DSGE Model Estimation and Labor Market Dynamics
2016 (English)Doctoral thesis, monograph (Other academic)
Essay 1: Estimation of DSGE Models with Uninformative Priors
DSGE models are typically estimated using Bayesian methods, but because prior information may be lacking, a number of papers have developed methods for estimation with less informative priors (diffuse priors). This paper takes this development one step further and suggests a method that allows full information maximum likelihood (FIML) estimation of a medium-sized DSGE model. FIML estimation is equivalent to placing uninformative priors on all parameters. Inference is performed using stochastic simulation techniques. The results reveal that all parameters are identifiable and several parameter estimates differ from previous estimates that were based on more informative priors. These differences are analyzed.
Essay 2: A DSGE Model with Labor Hoarding Applied to the US Labor Market
In the US, some relatively stable patterns can be observed with respect to employment, production and productivity. An increase in production is followed by an increase in employment with lags of one or two quarters. Productivity leads both production and employment, especially employment. I show that it is possible to replicate this empirical pattern in a model with only one demand-side shock and labor hoarding. I assume that firms have organizational capital that depreciates if workers are utilized to a high degree in current production. When demand increases, firms can increase utilization, but over time, they have to hire more workers and reduce utilization to restore organizational capital. The risk shock turns out to be very dominant and explains virtually all of the dynamics.
Essay 3: Demand Shocks and Labor Hoarding: Matching Micro Data
In Swedish firm-level data, output is more volatile than employment, and in response to demand shocks, employment follows output with a one- to two-year lag. To explain these observations, we use a model with labor hoarding in which firms can change production by changing the utilization rate of their employees. Matching the impulse response functions, we find that labor hoarding in combination with increasing returns to scale in production and a very high price stickiness can explain the empirical pattern very well. Increasing returns to scale implies a larger percentage change in output than in employment. Price stickiness amplifies volatility in output because the price has a dampening effect on demand changes. Both of these explain the delayed reaction in employment in response to output changes.
Place, publisher, year, edition, pages
Uppsala: Department of Economics, Uppsala University , 2016. , 166 p.
Economic studies, ISSN 0283-7668 ; 163
DSGE Models, Macroeconomics, Estimation, Uninformative Priors, Maximum Likelihood, Labor Hoarding, US Labor Market, Swedish Micro Data
Research subject Economics
IdentifiersURN: urn:nbn:se:uu:diva-301722ISBN: 978-91-85519-70-5OAI: oai:DiVA.org:uu-301722DiVA: diva2:955172
2016-11-15, B115, Ekonomikum, Kyrkogårdsgatan 10, Uppsala, 14:15 (English)
Andreasen, Martin M, Professor
Gottfries, Nils, ProfessorWalentin, Karl, PhD