Spatial bootstrapped microeconometrics: Forecasting for out‐of‐sample geo‐locations in big data

Spatial econometric models estimated on the big geo‐located point data have at least two problems: limited computational capabilities and inefficient forecasting for the new out‐of‐sample geo‐points. This is because of spatial weights matrix W defined for in‐sample observations only and the computat...

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Veröffentlicht in:	Scandinavian journal of statistics Jg. 50; H. 3; S. 1391 - 1419
1. Verfasser:	Kopczewska, Katarzyna
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken, USA John Wiley & Sons, Inc 01.09.2023 Blackwell Publishing Ltd
Schlagworte:	Algorithms Big Data bootstrapping Forecasting Machine learning Mathematical models predictions out‐of‐sample Profitability Regression coefficients spatial big data Spatial data spatial point‐pattern spatial weights matrix Tessellation Voronoi polygons
ISSN:	0303-6898, 1467-9469
Online-Zugang:	Volltext
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