Estimation of Mean and Covariance Function

Klaus Neusser ()

Chapter 11 in Time Series Econometrics, 2016, pp 207-214 from Springer

Abstract: 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

Keywords: Covariance Function; Asymptotic Distribution; ARMA Model; Multivariate Case; Natural Estimator (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/978-3-319-32862-1_11

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