A framework for spatiotemporal prediction with small and heterogeneous data - and an application to consumer price indexes -
Heinze C (2016)
Bielefeld: Universität Bielefeld.
Bielefelder E-Dissertation | Englisch
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Abstract / Bemerkung
This text considers the prediction of consumer price indexes which allow to compare the consumer price level across time and space. For specificity, the discussion is in terms of German counties and the years 1993–2014, but all methods apply more generally. The first part of this text outlines the application, and the second part presents some corresponding theory for estimation and prediction.
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2016
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https://pub.uni-bielefeld.de/record/2906682
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Heinze C. A framework for spatiotemporal prediction with small and heterogeneous data - and an application to consumer price indexes -. Bielefeld: Universität Bielefeld; 2016.
Heinze, C. (2016). A framework for spatiotemporal prediction with small and heterogeneous data - and an application to consumer price indexes -. Bielefeld: Universität Bielefeld.
Heinze, Christian. 2016. A framework for spatiotemporal prediction with small and heterogeneous data - and an application to consumer price indexes -. Bielefeld: Universität Bielefeld.
Heinze, C. (2016). A framework for spatiotemporal prediction with small and heterogeneous data - and an application to consumer price indexes -. Bielefeld: Universität Bielefeld.
Heinze, C., 2016. A framework for spatiotemporal prediction with small and heterogeneous data - and an application to consumer price indexes -, Bielefeld: Universität Bielefeld.
C. Heinze, A framework for spatiotemporal prediction with small and heterogeneous data - and an application to consumer price indexes -, Bielefeld: Universität Bielefeld, 2016.
Heinze, C.: A framework for spatiotemporal prediction with small and heterogeneous data - and an application to consumer price indexes -. Universität Bielefeld, Bielefeld (2016).
Heinze, Christian. A framework for spatiotemporal prediction with small and heterogeneous data - and an application to consumer price indexes -. Bielefeld: Universität Bielefeld, 2016.
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