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Estimation of Mean and Covariance Function

In: Time Series Econometrics

Author

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  • Klaus Neusser

Abstract

We characterize the stationary process {X t } by its mean and its (matrix) covariance function. In the Gaussian case, this already characterizes the whole distribution. The estimation of these entities becomes crucial in the empirical analysis. As it turns out, the results from the univariate process carry over analogously to the multivariate case. If the process is observed over the periods t = 1, 2, …, T, then a natural estimator for the mean μ is the arithmetic mean or sample average: Mean Mean estimation

Suggested Citation

  • Klaus Neusser, 2016. "Estimation of Mean and Covariance Function," Springer Texts in Business and Economics, in: Time Series Econometrics, chapter 11, pages 207-214, Springer.
    • Handle: RePEc:spr:sptchp:978-3-319-32862-1_11
    DOI: 10.1007/978-3-319-32862-1_11
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