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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Vydané v:	Scandinavian journal of statistics Ročník 50; číslo 3; s. 1391 - 1419
Hlavný autor:	Kopczewska, Katarzyna
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Hoboken, USA John Wiley & Sons, Inc 01.09.2023 Blackwell Publishing Ltd
Predmet:	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
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