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A Re-interpretation of the Linear-Quadratic Model When Inventories and Sales are Polynomially Cointegrated

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  • Anindya BANERJEE
  • Paul MIZEN

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 to be stationary. However, these assumptions do not match the properties of the data for the last two decades in the US and the UK. We offer 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 and shows good forecasting properties.

Suggested Citation

  • Anindya BANERJEE & Paul MIZEN, 2003. "A Re-interpretation of the Linear-Quadratic Model When Inventories and Sales are Polynomially Cointegrated," Economics Working Papers ECO2003/11, European University Institute.
  • Handle: RePEc:eui:euiwps:eco2003/11
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    References listed on IDEAS

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    1. Engsted, Tom & Haldrup, Niels, 1999. " Multicointegration in Stock-Flow Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(2), pages 237-254, May.
    2. Paruolo, Paolo, 1996. "On the determination of integration indices in I(2) systems," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 313-356.
    3. Rahbek, Anders & Christian Kongsted, Hans & Jorgensen, Clara, 1999. "Trend stationarity in the I(2) cointegration model," Journal of Econometrics, Elsevier, vol. 90(2), pages 265-289, June.
    4. Alan S. Blinder & Louis J. Maccini, 1991. "Taking Stock: A Critical Assessment of Recent Research on Inventories," Journal of Economic Perspectives, American Economic Association, vol. 5(1), pages 73-96, Winter.
    5. Phillips, P C B, 1987. "Time Series Regression with a Unit Root," Econometrica, Econometric Society, vol. 55(2), pages 277-301, March.
    6. Niels Haldrup, 1998. "An Econometric Analysis of I(2) Variables," Journal of Economic Surveys, Wiley Blackwell, vol. 12(5), pages 595-650, December.
    7. Dolado, Juan & Galbraith, John W & Banerjee, Anindya, 1991. "Estimating Intertemporal Quadratic Adjustment Cost Models with Integrated Series," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 32(4), pages 919-936, November.
    8. Phillips, P C B, 1987. "Time Series Regression with a Unit Root," Econometrica, Econometric Society, vol. 55(2), pages 277-301, March.
    9. Engsted, Tom & Haldrup, Niels, 1994. "The Linear Quadratic Adjustment Cost Model and the Demand for Labour," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 9(S), pages 145-159, Suppl. De.
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    Cited by:

    1. 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.
    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. Hualde, Javier, 2014. "Estimation of long-run parameters in unbalanced cointegration," Journal of Econometrics, Elsevier, vol. 178(2), pages 761-778.

    More about this item

    Keywords

    cointegration; linear quadratic; inventories;

    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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