Specification and estimation of a periodic spatial panel autoregressive model
Conventional estimation methods for the the spatial autoregressive (SAR) model rely on the key assumption that the spatial lag parameter is time-invariant for the entire study period. This strong assumption is likely be violated in many economic situations where spillovers may change over time. At t...
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| Published in | Journal of Spatial Econometrics Vol. 3; no. 1 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Cham
Springer International Publishing
01.12.2022
Springer |
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| ISSN | 2662-2998 2662-298X |
| DOI | 10.1007/s43071-022-00028-5 |
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| Abstract | Conventional estimation methods for the the spatial autoregressive (SAR) model rely on the key assumption that the spatial lag parameter is time-invariant for the entire study period. This strong assumption is likely be violated in many economic situations where spillovers may change over time. At the other extreme, a time-varying model where the spatial lag coefficient changes every period might be unnecessary. This paper specifies a periodic spatial autoregressive model with fixed effects and develops three estimation methods: two-stage instrumental variable (2SLS) method, quasi-maximum likelihood estimation (QMLE) approach and generalized method of moments (GMM). The Monte Carlo study investigates the small-sample properties of the proposed estimators under various scenarios and evaluates the costs of misspecification of the Data Generating Process, pointing to the usefulness of the periodic spatial autoregressive model from an applied perspective. |
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| AbstractList | Conventional estimation methods for the the spatial autoregressive (SAR) model rely on the key assumption that the spatial lag parameter is time-invariant for the entire study period. This strong assumption is likely be violated in many economic situations where spillovers may change over time. At the other extreme, a time-varying model where the spatial lag coefficient changes every period might be unnecessary. This paper specifies a periodic spatial autoregressive model with fixed effects and develops three estimation methods: two-stage instrumental variable (2SLS) method, quasi-maximum likelihood estimation (QMLE) approach and generalized method of moments (GMM). The Monte Carlo study investigates the small-sample properties of the proposed estimators under various scenarios and evaluates the costs of misspecification of the Data Generating Process, pointing to the usefulness of the periodic spatial autoregressive model from an applied perspective. |
| ArticleNumber | 13 |
| Author | Amba, Marius C. O. Le Gallo, Julie |
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| Cites_doi | 10.1016/S0731-9053(04)18005-5 10.1257/jel.20201490 10.1016/j.jeconom.2005.10.004 10.1007/978-94-015-7799-1 10.1201/9781420064254 10.1002/jae.2792 10.1016/j.jeconom.2006.09.006 10.1016/j.jeconom.2016.09.001 10.1007/978-3-642-40340-8 10.1016/j.jeconom.2014.08.006 10.1002/jae.2565 10.1023/A:1007707430416 10.1016/j.jeconom.2017.08.019 10.1016/j.econlet.2020.109531 |
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| Keywords | Fixed effects Periodical spatial dependence Spatial autoregressive model C13 C23 |
| Language | English |
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| References_xml | – reference: KelejianHHPruchaIRA generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbancesJ Real Estate Financ Econ19981719912110.1023/A:1007707430416 – reference: YangZUnified m-estimation of fixed-effects spatial dynamic models with short panelsJ Econometr201820524234710.1016/j.jeconom.2017.08.019 – reference: Anselin L (1988) Spatial econometrics: methods and models, vol. 4. Springer – reference: Kelejian HH, Prucha IR, Yuzefovich Y (2004) Instrumental variable estimation of a spatial autoregressive model with autoregressive disturbances: large and small sample results. In: Lesage James P, Pace Kelley R (eds) Spatial and spatiotemporal econometrics, vol 18, pp 163–98. Emerald Group Publishing Limited – reference: BoucherVFortinBSome challenges in the empirics of the effects of networksHandb Econ Netw20164548 – reference: CataniaLBilléAGDynamic spatial autoregressive models with autoregressive and heteroskedastic disturbancesJ Appl Economet201732611789610.1002/jae.2565 – reference: LeSage J, Pace RK (2009) Introduction to spatial econometrics. In: Statistics, textbooks and monographs. CRC Press – reference: Elhorst JP (2014) Spatial econometrics from cross-sectional data to spatial panels. Springer – reference: AquaroMBaileyNPesaranMHEstimation and inference for spatial models with heterogeneous coefficients: an application to US house pricesJ Appl Economet2021361184410.1002/jae.2792 – reference: Sasser WE (1969) A finite-sample study of various simultaneous equation estimators. Duke University – reference: Agrawal DR, Hoyt WH, Wilson JD (2022) Local policy choice: theory and empirics. J Econ Lit (Forthcoming) – reference: GuoJXiQFixed effects spatial panel data models with time-varying spatial dependenceEcon Lett202019610.1016/j.econlet.2020.109531 – reference: BlasquesFKoopmanSJLucasASchaumburgJSpillover dynamics for systemic risk measurement using spatial financial time series modelsJ Econometr201619522112310.1016/j.jeconom.2016.09.001 – reference: LeeLGMM and 2sls estimation of mixed regressive, spatial autoregressive modelsJ Econometr2007137248951410.1016/j.jeconom.2005.10.004 – reference: LeeLFThe method of elimination and substitution in the GMM estimation of mixed regressive, spatial autoregressive modelsJ Econometr2007140115518910.1016/j.jeconom.2006.09.006 – reference: LeeLJihaiYuEstimation of fixed effects panel regression models with separable and nonseparable space-time filtersJ Econometr201518411749210.1016/j.jeconom.2014.08.006 – ident: 28_CR9 doi: 10.1016/S0731-9053(04)18005-5 – volume: 45 start-page: 48 year: 2016 ident: 28_CR5 publication-title: Handb Econ Netw – ident: 28_CR1 doi: 10.1257/jel.20201490 – volume: 137 start-page: 489 issue: 2 year: 2007 ident: 28_CR11 publication-title: J Econometr doi: 10.1016/j.jeconom.2005.10.004 – ident: 28_CR2 doi: 10.1007/978-94-015-7799-1 – ident: 28_CR14 doi: 10.1201/9781420064254 – volume: 36 start-page: 18 issue: 1 year: 2021 ident: 28_CR3 publication-title: J Appl Economet doi: 10.1002/jae.2792 – volume: 140 start-page: 155 issue: 1 year: 2007 ident: 28_CR12 publication-title: J Econometr doi: 10.1016/j.jeconom.2006.09.006 – volume: 195 start-page: 211 issue: 2 year: 2016 ident: 28_CR4 publication-title: J Econometr doi: 10.1016/j.jeconom.2016.09.001 – ident: 28_CR7 doi: 10.1007/978-3-642-40340-8 – ident: 28_CR15 – volume: 184 start-page: 174 issue: 1 year: 2015 ident: 28_CR13 publication-title: J Econometr doi: 10.1016/j.jeconom.2014.08.006 – volume: 32 start-page: 1178 issue: 6 year: 2017 ident: 28_CR6 publication-title: J Appl Economet doi: 10.1002/jae.2565 – volume: 17 start-page: 99 issue: 1 year: 1998 ident: 28_CR10 publication-title: J Real Estate Financ Econ doi: 10.1023/A:1007707430416 – volume: 205 start-page: 423 issue: 2 year: 2018 ident: 28_CR16 publication-title: J Econometr doi: 10.1016/j.jeconom.2017.08.019 – volume: 196 year: 2020 ident: 28_CR8 publication-title: Econ Lett doi: 10.1016/j.econlet.2020.109531 |
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| Title | Specification and estimation of a periodic spatial panel autoregressive model |
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