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A re-interpretation of the linear quadratic model when inventories and sales are polynomially cointegrated

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  • Paul Mizen

    (School of Economics, University of Nottingham, UK)

  • Anindya Banerjee

    (Department of Economics, European University Institute, Florence, Italy)

Abstract

Estimation of the linear quadratic model, the workhorse of the inventory literature, traditionally takes inventories and sales to be first-difference stationary series, and the ratio of the two variables to be stationary. However, these assumptions do not always match the properties of the data for the last two decades in the United States. We propose a model that allows for the non-stationary characteristics of the data, using polynomial cointegration. We show that the closed-form solution has other recent models as special cases. The resulting model performs well on aggregate and disaggregated data. Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • Paul Mizen & Anindya Banerjee, 2006. "A re-interpretation of the linear quadratic model when inventories and sales are polynomially cointegrated," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(8), pages 1249-1264.
    • Handle: RePEc:jae:japmet:v:21:y:2006:i:8:p:1249-1264
    DOI: 10.1002/jae.892
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    Cited by:

    1. Hualde, Javier, 2014. "Estimation of long-run parameters in unbalanced cointegration," Journal of Econometrics, Elsevier, vol. 178(2), pages 761-778.
    2. Gomez-Biscarri, Javier & Hualde, Javier, 2015. "A residual-based ADF test for stationary cointegration in I(2) settings," Journal of Econometrics, Elsevier, vol. 184(2), pages 280-294.
    3. John D. Tsoukalas, 2011. "Input and Output Inventories in the UK," Economica, London School of Economics and Political Science, vol. 78(311), pages 460-479, July.

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    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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