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Specifying and Estimating Multi-Level Models for Geographical Research

Bibliographic Data

ID8962152
AuthorsKelvyn Jones (0000-0001-8398-2190, corresponding author)
Year1991
Volume16
Issue2
Pages148-148
Publication date1991-01-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueTransactions of the Institute of British Geographers (JOURNAL)
Journal identifiersISSN: 0020-2754 • E-ISSN: 1475-5661
PublisherWiley (PUBLISHER • GB)
DOI10.2307/622610
OpenAlexW2058801639
LanguageEN
Citations received72
References cited9

It is argued that multi-level models based on shrinkage estimators represent a considerable improvement over single-level models estimated by ordinary-least squares. In substantive terms, the ML models allow relationships to vary in time and space according to context. Shrinkage estimators make very efficient use of the information contained in the hierprchical data sets that are estimated by ML models. A number of ML models for house-price variation are specified in terms of fixed and random, allowed-to-vary, effects. Empirical illustrations of some of these ML models are given for house-price variation in Southampton

Econometrics · Economic geography · Geography · Regional science · Statistics · demographic modeling and climate adaptation · Housing Market and Economics · Mathematics · Spatial and Panel Data Analysis

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Unique citing works72
Citations per year2,06
Citation span1991 - 2026 (36)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 71

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