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Wavelet Instruments for Efficiency Gains in Generalized Method of Moment Models

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  • Michis Antonis

    (ACNielsen)

  • Sapatinas Theofanis

    (University of Cyprus)

Abstract

We propose a simple computational method in the context of generalized method of moments for improving the efficiency of regression coefficient estimates. The gains in efficiency arise by incorporating additional moment conditions in the estimation framework based on maximal overlap wavelet packet transforms of the continuous explanatory variables. A major advantage of the proposed method is that it does not require additional exogenous auxiliary information but relies on wavelet packet transforms of the existing continuous explanatory variables. Based on existing theory, we provide theoretical arguments for the proposed methodology, for both linear and non-linear models, and demonstrate its advantages with both an empirical application concerning two brand demand models and a Monte Carlo simulation study.

Suggested Citation

  • Michis Antonis & Sapatinas Theofanis, 2007. "Wavelet Instruments for Efficiency Gains in Generalized Method of Moment Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 11(4), pages 1-25, December.
    • Handle: RePEc:bpj:sndecm:v:11:y:2007:i:4:n:4
    DOI: 10.2202/1558-3708.1531
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    Citations

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    Cited by:

    1. Michis Antonis A, 2009. "Regression Analysis of Marketing Time Series: A Wavelet Approach with Some Frequency Domain Insights," Review of Marketing Science, De Gruyter, vol. 7(1), pages 1-43, July.
    2. Michis, Antonis A., 2014. "Time scale evaluation of economic forecasts," Economics Letters, Elsevier, vol. 123(3), pages 279-281.
    3. Antonis A. Michis, 2022. "Multiscale Partial Correlation Clustering of Stock Market Returns," JRFM, MDPI, vol. 15(1), pages 1-22, January.
    4. Markidou Anna & Michis Antonis, 2016. "Channel Concentration and Retail Prices: Evidence from the Traditional Cheese Market of Cyprus," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 14(1), pages 109-119, May.
    5. Stelios Bekiros & Jose Arreola Hernandez & Gazi Salah Uddin & Ahmed Taneem Muzaffar, 2020. "On the predictability of crude oil market: A hybrid multiscale wavelet approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 599-614, July.
    6. Mohamed Elshazli A. Zidan & Anouar Ben Mabrouk & Nidhal Ben Abdallah & Tawfeeq M. Alanazi, 2024. "Multifractal wavelet dynamic mode decomposition modeling for marketing time series," Papers 2403.13361, arXiv.org.

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