Abstract
Having selected a model and fitted its parameters to a given times series, the model can then be used to estimate new data of the time series. If such data are estimated for a time period following the final data value X T of the given time series, we speak of a prediction or forecast.
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In order to keep the notation as simple as possible, we will adopt the convention of denoting the conditional variance by \(\operatorname {var}_{T+h} :=\operatorname {var} [X_{T+h}|X_{T},\ldots ,X_{1}]\), likewise for its estimators \(\widehat {\operatorname {var}}_{T+h}:=\widehat {\operatorname {var}}[X_{T+h}|X_{T},\ldots ,X_{1}]\) for any h > 0. When these abbreviations for the conditional variances are used, it is always to be understood that the values X T, …, X 1 are known.
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Deutsch, HP., Beinker, M.W. (2019). Forecasting with Time Series Models. In: Derivatives and Internal Models. Finance and Capital Markets Series. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-22899-6_33
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DOI: https://doi.org/10.1007/978-3-030-22899-6_33
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Publisher Name: Palgrave Macmillan, Cham
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Online ISBN: 978-3-030-22899-6
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