IDEAS home Printed from https://ideas.repec.org/a/fau/fauart/v61y2011i5p434-449.html
   My bibliography  Save this article

An Empirical Small Labor Market Model for the Czech Economy

Author

Abstract

An empirical small labor market model for the Czech Republic is estimated in the state-space framework. Its purpose is joint modeling of the labor force, employment, wages, hours worked, output, and the GDP deflator in a consistent “structural” framework suitable for short-run forecasting. The model entails, in the long run, five driving forces: a trend labor force component, a trend labor productivity component, a long-run inflation rate, an unemployment trend, and a trend hours worked component. In the short run, the dynamics are governed by a VAR model. The model aims at describing co-movements in the labor-market variables, provides a model-based decomposition into the trend and cyclical components of the underlying series, and outperforms unrestricted VARs in forecasting. The paper also describes the second moments of labor market data at various frequencies and discusses to what extent these properties can be replicated by the data.

Suggested Citation

  • Jan Brùha, 2011. "An Empirical Small Labor Market Model for the Czech Economy," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(5), pages 434-449, November.
  • Handle: RePEc:fau:fauart:v:61:y:2011:i:5:p:434-449
    as

    Download full text from publisher

    File URL: http://journal.fsv.cuni.cz/storage/1223_bruha.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fabio Canova & Filippo Ferroni, 2011. "Multiple filtering devices for the estimation of cyclical DSGE models," Quantitative Economics, Econometric Society, vol. 2(1), pages 73-98, March.
    2. Farmer, Roger, 2010. "Expectations, Employment and Prices," OUP Catalogue, Oxford University Press, number 9780195397901.
    3. Thomas B. King, 2005. "Labor productivity and job-market flows: trends, cycles, and correlations," Supervisory Policy Analysis Working Papers 2005-04, Federal Reserve Bank of St. Louis.
    4. Jan Babecky & Kamil Dybczak & Kamil Galuscak, 2008. "Survey on Wage and Price Formation of Czech Firms," Working Papers 2008/12, Czech National Bank.
    5. Koopman, Siem Jan & Harvey, Andrew, 2003. "Computing observation weights for signal extraction and filtering," Journal of Economic Dynamics and Control, Elsevier, vol. 27(7), pages 1317-1333, May.
    6. Laurence M. Ball, 2009. "Hysteresis in Unemployment: Old and New Evidence," NBER Working Papers 14818, National Bureau of Economic Research, Inc.
    7. Harvey, A C & Jaeger, A, 1993. "Detrending, Stylized Facts and the Business Cycle," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(3), pages 231-247, July-Sept.
    8. Kevin Clinton & Marianne Johnson & Mr. Jaromir Benes & Mr. Douglas Laxton & Mr. Troy D Matheson, 2010. "Structural Models in Real Time," IMF Working Papers 2010/056, International Monetary Fund.
    9. Andrle, Michal, 2008. "The Role of Trends and Detrending in DSGE Models," MPRA Paper 13289, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    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. Beatrice Pierluigi & Jan Bruha & Roberta Serafini, 2014. "Euro area labour markets: Different reaction to shocks?," Journal of Banking and Financial Economics, University of Warsaw, Faculty of Management, vol. 2(2), pages 34-60, November.
    2. Canova, Fabio, 2014. "Bridging DSGE models and the raw data," Journal of Monetary Economics, Elsevier, vol. 67(C), pages 1-15.
    3. Mellár, Tamás & Németh, Kristóf, 2018. "A kibocsátási rés becslése többváltozós állapottérmodellekben. Szuperhiszterézis és további empirikus eredmények [Estimating output gap in multivariate state space models. Super-hysteresis and furt," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(6), pages 557-591.
    4. Bhattarai, Keshab & Mallick, Sushanta K. & Yang, Bo, 2021. "Are global spillovers complementary or competitive? Need for international policy coordination," Journal of International Money and Finance, Elsevier, vol. 110(C).
    5. Thomas M. Trimbur, 2006. "Detrending economic time series: a Bayesian generalization of the Hodrick-Prescott filter," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(4), pages 247-273.
    6. Lafourcade, Pierre & Gerali, Andrea & Brůha, Jan & Bursian, Dirk & Buss, Ginters & Corbo, Vesna & Haavio, Markus & Håkanson, Christina & Hlédik, Tibor & Kátay, Gábor & Kulikov, Dmitry & Lozej, Matija , 2016. "Labour market modelling in the light of the financial crisis," Occasional Paper Series 175, European Central Bank.
    7. Roberto Iannaccone & Edoardo Otranto, 2003. "Signal Extraction in Continuous Time and the Generalized Hodrick- Prescott Filter," Econometrics 0311002, University Library of Munich, Germany.
    8. Claudio Borio & Piti Disyatat & Mikael Juselius, 2014. "A parsimonious approach to incorporating economic information in measures of potential output," BIS Working Papers 442, Bank for International Settlements.
    9. Claudio BorioBy & Piti Disyatat & Mikael Juselius, 2017. "Rethinking potential output: embedding information about the financial cycle," Oxford Economic Papers, Oxford University Press, vol. 69(3), pages 655-677.
    10. Vasco J. Gabriel & Paul Levine & Bo Yang, 2023. "Partial dollarization and financial frictions in emerging economies," Review of International Economics, Wiley Blackwell, vol. 31(2), pages 609-651, May.
    11. Ferroni Filippo, 2011. "Trend Agnostic One-Step Estimation of DSGE Models," The B.E. Journal of Macroeconomics, De Gruyter, vol. 11(1), pages 1-36, July.
    12. Andrea Čížků, 2023. "Potenciální produkt a mezera výstupu v období ekonomických krizí [Potential Output and Output Gap in a Period of Economic Crises]," Politická ekonomie, Prague University of Economics and Business, vol. 2023(2), pages 177-198.
    13. Proietti, Tommaso, 2007. "Signal extraction and filtering by linear semiparametric methods," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 935-958, October.
    14. Katsuyuki Shibayama, 2015. "Trend Dominance in Macroeconomic Fluctuations," Studies in Economics 1518, School of Economics, University of Kent.
    15. Jaromir Tonner & Stanislav Tvrz & Osvald Vasicek, 2015. "Labour Market Modelling within a DSGE Approach," Working Papers 2015/06, Czech National Bank.
    16. Sun Xiaojin & Tsang Kwok Ping, 2019. "What cycles? Data detrending in DSGE models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(3), pages 1-23, June.
    17. Nicholas Sander, 2013. "Fresh perspectives on unobservable variables: Data decomposition of the Kalman smoother," Reserve Bank of New Zealand Analytical Notes series AN2013/09, Reserve Bank of New Zealand.
    18. Claudio BorioBy & Piti Disyatat & Mikael Juselius, 2017. "Rethinking potential output: embedding information about the financial cycle," Oxford Economic Papers, Oxford University Press, vol. 69(3), pages 655-677.
    19. Jimborean, R. & Ferroni, F., 2010. "Did Tax Policies mitigate US Business Cycles?," Working papers 296, Banque de France.

    More about this item

    Keywords

    structural time series; labor market; forecasting;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure

    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:fau:fauart:v:61:y:2011:i:5:p:434-449. 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: Natalie Svarcova (email available below). General contact details of provider: https://edirc.repec.org/data/icunicz.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.