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Estimating the LQAC Model with I(2) Variables

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  • Engsted, Tom
  • Haldrup, Niels

Abstract

This paper derives a method for estimating and testing the Linear Quadratic Adjustment Cost (LQAC) model when the target variable and some of the forcing variables follow I(2) processes. Based on a forward-looking error-correction formulation of the model it is shown how to obtain strongly consistent estimates of the structural parameters from both a linear and a non-linear cointegrating regression where first-differences of the I(2) variables are included as regressors (multicointegration). Further, based on the estimated parameter values, it is shown how to test and evaluate the LQAC model using a VAR approach. A simple easy interpretable metric for measuring the model fit is suggested. In an empirical application using UK money demand data, the non-linear multicointegrating regression delivers an economically plausible estimate of the adjustment cost parameter. However, the restrictions implied by the exact LQAC model under rational expectations are strongly rejected and the metric for model fit indicates a substantial noise component in the model.

Suggested Citation

  • Engsted, Tom & Haldrup, Niels, 1999. "Estimating the LQAC Model with I(2) Variables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(2), pages 155-170, March-Apr.
    • Handle: RePEc:jae:japmet:v:14:y:1999:i:2:p:155-70
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    File URL: http://qed.econ.queensu.ca:80/jae/1999-v14.2/
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    Cited by:

    1. David Aristei & Luca Pieroni, 2010. "Habits, Complementarities and Heterogeneity in Alcohol and Tobacco Demand: A Multivariate Dynamic Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 428-457, August.
    2. Víctor Tiberio Olivo, 2003. "Taylor Rules and Inflation Targeting do not Work with Systematic Foreign Exchange Market Intervention," Money Affairs, CEMLA, vol. 0(1), pages 51-67, January-J.
    3. Carlos José García T. & Jorge Enrique Restrepo, 2003. "Price Inflation and Exchange Rate Pass-Trough in Chile," Money Affairs, CEMLA, vol. 0(1), pages 69-88, January-J.
    4. Fanelli, Luca, 2006. "Multi-equational linear quadratic adjustment cost models with rational expectations and cointegration," Journal of Economic Dynamics and Control, Elsevier, vol. 30(3), pages 445-456, March.
    5. Prudence Serju, 2003. "Monetary Conditions and Core Inflation: an Application of Neural Networks," Money Affairs, CEMLA, vol. 0(1), pages 31-50, January-J.
    6. Anindya Banerjee & Paul Mizen, 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, December.
    7. Thórarinn G. Pétursson, 2000. "The representative household’s demand for money in a cointegrated VAR model," Econometrics Journal, Royal Economic Society, vol. 3(2), pages 162-176.
    8. René Lalonde & Zhenhua Zhu & Frédérick Demers, 2003. "Forecasting and Analyzing World Commodity Prices," Money Affairs, CEMLA, vol. 0(1), pages 1-30, January-J.
    9. Engsted, Tom, 2002. "Measuring noise in the Permanent Income Hypothesis," Journal of Macroeconomics, Elsevier, vol. 24(3), pages 353-370, September.
    10. Matteo Manera, 2006. "Modelling factor demands with SEM and VAR: an empirical comparison," Journal of Productivity Analysis, Springer, vol. 26(2), pages 121-146, October.
    11. repec:bla:jecsur:v:16:y:2002:i:3:p:301-55 is not listed on IDEAS
    12. Engsted, Tom & Tanggaard, Carsten, 2001. "The Danish stock and bond markets: comovement, return predictability and variance decomposition," Journal of Empirical Finance, Elsevier, vol. 8(3), pages 243-271, July.

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