On Accommodating Spatial Interactions in a Generalized Heterogeneous Data Model (GHDM) of Mixed Types of Dependent Variables
The authors develop an econometric framework for incorporating spatial dependence in integrated model systems of latent variables and multidimensional mixed data outcomes. The framework combines Bhat’s Generalized Heterogeneous Data Model (GHDM) with a spatial formulation to introduce spatial dependencies through latent constructs. Monte Carlo simulation experiments on synthetic data demonstrate the efficacy of the maximum approximate composite marginal likelihood (MACML) approach in recovering parameters from spatially dependent datasets, as accurately and precisely as that from aspatial data (without spatial dependency). The results also suggest that ignoring spatial dependency can lead to a substantial loss in the accuracy and efficiency of parameter estimation and in overall data fit.
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Supplemental Notes:
- This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
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Corporate Authors:
Data-Supported Transportation Operations and Planning Center
University of Texas at Austin
Austin, TX United States 78701Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Authors:
- Bhat, Chandra R
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0000-0002-0715-8121
- Dubey, Subodh K
- Pinjari, Abdul R
- Publication Date: 2015-12
Language
- English
Media Info
- Media Type: Digital/other
- Features: References; Tables;
- Pagination: 32p
Subject/Index Terms
- TRT Terms: Accuracy; Econometric models; Estimation theory; Monte Carlo method; Spatial analysis; Variables
- Subject Areas: Planning and Forecasting; Transportation (General);
Filing Info
- Accession Number: 01626755
- Record Type: Publication
- Report/Paper Numbers: D-STOP/2016/120
- Contract Numbers: DTRT13-G-UTC58
- Files: UTC, TRIS, ATRI, USDOT
- Created Date: Feb 27 2017 9:26AM