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  • 1.
    Gottfries, Nils
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Economics.
    Mickelsson, Glenn
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Economics.
    Stadin, Karolina
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Economics.
    Deep Dynamics2018Report (Other academic)
    Abstract [en]

    Combining micro and macro data, we construct demand-side shocks, which we take to be exogenous for individual firms. We estimate a reduced-form model to describe how firms adjust their production, employment, capital stock, and inventories in response to such shocks. Then, we chose the structural parameters of a theoretical model so that the theoretical model can match the impulse-response functions from the estimated reduced-form model. Firms’ reactions to demand-side shocks are well explained by a model where firms have modest market power, face convex adjustment costs and where they can vary utilization flexibly. The stock-out motive helps to explain inventory dynamics.

  • 2.
    Mickelsson, Glenn
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Economics.
    DSGE Model Estimation and Labor Market Dynamics2016Doctoral thesis, monograph (Other academic)
    Abstract [en]

    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.

  • 3.
    Mickelsson, Glenn
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Economics.
    Estimation of DSGE models: Maximum Likelihood vs. Bayesian methods2015Report (Other academic)
    Abstract [en]

    DSGE models are typically estimated using Bayesian methods, but a researcher may want to estimate a DSGE model with full information maximum likelihood (FIML) so as to avoid the use of prior distributions. A very robust algorithm is needed to find the global maximum within the relevant parameter space. I suggest such an algorithm and show that it is possible to estimate the model of Smets and Wouters (2007) using FIML. Inference is carried out using stochastic bootstrapping techniques. Several FIML estimates turn out to be significantly different from the Bayesian estimates and the reasons behind those differences are analyzed.

  • 4. Mickelsson, Glenn
    Understanding productivity and employment dynamics2013Licentiate thesis, monograph (Other academic)
1 - 4 of 4
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