CHALMOVIANSKÝ, Jakub. Impact of labour-market segmentation on identification of parameters in a DSGE Framework. Online. In M. Reiff, P. Gežík. Quantitative Methods in Economics; Multiple Criteria Decision Making XIX. University of Economics: University of Economics, Bratislava, 2018, p. 24-31. ISBN 978-80-89962-08-2.
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Basic information
Original name Impact of labour-market segmentation on identification of parameters in a DSGE Framework
Authors CHALMOVIANSKÝ, Jakub (703 Slovakia, guarantor, belonging to the institution).
Edition University of Economics, Quantitative Methods in Economics; Multiple Criteria Decision Making XIX, p. 24-31, 8 pp. 2018.
Publisher University of Economics, Bratislava
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 50202 Applied Economics, Econometrics
Country of publisher Slovakia
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
WWW Conference proceedings
RIV identification code RIV/00216224:14560/18:00102845
Organization unit Faculty of Economics and Administration
ISBN 978-80-89962-08-2
UT WoS 000455265500003
Keywords in English DSGE; identification issues; labour market segmentation; search and matching frictions
Tags International impact, Reviewed
Changed by Changed by: Mgr. Jakub Chalmovianský, Ph.D., učo 380234. Changed: 24/1/2019 12:22.
Abstract
In this contribution, I present two small-scale DSGE models of a closed economy with search and matching frictions on the labor market and right-to-manage bargaining process. The first model is the well-known model from (Lubik, 2009). The second model stems from Lubik's work, introducing labor market segmentation to account for different wage setting processes for two groups of workers with a dissimilar level of qualification. The aim of this contribution is to examine how this modification in the second model affects the amount of information needed to properly identify its parameters. At first, I shortly introduce main aspects of both models. Based on the presented calibration, trajectories of main endogenous variables are obtained. Various subsets of these simulated trajectories are then used as observables for estimation of the model parameters to compare to what extent rich information is needed for each model to properly identify its parameters.
Links
MUNI/A/0966/2017, interní kód MUName: Nekonvenční monetární politika a instituce trhu práce pohledem dynamických stochastických modelů všeobecné rovnováhy (Acronym: Nekonvenční monetární politika)
Investor: Masaryk University, Category A
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