IDEAS home Printed from https://ideas.repec.org/a/ses/arsjes/2015-iii-1.html
   My bibliography  Save this article

How do Individual Sectors Respond to Macroeconomic Shocks? A Structural Dynamic Factor Approach Applied to Swiss Data

Author

Listed:
  • Gregor Bäurle
  • Elizabeth Steiner

Abstract

This paper quantifies the impact of monetary policy, exchange rates and external demand on the production sectors of the Swiss economy. As the model covers the full set of production sectors it is possible through aggregation to estimate the impact of a given shock on total GDP. We conduct the analysis in the framework of a Bayesian structural dynamic factor model. Our approach proves to be useful to cope with the large data set and at the same time allows us to consistently identify fundamental aggregate shocks. We find that monetary variables, such as interest rates and exchange rates, mainly influence the financial sectors. Variations in value added in the manufacturing sectors or business services, on the other hand, are markedly influenced by changes in external demand, but show a weaker and slower reaction to monetary variables.

Suggested Citation

  • Gregor Bäurle & Elizabeth Steiner, 2015. "How do Individual Sectors Respond to Macroeconomic Shocks? A Structural Dynamic Factor Approach Applied to Swiss Data," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 151(III), pages 167-225, September.
    • Handle: RePEc:ses:arsjes:2015-iii-1
    as

    Download full text from publisher

    File URL: http://www.sjes.ch/papers/2015-III-1.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Unknown, 1962. "Economic Development And Aid," Increasing Understanding of Public Problems and Policies:1962 17983, Farm Foundation.
    2. Andrew T. Foerster & Pierre-Daniel G. Sarte & Mark W. Watson, 2011. "Sectoral versus Aggregate Shocks: A Structural Factor Analysis of Industrial Production," Journal of Political Economy, University of Chicago Press, vol. 119(1), pages 1-38.
    3. Dr. Gregor Bäurle & Daniel Kaufmann, 2014. "Exchange rate and price dynamics in a small open economy - the role of the zero lower bound and monetary policy regimes," Working Papers 2014-10, Swiss National Bank.
    4. Chib, Siddhartha, 1993. "Bayes regression with autoregressive errors : A Gibbs sampling approach," Journal of Econometrics, Elsevier, vol. 58(3), pages 275-294, August.
    5. Elizabeth Steiner, 2010. "Estimating a Stock-Flow Model for the Swiss Housing Market," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 146(III), pages 601-627, September.
    6. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    7. repec:tep:teppwp:wp1215 is not listed on IDEAS
    8. Harald Uhlig & Pooyan Amir Ahmadi, 2012. "Measuring The Dynamic Effects Of Monetary Policy Shocks: A Bayesian Favar Approach With Sign Restriction," 2012 Meeting Papers 1060, Society for Economic Dynamics.
    9. Peter Kugler & Georg Rich, 2002. "Monetary Policy Under Low Interest Rates: The Experience of Switzerland in the late 1970s," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 138(III), pages 241-269, September.
    10. Katrin Assenmacher-Wesche, 2008. "Modeling Monetary Transmission in Switzerland with a Structural Cointegrated VAR Model," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 144(II), pages 197-246, June.
    11. Sims, Christopher A., 1992. "Interpreting the macroeconomic time series facts : The effects of monetary policy," European Economic Review, Elsevier, vol. 36(5), pages 975-1000, June.
    12. 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.
    13. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 10, pages 515-554, Elsevier.
    14. Joe Ganley & Chris Salmon, 1997. "The Industrial Impact of Monetary Policy Shocks: Some Stylised Facts," Bank of England working papers 68, Bank of England.
    15. 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.
    16. Unknown, 1990. "Outstanding Black Agricultural Economists," COSBAE Brochures, Agricultural and Applied Economics Association, Committee on the Opportunities and Status of Blacks in Agricultural Economics (COSBAE), pages 1-12, March.
    17. Marc P. Giannoni & Jean Boivin, 2005. "DSGE Models in a Data-Rich Environment," Computing in Economics and Finance 2005 431, Society for Computational Economics.
    18. Dr. Nicolas Alexis Cuche-Curti & Harris Dellas & Jean-Marc Natal, 2009. "A dynamic stochastic general equilibrium model for Switzerland," Economic Studies 2009-05, Swiss National Bank.
    19. Jordan, Thomas J. & Kugler, Peter & Lenz, Carlos & Savioz, Marcel R., 2005. "The Analysis of Forward-Looking Monetary Policy in a SVAR Framework," Working papers 2005/10, Faculty of Business and Economics - University of Basel.
    20. Unknown, 2005. "Forward," 2005 Conference: Slovenia in the EU - Challenges for Agriculture, Food Science and Rural Affairs, November 10-11, 2005, Moravske Toplice, Slovenia 183804, Slovenian Association of Agricultural Economists (DAES).
    21. Bai, Jushan & Ng, Serena, 2007. "Determining the Number of Primitive Shocks in Factor Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 52-60, January.
    22. Christiane Baumeister & Luca Benati, 2013. "Unconventional Monetary Policy and the Great Recession: Estimating the Macroeconomic Effects of a Spread Compression at the Zero Lower Bound," International Journal of Central Banking, International Journal of Central Banking, vol. 9(2), pages 165-212, June.
    23. Unknown, 1945. "Wheat in the World Economy. A Guide to Wheat Studies of the Food Research Institute - Part I. Abstracts B. Wheat in the world economy," Wheat Studies, Stanford University, Food Research Institute, pages 1-64.
    24. Caporale, Guglielmo Maria & Chui, Michael K F, 1999. "Estimating Income and Price Elasticities of Trade in a Cointegration Framework," Review of International Economics, Wiley Blackwell, vol. 7(2), pages 254-264, May.
    25. Stefan Leist, 2011. "Driving Forces of the Swiss Output Gap," Diskussionsschriften dp1107, Universitaet Bern, Departement Volkswirtschaft.
    26. Unknown, 1962. "Economic Growth Through Regional Associations," Increasing Understanding of Public Problems and Policies:1962 17970, Farm Foundation.
    27. Dr. Jonas Stulz, 2007. "Exchange rate pass-through in Switzerland: Evidence from vector autoregressions," Economic Studies 2007-04, Swiss National Bank.
    28. Peter Kugler & Thomas J. Jordan, 2004. "Structural Vector Autoregressions and the Analysis of Monetary Policy Interventions: The Swiss Case," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 140(I), pages 67-87, March.
    29. Gregor Bäurle & Tobias Menz, 2008. "Monetary Policy in a Small Open Economy Model: A DSGE-VAR Approach for Switzerland," Working Papers 08.03, Swiss National Bank, Study Center Gerzensee.
    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. Dr. Christian Hepenstrick & Jason Blunier, 2022. "What were they thinking? Estimating the quarterly forecasts underlying annual growth projections," Working Papers 2022-05, Swiss National Bank.
    2. Buchholz, Manuel & von Schweinitz, Gregor & Tonzer, Lena, 2018. "Did the Swiss exchange rate shock shock the market?," IWH Discussion Papers 9/2018, Halle Institute for Economic Research (IWH).
    3. Gregor Bäurle & Rolf Scheufele, 2019. "Credit cycles and real activity: the Swiss case," Empirical Economics, Springer, vol. 56(6), pages 1939-1966, June.
    4. Olajide O. Oyadeyi, 2024. "Macroeconomic Uncertainty and Sectoral Output in Nigeria," Economies, MDPI, vol. 12(11), pages 1-41, November.
    5. Dr. Gregor Bäurle & Sarah M. Lein & Elizabeth Steiner, 2022. "Firm net worth, external finance premia and monitoring cost - estimates based on firm-level data," Working Papers 2022-07, Swiss National Bank.
    6. Gregor von Schweinitz & Lena Tonzer & Manuel Buchholz, 2021. "Monetary policy through exchange rate pegs: The removal of the Swiss franc‐Euro floor and stock price reactions," International Review of Finance, International Review of Finance Ltd., vol. 21(4), pages 1382-1406, December.
    7. Gregor Bäurle & Elizabeth Steiner & Gabriel Züllig, 2021. "Forecasting the production side of GDP," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 458-480, April.
    8. Gregor Bäurle & Matthias Gubler & Diego R. Känzig, 2021. "International Inflation Spillovers: The Role of Different Shocks," International Journal of Central Banking, International Journal of Central Banking, vol. 17(1), pages 191-230, March.
    9. Bäurle, Gregor & Lein, Sarah M. & Steiner, Elizabeth, 2021. "Employment adjustment and financial tightness – Evidence from firm-level data," Journal of International Money and Finance, Elsevier, vol. 115(C).

    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. Helmut Lütkepohl, 2014. "Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey," Discussion Papers of DIW Berlin 1351, DIW Berlin, German Institute for Economic Research.
    2. repec:hum:wpaper:sfb649dp2014-004 is not listed on IDEAS
    3. Dr. Gregor Bäurle & Daniel Kaufmann, 2014. "Exchange rate and price dynamics in a small open economy - the role of the zero lower bound and monetary policy regimes," Working Papers 2014-10, Swiss National Bank.
    4. Jean Boivin & Marc P. Giannoni & Dalibor Stevanović, 2020. "Dynamic Effects of Credit Shocks in a Data-Rich Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 272-284, April.
    5. Gregor Bäurle & Elizabeth Steiner & Gabriel Züllig, 2021. "Forecasting the production side of GDP," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 458-480, April.
    6. Michael W. McCracken & Serena Ng, 2016. "FRED-MD: A Monthly Database for Macroeconomic Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
    7. Heaton, Chris & Solo, Victor, 2012. "Estimation of high-dimensional linear factor models with grouped variables," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 348-367.
    8. Forni, Mario & Gambetti, Luca, 2010. "The dynamic effects of monetary policy: A structural factor model approach," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 203-216, March.
    9. Ivan Hajdukovic, 2022. "Transmission mechanisms of conventional and unconventional monetary policies in open economies," International Economics and Economic Policy, Springer, vol. 19(3), pages 491-536, July.
    10. Mario Forni & Luca Gambetti, 2010. "Macroeconomic Shocks and the Business Cycle: Evidence from a Structural Factor Model," Center for Economic Research (RECent) 040, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    11. Marcella Lucchetta & Mr. Gianni De Nicolo, 2012. "Systemic Real and Financial Risks: Measurement, Forecasting, and Stress Testing," IMF Working Papers 2012/058, International Monetary Fund.
    12. Mr. Maxym Kryshko, 2011. "Data-Rich DSGE and Dynamic Factor Models," IMF Working Papers 2011/216, International Monetary Fund.
    13. Kaufmann, Daniel & Bäurle, Gregor, 2013. "Exchange Rate and Price Dynamics at the Zero Lower Bound," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79872, Verein für Socialpolitik / German Economic Association.
    14. Carlo A. Favero, 2007. "Model Evaluation in Macroeconometrics: from early empirical macroeconomic models to DSGE models," Working Papers 327, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    15. Luke Hartigan & James Morley, 2020. "A Factor Model Analysis of the Australian Economy and the Effects of Inflation Targeting," The Economic Record, The Economic Society of Australia, vol. 96(314), pages 271-293, September.
    16. Romain Houssa & Lasse Bork & Hans Dewachter, 2008. "Identification of Macroeconomic Factors in Large Panels," Working Papers 1010, University of Namur, Department of Economics.
    17. Dimitris Korobilis, 2013. "Assessing the Transmission of Monetary Policy Using Time-varying Parameter Dynamic Factor Models-super-," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(2), pages 157-179, April.
    18. Gregor Bäurle & Matthias Gubler & Diego R. Känzig, 2021. "International Inflation Spillovers: The Role of Different Shocks," International Journal of Central Banking, International Journal of Central Banking, vol. 17(1), pages 191-230, March.
    19. Mario Forni & Marc Hallin & Marco Lippi & Paolo Zaffaroni, 2011. "One-Sided Representations of Generalized Dynamic Factor Models," Working Papers ECARES ECARES 2011-019, ULB -- Universite Libre de Bruxelles.
    20. Jushan Bai & Kunpeng Li & Lina Lu, 2016. "Estimation and Inference of FAVAR Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 620-641, October.
    21. Sylvain Weber & Giovanni Ferro Luzzi, 2014. "From Lifetime Jobs to Churning?," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 150(III), pages 227-260, September.

    More about this item

    Keywords

    sectoral value added; dynamic factor model; sign restrictions;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)

    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:ses:arsjes:2015-iii-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: Kurt Schmidheiny (email available below). General contact details of provider: https://edirc.repec.org/data/sgvssea.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.