A Matrix Exponential Spatial Panel Model with Heterogeneous Coefficients
We extend the heterogeneous coefficients spatial autoregressive panel model (HSAR) from Aquaro, Bailey, and Pesaran (2015) to the case of a heterogeneous coefficients matrix exponential spatial specification (HMESS). The HSAR is capable of producing parameter estimates for each region in the sample,...
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| Published in: | Geographical analysis Vol. 50; no. 4; pp. 422 - 453 |
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| Format: | Journal Article |
| Language: | English |
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01.10.2018
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| ISSN: | 0016-7363, 1538-4632 |
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| Abstract | We extend the heterogeneous coefficients spatial autoregressive panel model (HSAR) from Aquaro, Bailey, and Pesaran (2015) to the case of a heterogeneous coefficients matrix exponential spatial specification (HMESS). The HSAR is capable of producing parameter estimates for each region in the sample, that follow a spatial autoregressive process. Spatial autoregressive processes apply geometric decay of influence to higher‐order neighboring regions. The HMESS takes a similar approach as the HSAR to produce estimates for each region in the sample, but relies on a matrix exponential function to apply exponential decay to higher‐order neighbors. The MESS introduced by LeSage and Pace (2007) for the case of cross‐sectional spatial data samples has some potential computational advantages over the spatial autoregressive specification. In addition, the spatial dependence parameter in the MESS ranges from minus to plus infinity, which allows for use of normal priors assigned to this parameter in a Bayesian setting. We extend the cross‐sectional MESS to the case of a heterogeneous coefficients model, and describe Bayesian Markov Chain Monte Carlo estimation. We illustrate the HMESS model with a panel wage curve relationship using quarterly unemployment and wage rates from 261 counties centered on the Bakken shale oil region in North Dakota and Montana. |
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| AbstractList | We extend the heterogeneous coefficients spatial autoregressive panel model (HSAR) from Aquaro, Bailey, and Pesaran (2015) to the case of a heterogeneous coefficients matrix exponential spatial specification (HMESS). The HSAR is capable of producing parameter estimates for each region in the sample, that follow a spatial autoregressive process. Spatial autoregressive processes apply geometric decay of influence to higher‐order neighboring regions. The HMESS takes a similar approach as the HSAR to produce estimates for each region in the sample, but relies on a matrix exponential function to apply exponential decay to higher‐order neighbors. The MESS introduced by LeSage and Pace (2007) for the case of cross‐sectional spatial data samples has some potential computational advantages over the spatial autoregressive specification. In addition, the spatial dependence parameter in the MESS ranges from minus to plus infinity, which allows for use of normal priors assigned to this parameter in a Bayesian setting. We extend the cross‐sectional MESS to the case of a heterogeneous coefficients model, and describe Bayesian Markov Chain Monte Carlo estimation. We illustrate the HMESS model with a panel wage curve relationship using quarterly unemployment and wage rates from 261 counties centered on the Bakken shale oil region in North Dakota and Montana. |
| Author | LeSage, James Chih, Yao‐Yu |
| Author_xml | – sequence: 1 givenname: James surname: LeSage fullname: LeSage, James email: james.lesage@txstate.edu organization: Texas State University – sequence: 2 givenname: Yao‐Yu surname: Chih fullname: Chih, Yao‐Yu organization: Texas State University |
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| CitedBy_id | crossref_primary_10_1002_jae_2792 crossref_primary_10_1111_joes_12683 crossref_primary_10_1016_j_jeconom_2023_02_007 crossref_primary_10_1080_07474938_2022_2039494 crossref_primary_10_1007_s12076_020_00254_1 crossref_primary_10_1016_j_spasta_2018_10_004 |
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