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

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  • 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
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    as
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    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.

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    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

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