AI helps you reading Science

AI generates interpretation videos

AI extracts and analyses the key points of the paper to generate videos automatically


pub
Go Generating

AI Traceability

AI parses the academic lineage of this thesis


Master Reading Tree
Generate MRT

AI Insight

AI extracts a summary of this paper


Weibo:
This paper has presented some recent developments in the specification and estimation of spatial panel data models

Some recent developments in spatial panel data models

Regional Science and Urban Economics, no. 5 (2010): 255-271

Cited: 115|Views12
EI
Keywords

Abstract

Spatial econometrics has been an ongoing research field. Recently, it has been extended to panel data settings. Spatial panel data models can allow cross sectional dependence as well as state dependence, and can also enable researchers to control for unknown heterogeneity. This paper reports some recent developments in econometric specifi...More

Code:

Data:

Introduction
  • Spatial econometrics consists of econometric techniques dealing with the interactions of economic units in space, where the space can be physical or economic in nature.
  • To allow different spatial effects in the random component and the disturbances terms, Baltagi et al (2007a) generalize the panel regression model in Kapoor et al (2007).
  • It can be shown that the common parameter estimates from the transformed approach are consistent when either n or T is large, and their asymptotic distributions are properly centered (Lee and Yu, 2010b).
Highlights
  • Spatial econometrics consists of econometric techniques dealing with the interactions of economic units in space, where the space can be physical or economic in nature
  • Baltagi et al (2003) consider the testing of spatial dependence in a panel model, where spatial dependence is allowed in the disturbances
  • This paper reports some recent developments in econometric specification and estimation of the spatial panel data models for both static and dynamic cases, investigates some finite sample properties of estimators, and illustrates their relevance for empirical research in economics with two applications
  • We report bias (Bias), empirical standard deviation (E-SD) and root mean square error (RMSE)
  • This paper has presented some recent developments in the specification and estimation of spatial panel data models
  • 18.8330 40.0265 1.0870 case, we review the estimation and asymptotic properties of various spatial dynamic panel data (SDPD) models depending on the eigenvalue structure, as well as the dynamic panel data model with spatial disturbances
Results
  • By taking time differences to eliminate the fixed effects in the dynamic equation and by the construction of instrumental variables (IVs), Anderson and Hsiao (1981) show that IV methods can provide consistent estimates.
  • When T is fixed, the authors need to specify the initial condition if MLE is used.8 Section 3.3 discusses the dynamic panel model with spatial correlated disturbances, which can be treated in some situations as a special case of the general SDPD model.
  • The log likelihood function of the spatial cointegration model is the same as the stable case.
  • Transformation approach of Jn: the case with time dummies When the authors have time effects included in the SDPD model, the direct estimation method above will yield a bias of order O(max(1/n,1/T)) for the common parameters.10 In order to avoid the bias of the order
  • Elhorst (2005), Su and Yang (2007), and Yu and Lee (2007) consider the estimation of a dynamic panel data model with spatial disturbances
  • The likelihood of the unit root SDPD model without imposing the constraints γ0 = 1 and ρ0 + λ0 = 0 is similar to the stable case in Eq (19), but the asymptotic distributions of the estimates are different.
  • 13 Mutl (2006) suggests feasible generalized 2SLS approach for the estimation of dynamic panel data model with fixed effects and SAR disturbances after first-difference of data.
  • Tao (2005) considers the SDPD model with fixed effects where the disturbances are i.i.d. and suggests the use of 2SLS for the estimation.
  • As is shown in Su and Yang (2007), the ML estimates under both random and fixed effects specifications are consistent and asymptotically normally distributed, under the assumption that the specification of ΔYn1 is correct.
Conclusion
  • For the DGP with both individual and time effects, from (3a)–(3c), the authors see that the bias of the transformation approach is small when either n or T is large.
  • From (4a)–(4c), when the authors omit the time effects in the regression, the authors have much larger bias for the spatial effects coefficients λ0 and ρ0 from both the direct and transformation approaches.
  • From the first column to the third column are February, August and combined prices
Tables
  • Table1: Static spatial panel data models
  • Table2: Stable SDPD models: before bias correction
  • Table3: Stable SDPD models: after bias correction
  • Table4: Non-stable SDPD models: before bias correction
  • Table5: Non-stable SDPD models: after bias correction
  • Table6: Table 6
  • Table7: Table 7
  • Table8: Estimation results for the cigarettes demand
  • Table9: Average of 121 mid-prices of February, August and combined
  • Table10: SDPD models, August prices, wij = exp{− 1.2Dij} with row-normalization
  • Table11: SDPD models, February and August Prices, wij = exp{− 1.2Dij} with row-normalization
  • Table12: SDPD models, February and August Prices, wij = exp{θdDij} with row-normalization
  • Table13: SDPD models, February and August Prices, Wn = Wn(i), i = 1, 2, 3 with row-normalization
Download tables as Excel
Reference
  • Alvarez, J., Arellano, M., 2003. The time series and cross-section asymptotics of dynamic panel data estimators. Econometrica 71, 1121–1159.
    Google ScholarLocate open access versionFindings
  • Anderson, T.W., Hsiao, C., 1981. Estimation of dynamic models with error components. Journal of the American Statistical Association 76, 598–606.
    Google ScholarLocate open access versionFindings
  • Anselin, L., 1988. Spatial econometrics: methods and models. Kluwer Academic, The Netherlands.
    Google ScholarFindings
  • Anselin, L., 1992. Space and applied econometrics. In: Anselin (Ed.), Special Issue. Regional Science and Urban Economics, vol. 22.
    Google ScholarLocate open access versionFindings
  • Anselin, L., 2001. Spatial econometrics. In: Baltagi, Badi H. (Ed.), A companion to theoretical econometrics. Blackwell Publishers Lte, Massachusetts.
    Google ScholarLocate open access versionFindings
  • Anselin, L., Bera, A.K., 1998. Spatial dependence in linear regression models with an introduction to spatial econometrics. In: Ullah, A., Giles, D.E.A. (Eds.), Handbook of Applied Economics Statistics. Marcel Dekker, New York.
    Google ScholarLocate open access versionFindings
  • Anselin, L., Florax, R., 1995. New directions in spatial econometrics. Springer-Verlag, Berlin.
    Google ScholarFindings
  • Anselin, L., Rey, S., 1997. Spatial econometrics. In: Anselin, L., Rey, S. (Eds.), Special Issue
    Google ScholarFindings
  • International Regional Science Review, vol.
    Google ScholarLocate open access versionFindings
  • 20. Anselin, L., Le Gallo, J, Jayet, J., 2008. Spatial panel econometrics. The econometrics of panel data: fundamentals and recent developments in theory and practice. Springer, Berlin Heidelberg. Arellano, M., Bond, O., 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58, 277–297.
    Google ScholarLocate open access versionFindings
  • Baltagi, B., Levin, D., 1986. Estimating dynamic demand for cigarettes using panel data: the effects of bootlegging, taxation and advertising reconsidered. Review of Economics and Statistics 48, 148–155.
    Google ScholarLocate open access versionFindings
  • Baltagi, B., Levin, D., 1992. Cigarette taxation: raising revenues and reducing consumptions. Structural Change and Economic Dynamics 3, 321–335.
    Google ScholarLocate open access versionFindings
  • Baltagi, B., Li, D., 2006. Prediction in the panel data model with spatial correlation: the case of liquor. Spatial Economic Analysis 1, 175–185.
    Google ScholarLocate open access versionFindings
  • Baltagi, B., Song, S.H., Koh, W., 2003. Testing panel data regression models with spatial error correlation. Journal of Econometrics 117, 123–150.
    Google ScholarLocate open access versionFindings
  • Baltagi, B., Egger, P., Pfaffermayr, M., 2007a. A generalized spatial panel data model with random effects. Syracuse University, Working Paper.
    Google ScholarFindings
  • Baltagi, B., Song, S.H., Jung, B.C., Koh, W., 2007b. Testing for serial correlation, spatial autocorrelation and random effects using panel data. Journal of Econometrics 140, 5–51.
    Google ScholarLocate open access versionFindings
  • Baltagi, B., Bresson, G., Pirotte, A., 2007c. Panel unit root tests and spatial dependence. Journal of Applied Econometrics 22, 339–360.
    Google ScholarLocate open access versionFindings
  • Bhargava, A., Sargan, J.D., 1983. Estimating dynamic random effects models from panel data covering short time periods. Econometrica 51, 1635–1659.
    Google ScholarLocate open access versionFindings
  • Bun, M., Carree, M., 2005. Bias-corrected estimation in dynamic panel data models. Journal of Business & Economic Statistics 3 (2), 200–210 (11).
    Google ScholarLocate open access versionFindings
  • Case, A., 1991. Spatial patterns in household demand. Econometrica 59, 953–965.
    Google ScholarLocate open access versionFindings
  • Case, A., Hines, J.R., Rosen, H.S., 1993. Budget spillovers and fiscal policy interdependence: evidence from the states. Journal of Public Economics 52, 285–307.
    Google ScholarLocate open access versionFindings
  • Cliff, A.D., Ord, J.K., 1973. Spatial autocorrelation. Pion Ltd., London.
    Google ScholarFindings
  • Cox, D.R., 1975. Partial likelihood. Biometrika 62, 269–276.
    Google ScholarLocate open access versionFindings
  • Cox, D.R., Reid, N., 1987. Parameter orthogonality and approximate conditional inference. Journal of the Royal Statistical Society. Series B (Methodological) 49, 1–39.
    Google ScholarLocate open access versionFindings
  • Cressie, N., 1993. Statistics for spatial data. Wiley, New York. Druska, V., Horrace, W.C., 2004. Generalized moments estimation for spatial panel data: Indonesian rice farming. American Journal of Agricultural Economics 86, 185–198.
    Google ScholarLocate open access versionFindings
  • Egger, P., Pfaffermayr, M., Winner, H., 2005. An unbalanced spatial panel data approach to US state tax competition. Economics Letters 88, 329–335.
    Google ScholarLocate open access versionFindings
  • Ertur, C., Koch, W., 2007.
    Google ScholarFindings
  • Growth, technological interdependence and spatial externalities: theory and evidence. Journal of Applied Econometrics 22, 1033–1062.
    Google ScholarLocate open access versionFindings
  • Elhorst, J.P., 2003. Specification and estimation of spatial panel data models. International Regional Science Review 26, 244–268.
    Google ScholarLocate open access versionFindings
  • Elhorst, J.P., 2005. Unconditional maximum likelihood estimation of linear and loglinear dynamic models for spatial panels. Geographical Analysis 37, 85–106.
    Google ScholarLocate open access versionFindings
  • Foote, C.L., 2007. Space and time in macroeconomic panel data: young workers and state-level unemployment revisited. Working Paper No. 07-10, Federal Reserve Bank of Boston.
    Google ScholarFindings
  • Franzese, R.J., 2007. Spatial econometric models of cross-sectional interdependence in political science panel and time-series-cross-section data. Political Analysis 15, 140–164.
    Google ScholarLocate open access versionFindings
  • Frazier, C., Kockelman, K.M., 2005. Spatial econometric models for panel data: incorporating spatial and temporal data. Transportation Research Record: Journal of the Transportation Research Board 1902 (2005), 80–90.
    Google ScholarLocate open access versionFindings
  • Hahn, J., Kuersteiner, G., 2002. Asymptotically unbiased inference for a dynamic panel model with fixed effects when both n and T are large. Econometrica 70, 1639–1657.
    Google ScholarLocate open access versionFindings
  • Hahn, J., Newey, W., 2004. Jackknife and analytical bias reduction for nonlinear panel models. Econometrica 72, 1295–1319.
    Google ScholarLocate open access versionFindings
  • Hsiao, C., 1986. Analysis of Panel Data. Cambridge University Press.
    Google ScholarFindings
  • Kalbfleisch, J.D., Sprott, D.A., 1970. Application of likelihood methods to models involving large numbers of parameters. Journal of the Royal Statistical Society. Series B (Methodological) 32, 175–208.
    Google ScholarLocate open access versionFindings
  • Kapoor, M., Kelejian, H.H., Prucha, I.R., 2007. Panel data models with spatially correlated error components. Journal of Econometrics 140, 97–130.
    Google ScholarLocate open access versionFindings
  • Kelejian, H.H., Prucha, I.R., 1998. A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbance. Journal of Real Estate Finance and Economics 17 (1), 99–121.
    Google ScholarLocate open access versionFindings
  • Kelejian, H.H., Prucha, I.R., 2001. On the asymptotic distribution of the Moran I test statistic with applications. Journal of Econometrics 104, 219–257.
    Google ScholarLocate open access versionFindings
  • Kelejian, H.H., Prucha, I.R., 2007. Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances, Forthcoming in Journal of Econometrics.
    Google ScholarLocate open access versionFindings
  • Kelejian, H.H., Robinson, D., 1993. A suggested method of estimation for spatial interdependent models with autocorrelated errors, and an application to a county expenditure model. Papers in Regional Science 72, 297–312.
    Google ScholarLocate open access versionFindings
  • Keller, W., Shiue, C.H., 2007. The origin of spatial interaction. Journal of Econometrics 140, 304–332.
    Google ScholarLocate open access versionFindings
  • Kiviet, J., 1995. On bias, inconsistency, and efficiency of various estimators in dynamic panel data models. Journal of Econometrics 68, 81–126.
    Google ScholarLocate open access versionFindings
  • Korniotis, G.M., forthcoming. Estimating panel models with internal and external habit formation. Journal of Business and Economic Statistics.
    Google ScholarLocate open access versionFindings
  • Lancaster, T., 2000. The incidental parameter problem since 1948. Journal of Econometrics 95, 391–413.
    Google ScholarLocate open access versionFindings
  • Lee, L.F., 2003. Best spatial two-stage least squares estimator for a spatial autoregressive model with autoregressive disturbances. Econometric Reviews 22, 307–335.
    Google ScholarLocate open access versionFindings
  • Lee, L.F., 2004. Asymptotic distributions of quasi-maximum likelihood estimators for spatial econometric models. Econometrica 72, 1899–1925.
    Google ScholarLocate open access versionFindings
  • Lee, L.F., 2007. GMM and 2SLS estimation of mixed regressive, spatial autoregressive models. Journal of Econometrics 137, 489–514.
    Google ScholarLocate open access versionFindings
  • Lee, L.F., Yu, J., 2010a. A spatial dynamic panel data model with both time and individual fixed effects. Econometric Theory 26, 564–597.
    Google ScholarLocate open access versionFindings
  • Lee, L.F., Yu, J., 2010b. Estimation of spatial autoregressive panel data models with fixed effects. Journal of Econometrics 154 (2), 165–185.
    Google ScholarLocate open access versionFindings
  • Lee, L.F., Yu, J., 2009. A unified transformation approach for the estimation of spatial dynamic panel data models: stability, spatial cointegration and explosive roots. Manuscript. Ohio State University. http://gatton.uky.edu/faculty/yu/Research/ Unified-Transformation-0211-09.pdf.
    Locate open access versionFindings
  • Maddala, G.S., 1971. The use of variance components models in pooling cross section and time series data. Econometrica 39, 341–358.
    Google ScholarLocate open access versionFindings
  • Magnus, J.R., 1982. Multivariate error components analysis of linear and nonlinear regression models by maximum likelihood. Journal of Econometrics 19, 239–285.
    Google ScholarLocate open access versionFindings
  • Mutl, J., 2006. Dynamic panel data models with spatially correlated disturbances. PhD thesis, University of Maryland, College Park.
    Google ScholarFindings
  • Mutl, J., Pfaffermayr, M., 2008. The spatial random effects and the spatial fixed effects model: the Hausman test in a Cliff and Ord panel model. Manuscript, Institute for Advanced Studies, Vienna.
    Google ScholarFindings
  • Neyman, J., Scott, E., 1948. Consistent estimates based on partially consistent observations. Econometrica 16, 1–32.
    Google ScholarLocate open access versionFindings
  • Nickell, S.J., 1981. Biases in dynamic models with fixed effects. Econometrica 49, 1417–1426.
    Google ScholarLocate open access versionFindings
  • Ord, J.K., 1975. Estimation methods for models of spatial interaction. Journal of the
    Google ScholarLocate open access versionFindings
  • American Statistical Association 70, 120–297.
    Google ScholarFindings
  • Pesaran, M.H., Tosetti, E., 2007. Large panels with common factors and spatial correlations. Working paper. Cambridge University.
    Google ScholarFindings
  • Revelli, F., 2001. Spatial patterns in local taxation: tax mimicking or error mimicking?
    Google ScholarFindings
  • Shiue, C.H., 2002. Transport costs and the geography of arbitrage in eighteen-century
    Google ScholarFindings
  • Su, L., Yang, Z., 2007. QML estimation of dynamic panel data models with spatial errors.
    Google ScholarFindings
  • Tao, J., 2005. Spatial econometrics: models, methods and applications. PhD thesis, Ohio
    Google ScholarFindings
  • Yang, Z., Li, C., Tse, Y.K., 2006. Functional form and spatial dependence in dynamic panels. Economic Letters 91, 138–145.
    Google ScholarLocate open access versionFindings
  • Yu, J., Lee, L.F., 2007. Estimation of unit root spatial dynamic panel data models.
    Google ScholarFindings
  • Yu, J., de Jong, R., Lee, L.F., 2007. Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large: a nonstationary case. Manuscript. Ohio State University. http://www.econ.ohio-state.edu/lee/wp/ NonStationary-Spatial-Panel-0825.pdf. Yu, J., de Jong, R., Lee, L.F., 2008. Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large. Journal of Econometrics 146, 118–134.
    Locate open access versionFindings
0
Your rating :

No Ratings

Tags
Comments
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn