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Parameter estimation and hypothesis testing in stationary vector time series

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  • Kakizawa, Yoshihide

Abstract

Statistical inference for stationary time series is often based on the maximum likelihood principle, i.e., the maximization of the (quasi) likelihood of observations derived on Gaussian assumptions, although no such distributional assumptions are made. In this paper, we define the disparity measure between spectral density matrices and introduce the minimum distance principle for parameter estimation and hypothesis testing in spectral analysis of stationary vector time series.

Suggested Citation

  • Kakizawa, Yoshihide, 1997. "Parameter estimation and hypothesis testing in stationary vector time series," Statistics & Probability Letters, Elsevier, vol. 33(3), pages 225-234, May.
    • Handle: RePEc:eee:stapro:v:33:y:1997:i:3:p:225-234
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    References listed on IDEAS

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    1. Robinson, P M, 1991. "Automatic Frequency Domain Inference on Semiparametric and Nonparametric Models," Econometrica, Econometric Society, vol. 59(5), pages 1329-1363, September.
    2. Robinson, P. M., 1978. "Alternative models for stationary stochastic processes," Stochastic Processes and their Applications, Elsevier, vol. 8(2), pages 141-152, December.
    3. Rice, John, 1979. "On the estimation of the parameters of a power spectrum," Journal of Multivariate Analysis, Elsevier, vol. 9(3), pages 378-392, September.
    4. Salicru, M. & Morales, D. & Menendez, M. L. & Pardo, L., 1994. "On the Applications of Divergence Type Measures in Testing Statistical Hypotheses," Journal of Multivariate Analysis, Elsevier, vol. 51(2), pages 372-391, November.
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