IDEAS home Printed from https://ideas.repec.org/p/boe/boeewp/0488.html
   My bibliography  Save this paper

News and labour market dynamics in the data and in matching models

Author

Listed:
  • Theodoridis, Konstantinos

    (Bank of England)

  • Zanetti, Francesco

    (Bank of England)

Abstract

This paper uses a vector autoregression model estimated with Bayesian methods to identify the effect of productivity news shocks on labour market variables by imposing that they are orthogonal to current technology but they explain future observed technology. In the aftermath of a positive news shock, unemployment falls, whereas wages and the job finding rate increase. The analysis establishes that news shocks are important in explaining the historical developments in labour market variables, whereas they play a minor role for movements in real activity. We show that the empirical responses to news shocks are in line with those of a baseline search and matching model of the labour market and that the job destruction rate and real wage rigidities are critical for the variables’ responses to the news shock.

Suggested Citation

  • Theodoridis, Konstantinos & Zanetti, Francesco, 2014. "News and labour market dynamics in the data and in matching models," Bank of England working papers 488, Bank of England.
  • Handle: RePEc:boe:boeewp:0488
    as

    Download full text from publisher

    File URL: https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2014/news-and-labour-market-dynamics-in-the-data-and-in-matching-models.pdf?la=en&hash=7182965D12C7AFE7A91110DAE0F4DB2284A67B1B
    File Function: Full text
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Carlos Thomas, 2011. "Search Frictions, Real Rigidities, and Inflation Dynamics," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(6), pages 1131-1164, September.
    2. Stephanie Schmitt‐Grohé & Martín Uribe, 2012. "What's News in Business Cycles," Econometrica, Econometric Society, vol. 80(6), pages 2733-2764, November.
    3. Miles S. Kimball & John G. Fernald & Susanto Basu, 2006. "Are Technology Improvements Contractionary?," American Economic Review, American Economic Association, vol. 96(5), pages 1418-1448, December.
    4. Olivier Blanchard & Jordi Galí, 2010. "Labor Markets and Monetary Policy: A New Keynesian Model with Unemployment," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(2), pages 1-30, April.
    5. Hashmat Khan & John Tsoukalas, 2012. "The Quantitative Importance of News Shocks in Estimated DSGE Models," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(8), pages 1535-1561, December.
    6. Di Pace, F. & Faccini, R., 2012. "Deep habits and the cyclical behaviour of equilibrium unemployment and vacancies," Journal of Economic Dynamics and Control, Elsevier, vol. 36(2), pages 183-200.
    7. Christopher A. Pissarides & Barbara Petrongolo, 2001. "Looking into the Black Box: A Survey of the Matching Function," Journal of Economic Literature, American Economic Association, vol. 39(2), pages 390-431, June.
    8. Paul Beaudry & Franck Portier, 2014. "News-Driven Business Cycles: Insights and Challenges," Journal of Economic Literature, American Economic Association, vol. 52(4), pages 993-1074, December.
    9. Andr? Kurmann & Christopher Otrok, 2013. "News Shocks and the Slope of the Term Structure of Interest Rates," American Economic Review, American Economic Association, vol. 103(6), pages 2612-2632, October.
    10. Galí, Jordi, 2010. "Monetary Policy and Unemployment," Handbook of Monetary Economics, in: Benjamin M. Friedman & Michael Woodford (ed.), Handbook of Monetary Economics, edition 1, volume 3, chapter 10, pages 487-546, Elsevier.
    11. Barsky, Robert B. & Sims, Eric R., 2011. "News shocks and business cycles," Journal of Monetary Economics, Elsevier, vol. 58(3), pages 273-289.
    12. Paul Beaudry & Franck Portier, 2006. "Stock Prices, News, and Economic Fluctuations," American Economic Review, American Economic Association, vol. 96(4), pages 1293-1307, September.
    13. Sims,Christopher A. (ed.), 1994. "Advances in Econometrics," Cambridge Books, Cambridge University Press, number 9780521444606.
    14. Karnizova, Lilia, 2010. "The spirit of capitalism and expectation-driven business cycles," Journal of Monetary Economics, Elsevier, vol. 57(6), pages 739-752, September.
    15. Neville Francis & Michael T. Owyang & Jennifer E. Roush & Riccardo DiCecio, 2014. "A Flexible Finite-Horizon Alternative to Long-Run Restrictions with an Application to Technology Shocks," The Review of Economics and Statistics, MIT Press, vol. 96(4), pages 638-647, October.
    16. Jonas D. M. Fisher, 2006. "The Dynamic Effects of Neutral and Investment-Specific Technology Shocks," Journal of Political Economy, University of Chicago Press, vol. 114(3), pages 413-451, June.
    17. 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.
    18. King, Robert G. & Rebelo, Sergio T., 1999. "Resuscitating real business cycles," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 14, pages 927-1007, Elsevier.
    19. Beaudry, Paul & Portier, Franck, 2004. "An exploration into Pigou's theory of cycles," Journal of Monetary Economics, Elsevier, vol. 51(6), pages 1183-1216, September.
    20. Robert Shimer, 2005. "The Cyclical Behavior of Equilibrium Unemployment and Vacancies," American Economic Review, American Economic Association, vol. 95(1), pages 25-49, March.
    21. Mark Gertler & Antonella Trigari, 2009. "Unemployment Fluctuations with Staggered Nash Wage Bargaining," Journal of Political Economy, University of Chicago Press, vol. 117(1), pages 38-86, February.
    22. Ireland, Peter N., 2004. "A method for taking models to the data," Journal of Economic Dynamics and Control, Elsevier, vol. 28(6), pages 1205-1226, March.
    23. Den Haan, Wouter J. & Kaltenbrunner, Georg, 2009. "Anticipated growth and business cycles in matching models," Journal of Monetary Economics, Elsevier, vol. 56(3), pages 309-327, April.
    24. Paul Beaudry & Bernd Lucke, 2010. "Letting Different Views about Business Cycles Compete," NBER Chapters, in: NBER Macroeconomics Annual 2009, Volume 24, pages 413-455, National Bureau of Economic Research, Inc.
    25. Eric Leeper & Todd Walker, 2011. "Information Flows and News Driven Business Cycles," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(1), pages 55-71, January.
    26. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    27. J. B. Taylor & M. Woodford (ed.), 1999. "Handbook of Macroeconomics," Handbook of Macroeconomics, Elsevier, edition 1, volume 1, number 1.
    28. Robert Shimer, 2012. "Reassessing the Ins and Outs of Unemployment," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 15(2), pages 127-148, April.
    29. repec:ulb:ulbeco:2013/13388 is not listed on IDEAS
    30. Nir Jaimovich & Sergio Rebelo, 2009. "Can News about the Future Drive the Business Cycle?," American Economic Review, American Economic Association, vol. 99(4), pages 1097-1118, September.
    31. Sims,Christopher A. (ed.), 1994. "Advances in Econometrics," Cambridge Books, Cambridge University Press, number 9780521444590.
    32. 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.
    33. Benjamin M. Friedman & Michael Woodford (ed.), 2010. "Handbook of Monetary Economics," Handbook of Monetary Economics, Elsevier, edition 1, volume 3, number 3.
    34. Klein, Paul, 2000. "Using the generalized Schur form to solve a multivariate linear rational expectations model," Journal of Economic Dynamics and Control, Elsevier, vol. 24(10), pages 1405-1423, September.
    35. Theodoridis, Konstantinos, 2011. "An efficient minimum distance estimator for DSGE models," Bank of England working papers 439, Bank of England.
    36. 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.
    37. Christoph Görtz & John D. Tsoukalas, 2017. "News and Financial Intermediation in Aggregate Fluctuations," The Review of Economics and Statistics, MIT Press, vol. 99(3), pages 514-530, July.
    38. 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.
    39. Haroon Mumtaz & Francesco Zanetti, 2012. "Neutral Technology Shocks And The Dynamics Of Labor Input: Results From An Agnostic Identification," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(1), pages 235-254, February.
    40. Arthur J. Hosios, 1990. "On The Efficiency of Matching and Related Models of Search and Unemployment," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 57(2), pages 279-298.
    41. 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.
    42. Paul Beaudry & Deokwoo Nam & Jian Wang, 2011. "Do Mood Swings Drive Business Cycles and is it Rational?," NBER Working Papers 17651, National Bureau of Economic Research, Inc.
    43. Robert E. Hall, 2005. "Employment Fluctuations with Equilibrium Wage Stickiness," American Economic Review, American Economic Association, vol. 95(1), pages 50-65, March.
    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. Christoph Görtz & John D. Tsoukalas & Francesco Zanetti, 2022. "News Shocks under Financial Frictions," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(4), pages 210-243, October.
    2. Konstantinos Theodoridis & Francesco Zanetti, 2016. "News shocks and labour market dynamics in matching models," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 49(3), pages 906-930, August.
    3. Christoph Görtz & John D. Tsoukalas, 2013. "News shocks and business cycles: bridging the gap from different methodologies," Working Papers 2013_25, Business School - Economics, University of Glasgow.
    4. Kamber, Güneş & Theodoridis, Konstantinos & Thoenissen, Christoph, 2017. "News-driven business cycles in small open economies," Journal of International Economics, Elsevier, vol. 105(C), pages 77-89.
    5. Giraitis, Liudas & Kapetanios, George & Theodoridis, Konstantinos & Yates, Tony, 2014. "Estimating time-varying DSGE models using minimum distance methods," Bank of England working papers 507, Bank of England.
    6. Giraitis, Liudas & Kapetanios, George & Theodoridis, Konstantinos & Yates, Tony, 2014. "Estimating time-varying DSGE models using minimum distance methods," Bank of England working papers 507, Bank of England.

    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. Konstantinos Theodoridis & Francesco Zanetti, 2016. "News shocks and labour market dynamics in matching models," Canadian Journal of Economics, Canadian Economics Association, vol. 49(3), pages 906-930, August.
    2. Paul Beaudry & Franck Portier, 2014. "News-Driven Business Cycles: Insights and Challenges," Journal of Economic Literature, American Economic Association, vol. 52(4), pages 993-1074, December.
    3. Haroon Mumtaz & Francesco Zanetti, 2012. "Neutral Technology Shocks And The Dynamics Of Labor Input: Results From An Agnostic Identification," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(1), pages 235-254, February.
    4. Kamber, Güneş & Theodoridis, Konstantinos & Thoenissen, Christoph, 2017. "News-driven business cycles in small open economies," Journal of International Economics, Elsevier, vol. 105(C), pages 77-89.
    5. Mumtaz, Haroon & Zanetti, Francesco, 2012. "Neutral technology shocks and employment dynamics: results based on an RBC identification scheme," Bank of England working papers 453, Bank of England.
    6. Danilo Cascaldi‐Garcia & Ana Beatriz Galvao, 2021. "News and Uncertainty Shocks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(4), pages 779-811, June.
    7. Christoph Görtz & John D. Tsoukalas & Francesco Zanetti, 2022. "News Shocks under Financial Frictions," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(4), pages 210-243, October.
    8. Karnizova Lilia, 2012. "News Shocks, Productivity and the U.S. Investment Boom-Bust Cycle," The B.E. Journal of Macroeconomics, De Gruyter, vol. 12(1), pages 1-50, June.
    9. Ramey, V.A., 2016. "Macroeconomic Shocks and Their Propagation," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 71-162, Elsevier.
    10. Miranda-Agrippino, Silvia & Hacıoglu Hoke, Sinem, 2018. "When creativity strikes: news shocks and business cycle fluctuations," LSE Research Online Documents on Economics 90381, London School of Economics and Political Science, LSE Library.
    11. Görtz, Christoph & Tsoukalas, John, 2011. "News and financial intermediation in aggregate and sectoral fluctuations," MPRA Paper 38986, University Library of Munich, Germany, revised Mar 2012.
    12. Pinter, Gabor & Theodoridis, Konstantinos & Yates, Tony, 2013. "Risk news shocks and the business cycle," Bank of England working papers 483, Bank of England.
    13. Born, Benjamin & Peter, Alexandra & Pfeifer, Johannes, 2013. "Fiscal news and macroeconomic volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2582-2601.
    14. Mumtaz, Haroon & Zanetti, Francesco, 2015. "Factor adjustment costs: A structural investigation," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 341-355.
    15. Luca Gambetti & Christoph Görtz & Dimitris Korobilis & John D. Tsoukalas & Francesco Zanetti, 2022. "The Effect of News Shocks and Monetary Policy," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 139-164, Emerald Group Publishing Limited.
    16. Haroon Mumtaz & Francesco Zanetti, 2015. "Labor Market Dynamics: A Time-Varying Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(3), pages 319-338, June.
    17. Martin Gervais & Nir Jaimovich & Henry E. Siu & Yaniv Yedid‐Levi, 2015. "Technological Learning And Labor Market Dynamics," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(1), pages 27-53, February.
    18. Mario Forni & Luca Gambetti & Luca Sala, 2014. "No News in Business Cycles," Economic Journal, Royal Economic Society, vol. 124(581), pages 1168-1191, December.
    19. Renato Faccini & Leonardo Melosi, 2022. "Pigouvian Cycles," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(2), pages 281-318, April.
    20. Francesco Zanetti, 2015. "Financial Shocks and Labor Market Fluctuations," Economics Series Working Papers Number-746, University of Oxford, Department of Economics.

    More about this item

    Keywords

    Anticipated productivity shocks; Bayesian SVAR methods; labour market search frictions;
    All these keywords.

    JEL classification:

    • 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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:boe:boeewp:0488. 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: Digital Media Team (email available below). General contact details of provider: https://edirc.repec.org/data/boegvuk.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.