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News shocks and labour market dynamics in matching models

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  • Konstantinos Theodoridis
  • Francesco Zanetti

Abstract

We enrich a baseline real business cycle (RBC) model with search and matching frictions on the labour market and real frictions that are helpful in accounting for the response of macroeconomic aggregates to shocks. The analysis allows shocks to have an unanticipated and a news (i.e., anticipated) component. The Bayesian estimation of the model reveals that the model that includes news shocks on macroeconomic aggregates produces a remarkable fit of the data. News shocks in stationary and non‐stationary TFP, investment‐specific productivity and preference shocks significantly affect labour market variables and explain a sizeable fraction of macroeconomic fluctuations at medium‐ and long‐run horizons. Historically, news shocks have played a relevant role for output, but they have had a limited influence on unemployment. Nouvelles et dynamique du marché du travail dans les données et dans les modèles d’arrimage. Les auteurs enrichissent un modèle de cycle d’affaires réel (CAR) d’un lissage de la consommation, d’une prise en compte de l’utilisation du capital, du coût d’ajustement de l’investissement, et des frictions dans l’arrimage sur le marché du travail, en plus d’introduire des chocs de nouvelles dans la macroéconomie et le marché du travail. Le modèle est calibré en utilisant des données sur la macroéconomie et sur le marché du travail, et son habileté à s’ajuster aux séries économiques est remarquable. D’une manière qui cadre bien avec les résultats de la littérature sur le CAR, la calibration identifie l’effet non‐linéaire du choc positif dans la productivité attribuable au travail en tant que source majeure du dynamisme économique, alors que la nouvelle à propos de ce choc joue un rôle mineur. C’est le cas pour la plupart des chocs anticipés dans ce modèle, à cette exception près que la nouvelle d’un choc dans la productivité attribuable à la fonction d’arrimage laquelle a un pouvoir substantiel d’explication du chômage et de la probabilité de trouver un emploi. Finalement un choc non anticipé dans la destruction d’emplois explique une portion importante du chômage aux États‐Unis.

Suggested Citation

  • 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.
    • Handle: RePEc:wly:canjec:v:49:y:2016:i:3:p:906-930
    DOI: 10.1111/caje.12218
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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. 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.
    3. Renato Faccini & Leonardo Melosi, 2022. "Pigouvian Cycles," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(2), pages 281-318, April.
    4. Ryan Chahrour & Sanjay K. Chugh & Tristan Potter, 2023. "Anticipated productivity and the labor market," Quantitative Economics, Econometric Society, vol. 14(3), pages 897-934, July.
    5. Luca Gambetti & Dimitris Korobilis & John D. Tsoukalas & Francesco Zanetti, 2023. "Agreed and Disagreed Uncertainty," Working Paper series 23-01, Rimini Centre for Economic Analysis.
    6. Renato Faccini & Leonardo Melosi, 2018. "The Role of News about TFP in U.S. Recessions and Booms," Working Paper Series WP-2018-6, Federal Reserve Bank of Chicago.
    7. Schnattinger, Philip, 2023. "Beliefs- and fundamentals-driven job creation," Bank of England working papers 1040, Bank of England.

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    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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