS home Printed from https://ideas.repec.org/p/qmw/qmwecw/793.html
   My bibliography  Save this paper

The State Level Impact of Uncertainty Shocks

Author

Listed:
  • Haroon Mumtaz

    (Queen Mary University of London)

  • Laura Sunder-Plassmann

    (University of Copenhagen)

  • Angeliki Theophilopoulou

    (University of Westminster)

Abstract

This paper uses a FAVAR model with stochastic volatility to estimate the impact of uncertainty shocks on real income growth in US states. The results suggest that there is a large degree of heterogeneity in the magnitude and the persistence of the response to uncertainty shocks across states. The response is largest in Michigan, Indiana and Arkansas while the real income in New York, Alaska and New Mexico seems least sensitive to uncertainty. We relate the cross section of responses to state-level characteristics and find that the magnitude of the decline in income is largest in states with a large share of manufacturing, agriculture and construction industries, a high fiscal deficit and a more volatile housing market. In contrast, a higher share of mining industries and larger inter-governmental fiscal transfers ameliorate the impact of uncertainty.

Suggested Citation

  • Haroon Mumtaz & Laura Sunder-Plassmann & Angeliki Theophilopoulou, 2016. "The State Level Impact of Uncertainty Shocks," Working Papers 793, Queen Mary University of London, School of Economics and Finance.
    • Handle: RePEc:qmw:qmwecw:793
    as

    Download full text from publisher

    File URL: https://www.qmul.ac.uk/sef/media/econ/research/workingpapers/2016/items/wp793.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Haroon Mumtaz & Konstantinos Theodoridis, 2014. "The Changing Transmission of Uncertainty shocks in the US: An Empirical Analysis," Working Papers 735, Queen Mary University of London, School of Economics and Finance.
    2. Shoag, Daniel & Veuger, Stan, 2016. "Uncertainty and the geography of the great recession," Journal of Monetary Economics, Elsevier, vol. 84(C), pages 84-93.
    3. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 69-87, January.
    4. Haroon Mumtaz & Francesco Zanetti, 2013. "The Impact of the Volatility of Monetary Policy Shocks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(4), pages 535-558, June.
    5. Haroon Mumtaz & Konstantinos Theodoridis, 2015. "The International Transmission Of Volatility Shocks: An Empirical Analysis," Journal of the European Economic Association, European Economic Association, vol. 13(3), pages 512-533, June.
    6. Timothy Cogley & Thomas J. Sargent, 2005. "Drift and Volatilities: Monetary Policies and Outcomes in the Post WWII U.S," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 262-302, April.
    7. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
    8. repec:ulb:ulbeco:2013/13388 is not listed on IDEAS
    9. Guisinger, Amy Y. & Hernandez-Murillo, Ruben & Owyang, Michael T. & Sinclair, Tara M., 2018. "A state-level analysis of Okun's law," Regional Science and Urban Economics, Elsevier, vol. 68(C), pages 239-248.
    10. 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.
    11. Gerald A. Carlino & Robert H. DeFina, 1997. "The differential regional effects of monetary policy: evidence from the U.S. States," Working Papers 97-12, Federal Reserve Bank of Philadelphia.
    12. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 1994. "Bayesian Analysis of Stochastic Volatility Models: Comments: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 413-417, October.
    13. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Common Drifting Volatility in Large Bayesian VARs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 375-390, July.
    14. Haroon Mumtaz & Konstantinos Theodoridis, 2014. "The Changing Transmission of Uncertainty shocks in the US: An Empirical Analysis," Working Papers 735, Queen Mary University of London, School of Economics and Finance.
    15. Gerald Carlino & Robert Defina, 1998. "The Differential Regional Effects Of Monetary Policy," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 572-587, November.
    16. 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.
    17. Carrière-Swallow, Yan & Céspedes, Luis Felipe, 2013. "The impact of uncertainty shocks in emerging economies," Journal of International Economics, Elsevier, vol. 90(2), pages 316-325.
    18. Leduc, Sylvain & Liu, Zheng, 2016. "Uncertainty shocks are aggregate demand shocks," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 20-35.
    19. repec:mcb:jmoncb:v:45:y:2013:i::p:535-558 is not listed on IDEAS
    20. Owyang, Michael T. & Zubairy, Sarah, 2013. "Who benefits from increased government spending? A state-level analysis," Regional Science and Urban Economics, Elsevier, vol. 43(3), pages 445-464.
    Full references (including those not matched with items on IDEAS)

    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. Alessandri, Piergiorgio & Mumtaz, Haroon, 2019. "Financial regimes and uncertainty shocks," Journal of Monetary Economics, Elsevier, vol. 101(C), pages 31-46.
    2. Mumtaz, Haroon & Theodoridis, Konstantinos, 2020. "Dynamic effects of monetary policy shocks on macroeconomic volatility," Journal of Monetary Economics, Elsevier, vol. 114(C), pages 262-282.
    3. Alessandri, Piergiorgio & Mumtaz, Haroon, 2019. "Financial regimes and uncertainty shocks," Journal of Monetary Economics, Elsevier, vol. 101(C), pages 31-46.
    4. Mumtaz, Haroon & Theodoridis, Konstantinos, 2017. "Common and country specific economic uncertainty," Journal of International Economics, Elsevier, vol. 105(C), pages 205-216.
    5. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2019. "Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors," Journal of Econometrics, Elsevier, vol. 212(1), pages 137-154.
    6. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    7. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    8. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2022. "The global component of inflation volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 700-721, June.
    9. Haroon Mumtaz, 2016. "The Evolving Transmission of Uncertainty Shocks in the United Kingdom," Econometrics, MDPI, vol. 4(1), pages 1-18, March.
    10. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Common Drifting Volatility in Large Bayesian VARs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 375-390, July.
    11. Carriero, Andrea & Mumtaz, Haroon & Theophilopoulou, Angeliki, 2015. "Macroeconomic information, structural change, and the prediction of fiscal aggregates," International Journal of Forecasting, Elsevier, vol. 31(2), pages 325-348.
    12. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.
    13. Joshua C.C. Chan & Rodney W. Strachan, 2023. "Bayesian State Space Models In Macroeconometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 58-75, February.
    14. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    15. Martin Feldkircher & Nico Hauzenberger, 2019. "How useful are time-varying parameter models for forecasting economic growth in CESEE?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q1/19, pages 29-48.
    16. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
    17. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Large Vector Autoregressions with Asymmetric Priors," Working Papers 759, Queen Mary University of London, School of Economics and Finance.
    18. Haroon Mumtaz & Paolo Surico, 2018. "Policy uncertainty and aggregate fluctuations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(3), pages 319-331, April.
    19. Huber, Florian, 2016. "Density forecasting using Bayesian global vector autoregressions with stochastic volatility," International Journal of Forecasting, Elsevier, vol. 32(3), pages 818-837.
    20. Jamie L. Cross & Chenghan Hou & Gary Koop, 2021. "Macroeconomic Forecasting with Large Stochastic Volatility in Mean VARs," Working Papers No 04/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.

    More about this item

    Keywords

    FAVAR; Stochastic volatility; Uncertainty shocks; Regional effects;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    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:qmw:qmwecw:793. 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: Nicholas Owen (email available below). General contact details of provider: https://edirc.repec.org/data/deqmwuk.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.