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News and Labor Market Dynamics in the Data and in Matching Models

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  • Francesco Zanetti
  • Konstantinos Theodoridis

Abstract

This paper uses a VAR model estimated with Bayesian methods to identify the effect of productivity news shocks on labor 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 labor 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 labor market and that the job destruction rate and real wage rigidities are critical for the variables' responses to the news shock.

Suggested Citation

  • Francesco Zanetti & Konstantinos Theodoridis, 2014. "News and Labor Market Dynamics in the Data and in Matching Models," Economics Series Working Papers 699, University of Oxford, Department of Economics.
  • Handle: RePEc:oxf:wpaper:699
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    File URL: http://www.economics.ox.ac.uk/materials/papers/13253/paper699.pdf
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Christoph Görtz & John D. Tsoukalas & Francesco Zanetti, 2016. "News Shocks under Financial Frictions," Working Papers 2016_15, Business School - Economics, University of Glasgow.
    2. 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.
    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.

    More about this item

    Keywords

    Anticipated productivity shocks; Bayesian SVAR methods; labor market search frictions;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • 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

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