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An Empirical Analysis of Business Cycles in a New Keynesian Model with Inventories

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  • Marcel Förster

    (University of Giessen)

Abstract

This paper introduces inventories in an otherwise standard dynamic stochastic general equilibrium model. Firms accumulate inventories to facilitate sales, but face a cost of doing so in terms of costly storage of intermediate goods. Based on U.S. data we estimate the parameters of our model using Bayesian methods. The results show that accounting for inventory dynamics has a significant impact on parameter estimates and the following analyses. We find that inventories enter the New Keynesian Phillips curve as an additional and significant driving variable and make the inflation process less backward-looking. Moreover, impulse responses can change in terms of magnitude and persistence. The variance decompositionreveals substantial changes regarding the driving forces of inflation and the nominal interest rate when we consider inventory holding.

Suggested Citation

  • Marcel Förster, 2014. "An Empirical Analysis of Business Cycles in a New Keynesian Model with Inventories," MAGKS Papers on Economics 201413, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  • Handle: RePEc:mar:magkse:201413
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    File URL: https://www.uni-marburg.de/fb02/makro/forschung/magkspapers/13-2014_foerster.pdf
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Inventories; Bayesian Estimation; DSGE Model; Business Cycles;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • E20 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - General (includes Measurement and Data)
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)

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