Testing the hypothesis of stability in spatial econometric models
The objective of this paper is to advance in the discussion of the topic of heterogeneity in a spatial econometric model. The proposal is not a novelty as there is a great deal of experience accumulated on the same subject. What differentiates this work is that we use a broad notion of the term hete...
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| Published in | Papers in regional science Vol. 88; no. 2; pp. 409 - 444 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford, UK
Blackwell Publishing Ltd
01.06.2009
Springer |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1056-8190 1435-5957 |
| DOI | 10.1111/j.1435-5957.2009.00224.x |
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| Abstract | The objective of this paper is to advance in the discussion of the topic of heterogeneity in a spatial econometric model. The proposal is not a novelty as there is a great deal of experience accumulated on the same subject. What differentiates this work is that we use a broad notion of the term heterogeneity, in association with different symptoms of stability in the elements of the equation. That is, we deal with problems (i) in the coefficients of regression, (ii) in the variance of the random term and (iii) in the mechanisms of spatial dependence. With this purpose, we develop several tests through which we tackle the problem of heterogeneity from various perspectives. We assess their performance through a Monte Carlo experiment.
Resumen
El objetivo de este artículo es avanzar en la discusión sobre el tema de la heterogeneidad existente en un modelo econométrico espacial. La propuesta no es novedosa por cuanto ya existe mucha experiencia acumulada sobre el mismo tema. Lo que distingue a este trabajo es que utilizamos una lectura amplia del término heterogeneidad, asociado a diversos síntomas de estabilidad en los elementos de la ecuación. Esto es, tratamos con problemas (i) en los coeficientes de regresión, (ii) en la varianza del término aleatorio y (iii) en los mecanismos de dependencia espacial. Con este propósito, desarrollamos varias pruebas mediante las cuales abordamos el problema de la heterogeneidad desde perspectivas diferentes. Evaluamos su rendimiento mediante un experimento de Monte Carlo. |
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| AbstractList | The objective of this paper is to advance in the discussion of the topic of heterogeneity in a spatial econometric model. The proposal is not a novelty as there is a great deal of experience accumulated on the same subject. What differentiates this work is that we use a broad notion of the term heterogeneity, in association with different symptoms of stability in the elements of the equation. That is, we deal with problems (i) in the coefficients of regression, (ii) in the variance of the random term and (iii) in the mechanisms of spatial dependence. With this purpose, we develop several tests through which we tackle the problem of heterogeneity from various perspectives. We assess their performance through a Monte Carlo experiment. Reprinted by permission of Blackwell Publihing The objective of this paper is to advance in the discussion of the topic of heterogeneity in a spatial econometric model. The proposal is not a novelty as there is a great deal of experience accumulated on the same subject. What differentiates this work is that we use a broad notion of the term heterogeneity, in association with different symptoms of stability in the elements of the equation. That is, we deal with problems (i) in the coefficients of regression, (ii) in the variance of the random term and (iii) in the mechanisms of spatial dependence. With this purpose, we develop several tests through which we tackle the problem of heterogeneity from various perspectives. We assess their performance through a Monte Carlo experiment. Resumen El objetivo de este artículo es avanzar en la discusión sobre el tema de la heterogeneidad existente en un modelo econométrico espacial. La propuesta no es novedosa por cuanto ya existe mucha experiencia acumulada sobre el mismo tema. Lo que distingue a este trabajo es que utilizamos una lectura amplia del término heterogeneidad, asociado a diversos síntomas de estabilidad en los elementos de la ecuación. Esto es, tratamos con problemas (i) en los coeficientes de regresión, (ii) en la varianza del término aleatorio y (iii) en los mecanismos de dependencia espacial. Con este propósito, desarrollamos varias pruebas mediante las cuales abordamos el problema de la heterogeneidad desde perspectivas diferentes. Evaluamos su rendimiento mediante un experimento de Monte Carlo. |
| Author | Mur, Jesús López, Fernando Angulo, Ana |
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| Keywords | Matrix analysis Spatial interaction Structural stability Test Spatial analysis Econometric model Regression analysis |
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| References | Paez A, Uchida T, Miyamoto K (2002a) A general framework for estimation and inference of geographically weighted regression models. 1: location-specific kernel bandwidth and a test for locational heterogeneity. Environment and Planning A 34: 733-754 Chow G (1960) Tests of equality between sets of coefficients in two linear regressions. Econometrica 28: 591-605 Brunsdon C, Fotheringham S, Charlton, M (1998b) Spatial nonstationarity and autoregresive models. Environment and Planning A 30: 957-973 McMillen D (2004) Employment densities, Spatial autocorrelation, and subcenters in large metropolitan areas. Journal of Regional Science 44: 225-244 Casetti E (1972) Generating models by the expansion method. Application to geographical Rresearch. Geographical Analysis 4: 81-91 Mur J, Lopez F, Angulo A (2008) Symptoms of instability in models of spatial dependence. Geographical Analysis 40: 189-211 McMillen D, McDonald J (1997) A nonparametric analysis of employment density in a policentric city. Journal of Regional Science 37: 591-612 Rietveld P, Wintershoven H (1998) Border effects and spatial autocorrelation in the supply of network infrastructure. Papers in Regional Science 77: 265-276 Parent O, Riou S (2005) Bayesian analysis of knowledge: Spillovers in European regions. Journal of Regional Science 45: 747-775 Godfrey L (1988) Misspecification tests in econometrics. Cambridge University Press, Cambridge Leung Y, Mei C, Zhang W (2000) Testing for spatial autocorrelation among the residuals of the geographically weighted regression. Environment and Planning A 32: 871-890 Breusch T, Pagan A (1979) A simple test for heterocedasticity and random coefficient variation. Econometrica 47: 1287-1294 Paez A, Uchida T, Miyamoto K (2002b) A general framework for estimation and inference of geographically weighted regression models. 2: spatial association and model specification tests. Environment and Planning A 34: 883-904 LaCombe D (2004) Does econometric methodology matter? An analysis of public policy using spatial econometric techniques. Geographical Analysis 36: 105-118 Fotheringham A, Charlton M, Brunsdon C (1998) Geographically weighted regression: A natural evolution of the expansion method for spatial data analysis. Environment and Planning A 30: 1905-1927 Koop G, Potter S (2007) Estimation and forecasting in models with multiple breaks. Review of Economic Studies 74: 763-789 Bao Y, Ullah A (2007) Finite sample properties of maximum likelihood estimator in spatial models. Journal of Econometrics 137: 396-413 Ertur C, LeGallo J, Baumond C (2006) The regional convergence process, 1980-1995: Do spatial regimes and spatial dependence matter? International Regional Science Review 29: 3-34 Lee L (2002) Consistency and efficiency of least squares estimation for mixed regressive, spatial autoregressive models. Econometric Theory 18: 252-277 Anselin L (1988a) Spatial econometrics. Methods and models. Kluwer, Dordrecht Kelejian H, Prucha I (2009) Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances. Journal of Econometrics (forthcoming) Cleveland W (1979) Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association 74: 829-836 Koenker R, Basset G (1982) Robust tests for heterokedasticity based on regression quantiles. Econometrica 50: 43-61 Leung Y, Mei C, Zhang W (2003) Statistical tests for local patterns of spatial association. Environment and Planning A 35: 725-744 Cleveland W, Devlin S (1988) Locally weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association 83: 596-610 Anselin L (1988b) Lagrange multiplier test diagnostics for spatial dependence and spatial heterogeneity. Geographical Analysis 20: 1-17 Lee L (2004) Asymptotic distributions of maximum likelihood estimators for spatial autoregressive models. Econometrica 72: 1899-1925 Anselin L (1990) Spatial dependence and spatial structural instability in applied regression analysis. Journal of Regional Science 30: 185-207 McMillen D (1996) One hundred fifty years of land values in Chicago: A nonparametric approach. Journal of Urban Economics 40: 100-124 Brunsdon C, Fotheringham A, Charlton M (1998a) Geographically weighted regression-modelling spatial non-stationarity. The Statistician 47: 431-443 1990; 30 2004; 44 2002a; 34 1998a; 47 2002; 18 1982; 50 2009 2003; 35 2008 1998b; 30 1995 1988b; 20 2004 2007; 74 1972; 4 1979; 74 2002b; 34 2005; 45 2007; 137 2004; 72 1979; 47 2004; 36 2000; 32 1997; 37 1960; 28 2006; 29 1996; 40 1988a 1988; 83 1998; 30 2008; 40 1998; 77 1988 Cleveland (10.1111/j.1435-5957.2009.00224.x_b11) 1979; 74 Lee (10.1111/j.1435-5957.2009.00224.x_b22) 2004; 72 Breusch (10.1111/j.1435-5957.2009.00224.x_b5) 1979; 47 Kelejian (10.1111/j.1435-5957.2009.00224.x_b17) 2009 Fotheringham (10.1111/j.1435-5957.2009.00224.x_b15) 1998; 30 McMillen (10.1111/j.1435-5957.2009.00224.x_b28) 1997; 37 Paez (10.1111/j.1435-5957.2009.00224.x_b32) 2002; 34 McMillen (10.1111/j.1435-5957.2009.00224.x_b27) 2004; 44 Anselin (10.1111/j.1435-5957.2009.00224.x_b2) 1988; 20 McMillen (10.1111/j.1435-5957.2009.00224.x_b26) 1996; 40 López (10.1111/j.1435-5957.2009.00224.x_b25) 2008 Mur (10.1111/j.1435-5957.2009.00224.x_b29) 2008; 40 Koenker (10.1111/j.1435-5957.2009.00224.x_b18) 1982; 50 Godfrey (10.1111/j.1435-5957.2009.00224.x_b16) 1988 Bao (10.1111/j.1435-5957.2009.00224.x_b4) 2007; 137 Paez (10.1111/j.1435-5957.2009.00224.x_b31) 2002; 34 Lee (10.1111/j.1435-5957.2009.00224.x_b21) 2002; 18 Leung (10.1111/j.1435-5957.2009.00224.x_b24) 2003; 35 Parent (10.1111/j.1435-5957.2009.00224.x_b33) 2005; 45 Casetti (10.1111/j.1435-5957.2009.00224.x_b8) 1972; 4 Casetti (10.1111/j.1435-5957.2009.00224.x_b9) 1995 Ertur (10.1111/j.1435-5957.2009.00224.x_b13) 2006; 29 Anselin (10.1111/j.1435-5957.2009.00224.x_b3) 1990; 30 Brunsdon (10.1111/j.1435-5957.2009.00224.x_b7) 1998; 30 Rietveld (10.1111/j.1435-5957.2009.00224.x_b34) 1998; 77 Chow (10.1111/j.1435-5957.2009.00224.x_b10) 1960; 28 Cleveland (10.1111/j.1435-5957.2009.00224.x_b12) 1988; 83 Brunsdon (10.1111/j.1435-5957.2009.00224.x_b6) 1998; 47 Koop (10.1111/j.1435-5957.2009.00224.x_b19) 2007; 74 LaCombe (10.1111/j.1435-5957.2009.00224.x_b20) 2004; 36 Anselin (10.1111/j.1435-5957.2009.00224.x_b1) 1988 Pace (10.1111/j.1435-5957.2009.00224.x_b30) 2004 Florax (10.1111/j.1435-5957.2009.00224.x_b14) 2004 Leung (10.1111/j.1435-5957.2009.00224.x_b23) 2000; 32 |
| References_xml | – reference: Parent O, Riou S (2005) Bayesian analysis of knowledge: Spillovers in European regions. Journal of Regional Science 45: 747-775 – reference: LaCombe D (2004) Does econometric methodology matter? An analysis of public policy using spatial econometric techniques. Geographical Analysis 36: 105-118 – reference: Paez A, Uchida T, Miyamoto K (2002b) A general framework for estimation and inference of geographically weighted regression models. 2: spatial association and model specification tests. Environment and Planning A 34: 883-904 – reference: Brunsdon C, Fotheringham A, Charlton M (1998a) Geographically weighted regression-modelling spatial non-stationarity. The Statistician 47: 431-443 – reference: Casetti E (1972) Generating models by the expansion method. Application to geographical Rresearch. Geographical Analysis 4: 81-91 – reference: Fotheringham A, Charlton M, Brunsdon C (1998) Geographically weighted regression: A natural evolution of the expansion method for spatial data analysis. Environment and Planning A 30: 1905-1927 – reference: Rietveld P, Wintershoven H (1998) Border effects and spatial autocorrelation in the supply of network infrastructure. Papers in Regional Science 77: 265-276 – reference: McMillen D (2004) Employment densities, Spatial autocorrelation, and subcenters in large metropolitan areas. Journal of Regional Science 44: 225-244 – reference: Cleveland W (1979) Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association 74: 829-836 – reference: Ertur C, LeGallo J, Baumond C (2006) The regional convergence process, 1980-1995: Do spatial regimes and spatial dependence matter? International Regional Science Review 29: 3-34 – reference: Godfrey L (1988) Misspecification tests in econometrics. Cambridge University Press, Cambridge – reference: Koop G, Potter S (2007) Estimation and forecasting in models with multiple breaks. Review of Economic Studies 74: 763-789 – reference: Brunsdon C, Fotheringham S, Charlton, M (1998b) Spatial nonstationarity and autoregresive models. Environment and Planning A 30: 957-973 – reference: Anselin L (1990) Spatial dependence and spatial structural instability in applied regression analysis. Journal of Regional Science 30: 185-207 – reference: Leung Y, Mei C, Zhang W (2003) Statistical tests for local patterns of spatial association. Environment and Planning A 35: 725-744 – reference: Mur J, Lopez F, Angulo A (2008) Symptoms of instability in models of spatial dependence. Geographical Analysis 40: 189-211 – reference: Kelejian H, Prucha I (2009) Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances. Journal of Econometrics (forthcoming) – reference: Koenker R, Basset G (1982) Robust tests for heterokedasticity based on regression quantiles. Econometrica 50: 43-61 – reference: Cleveland W, Devlin S (1988) Locally weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association 83: 596-610 – reference: Anselin L (1988b) Lagrange multiplier test diagnostics for spatial dependence and spatial heterogeneity. Geographical Analysis 20: 1-17 – reference: Chow G (1960) Tests of equality between sets of coefficients in two linear regressions. Econometrica 28: 591-605 – reference: Bao Y, Ullah A (2007) Finite sample properties of maximum likelihood estimator in spatial models. Journal of Econometrics 137: 396-413 – reference: Paez A, Uchida T, Miyamoto K (2002a) A general framework for estimation and inference of geographically weighted regression models. 1: location-specific kernel bandwidth and a test for locational heterogeneity. Environment and Planning A 34: 733-754 – reference: McMillen D, McDonald J (1997) A nonparametric analysis of employment density in a policentric city. Journal of Regional Science 37: 591-612 – reference: Lee L (2002) Consistency and efficiency of least squares estimation for mixed regressive, spatial autoregressive models. Econometric Theory 18: 252-277 – reference: Leung Y, Mei C, Zhang W (2000) Testing for spatial autocorrelation among the residuals of the geographically weighted regression. Environment and Planning A 32: 871-890 – reference: Anselin L (1988a) Spatial econometrics. Methods and models. Kluwer, Dordrecht – reference: Lee L (2004) Asymptotic distributions of maximum likelihood estimators for spatial autoregressive models. Econometrica 72: 1899-1925 – reference: McMillen D (1996) One hundred fifty years of land values in Chicago: A nonparametric approach. Journal of Urban Economics 40: 100-124 – reference: Breusch T, Pagan A (1979) A simple test for heterocedasticity and random coefficient variation. 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| SubjectTerms | Bgi / Prodig C21 C50 Dependencia espacial Econometric models Economic systems General methodology heterogeneidad heterogeneity inestabilidad espacial Monte Carlo simulation R15 Regional studies Scientific research Spatial analysis Spatial dependence spatial instability Statistical analysis Statistical and stochastic methods |
| Title | Testing the hypothesis of stability in spatial econometric models |
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