An Application of Smooth Transition Regression Models to Homeless Research

Ali Jadidzadeh

MPRA Paper from University Library of Munich, Germany

Abstract: This article discusses the limitations of linear models in explaining certain aspects of homelessness-related data and proposes the use of nonlinear models to allow for state-dependent or regime-switching behavior. The threshold autoregressive (TAR) model and its smooth transition autoregressive (STAR) extensions are introduced as a popular class of nonlinear models. The article explains how these models can be applied to univariate time series data to investigate variations in weather conditions on the flow of homeless shelters over time. The objective is to identify the sensitivity of publicly-funded emergency shelter use to changes in weather conditions and better inform social agencies and government funders of predictable and unpredictable changes in demand for shelter beds. The smooth transition regression (STR) model is proposed as a useful tool for investigating nonlinearities in non-autoregressive contexts using both time series and panel data. The article concludes by highlighting the advantages of STR models and their three-stage modeling procedure: model specification, estimation, and evaluation.

Keywords: Homelessness; nonlinear models; smooth transition regression (STR) model. (search for similar items in EconPapers)
JEL-codes: C01 C53 I32 (search for similar items in EconPapers)
Date: 2022-12
New Economics Papers: this item is included in nep-ecm and nep-ets
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