跳至主要内容

Modeling Spatial Data Pooled over Time: Schematic Representation and Monte Carlo Evidences

Read full paper at:
Author(s) 
The spatial autocorrelation issue is now well established, and it is almost impossible to deal with spatial data without considering this reality. In addition, recent developments have been devoted to developing methods that deal with spatial autocorrelation in panel data. However, little effort has been devoted to dealing with spatial data (cross-section) pooled over time. This paper endeavours to bridge the gap between the theoretical modeling development and the application based on spatial data pooled over time. The paper presents a schematic representation of how spatial links can be expressed, depending on the nature of the variable, when combining the spatial multidirectional relations and temporal unidirectional relations. After that, a Monte Carlo experiment is conducted to establish the impact of applying a usual spatial econometric model to spatial data pooled over time. The results suggest that neglecting the temporal dimension of the data generating process can introduce important biases on autoregressive parameters and thus result in the inaccurate measurement of the indirect and total spatial effect related to the spatial spillover effect.
Cite this paper
Dubé, J. and Legros, D. (2015) Modeling Spatial Data Pooled over Time: Schematic Representation and Monte Carlo Evidences. Theoretical Economics Letters, 5, 132-154. doi: 10.4236/tel.2015.51018. 
 

[1] Gosset, W.S. (1914) The Elimination of Spurious Correlation Due to Position in Time or Space. Biometrika, 10, 179-180.
[2] Anselin, L. and Bera, A.K. (1998) Spatial Dependence in Linear Regression Models with an Introduction to Spatial Econometrics. Statistics: Textbooks and Monographs, 155, 237-289.
[3] Griffith, D.A. (2005) Effective Geographic Sample Size in the Presence of Spatial Autocorrelation. Annals of the Association of American Geographers, 95, 740-760. http://dx.doi.org/10.1111/j.1467-8306.2005.00484.x
[4] LeSage, J. and Pace, K.R. (2009) Introduction to Spatial Econometrics. Chapman and Hall/CRC, London.
[5] LeSage, J.P. and Pace, R.K. (2004) Models for Spatially Dependent Missing Data. The Journal of Real Estate Finance and Economics, 29, 233-254. http://dx.doi.org/10.1023/B:REAL.0000035312.82241.e4
[6] Anselin, L. (2010) Thirty Years of Spatial Econometrics. Papers in Regional Science, 89, 3-25. http://dx.doi.org/10.1111/j.1435-5957.2010.00279.x
[7] Arbia, G. (2011) A Lustrum of SEA: Recent Research Trends Following the Creation of the Spatial Econometrics Association. Spatial Economic Analysis, 6, 377-395.
[8] Dubé, J. and Legros, D. (2013) Dealing with Spatial Data Pooled over Time in Statistical Models. Letters in Spatial and Resource Sciences, 6, 1-18. http://dx.doi.org/10.1007/s12076-012-0082-3
[9] Dubé, J. and Legros, D. (2013) A Spatio-Temporal Measure of Spatial Dependence: An Example Using Real Estate Data. Papers in Regional Science, 92, 19-30.
[10] Huang, B., Huang, W. and Barry, M. (2010) Geographically and Temporally Weighted Regression for Modeling Spatio-Temporal Variation in House Prices. International Journal of Geographical Information Science, 24, 383-401. http://dx.doi.org/10.1080/13658810802672469
[11] Smith, T. and Wu, P. (2009) A Spatio-Temporal Model of Housing Prices Based on Individual Sales Transactions over Time. Journal of Geographical Systems, 11, 333-355. http://dx.doi.org/10.1007/s10109-009-0085-9
[12] Moran, P. (1950) A Test for the Serial Independence of Residuals. Biometrika, 37, 178-181. http://dx.doi.org/10.1093/biomet/37.1-2.178
[13] Dubé, J., Thériault, M. and Des Rosiers, F. (2012) Using a Fourier Polynomial Expansion to Generate a Spatial Predictor. International Journal of Housing Market and Analysis, 5, 177-195. http://dx.doi.org/10.1108/17538271211225922
[14] Legendre, P. (1993) Spatial Autocorrelation: Trouble or New Paradigm? Ecology, 74, 1659-1673. http://dx.doi.org/10.2307/1939924
[15] Getis, A. and Aldstadt, J. (2004) Constructing the Spatial Weights Matrix Using a Local Statistic. Geographical Analysis, 36, 90-104. http://dx.doi.org/10.1111/j.1538-4632.2004.tb01127.x
[16] Haining, R. (2009) The Special Nature of Spatial Data. In: Fotheringham, A.S. and Rogerson, P.A., Eds., The Sage Handbook of Spatial Analysis, Sage, Thousand Oaks, 5-24.
[17] Pinkse, J. and Slade, M.E. (2010) The Future of Spatial Econometrics. Journal of Regional Science, 50, 103-117. http://dx.doi.org/10.1111/j.1467-9787.2009.00645.x
[18] McMillen, D.P. (2010) Issues in Spatial Data Analysis. Journal of Regional Science, 50, 119-141. http://dx.doi.org/10.1111/j.1467-9787.2009.00656.x
[19] Tobler, W. (1970) A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46, 234-240. http://dx.doi.org/10.2307/143141
[20] McMillen, D.P. and McDonald, J.F. (2004) Locally Weighted Maximum-Likelihood Estimation: Monte Carlo Evidence and an Application. In: Anselin, L., Florax, R. and Rey, S.J., Eds., Advances in Spatial Econometrics: Methodology, Tools and Applications, Springer, Heidelberg, 225-240.
[21] LeSage, J. (2003) A Family of Geographically Weighted Regression Models. In: Anselin, L., Florax, R. and Rey, S.J., Eds., Advances in Spatial Econometrics: Methodology, Tools and Applications, Springer, Heidelberg, 241-278.
[22] Fotheringham, A.S., Brunsdon, C. and Charlton, M. (1998) Geographical Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis. Environment and Planning A, 30, 1905-1927. http://dx.doi.org/10.1068/a301905
[23] Fotheringham, A.S., Brunsdon, C. and Charlton, M. (2002) Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley, Hoboken.
[24] Hégerstrand, T. (1970) What about People in Regional Science? Papers of the Regional Science Association, 24, 7-21.
[25] Dubé, J., Baumont, C. and Legros, D. (2011) Utilisation des matrices de pondérations en économétrie spatiale: Proposition dans un contexte spatio-temporel. Discussion Paper e2011-01, Laboratoire d’ économie et de Gestion, Université de Bourgogne, Dijon.
[26] Pace, K.R., Barry, R., Clapp, J.M. and Rodriguez, M. (1998) Spatiotemporal Autoregressive Models of Neighborhood Effects. Journal of Real Estate Finance and Economics, 17, 15-33. http://dx.doi.org/10.1023/A:1007799028599
[27] Pace, K.R., Barry, R., Gilley, W.O. and Sirmans, C.F. (2000) A Method for Spatial-Temporal Forecasting with an Application to Real Estate Prices. International Journal of Forecasting, 16, 229-246. http://dx.doi.org/10.1016/S0169-2070(99)00047-3
[28] Wooldridge, J.M. (2001) Econometric Analysis of Cross Section and Panel Data. The MIT Press, Cambridge.
[29] LeSage, J. (1999) Spatial Econometrics. The Web Book of Regional Science, Regional Research Institute, West Virginia University, Morgantown.
[30] Des Rosiers, F., Dubé, J. and Thériault, M. (2011) Do Peer Effects Shape Property Values? Journal of Property Investment & Finance, 29, 510-528. http://dx.doi.org/10.1108/14635781111150376
[31] Dubé, J. and Legros, D. (2014) Spatial Data and Econometrics. Wiley-ISTE, Hoboken.
[32] LeSage, J. and Fischer, M. (2009) Spatial Growth Regressions: Model Specification, Estimation and Interpretation. Spatial Economic Analysis, 3, 275-304. http://dx.doi.org/10.1080/17421770802353758   eww150225lx
标签：

评论

发表评论

此博客中的热门博文

A Comparison of Methods Used to Determine the Oleic/Linoleic Acid Ratio in Cultivated Peanut (Arachis hypogaea L.)

Cultivated peanut ( Arachis hypogaea L.) is an important oil and food crop. It is also a cheap source of protein, a good source of essential vitamins and minerals, and a component of many food products. The fatty acid composition of peanuts has become increasingly important with the realization that oleic acid content significantly affects the development of rancidity. And oil content of peanuts significantly affects flavor and shelf-life. Early generation screening of breeding lines for high oleic acid content greatly increases the efficiency of developing new peanut varieties. The objective of this study was to compare the accuracy of methods used to classify individual peanut seed as high oleic or not high oleic. Three hundred and seventy-four (374) seeds, spanning twenty-three (23) genotypes varying in oil composition (i.e. high oleic (H) or normal/not high oleic (NH) inclusive of all four peanut market-types (runner, Spanish, Valencia and Virginia), were individually tested ...
发表评论
阅读全文

Location Optimization of a Coal Power Plant to Balance Costs against Plant’s Emission Exposure

Fuel and its delivery cost comprise the biggest expense in coal power plant operations. Delivery of electricity from generation to consumers requires investment in power lines and transmission grids. Placing a coal power plant or multiple power plants near dense population centers can lower transmission costs. If a coalmine is nearby, transportation costs can also be reduced. However, emissions from coal plants play a key role in worsening health crises in many countries. And coal upon combustion produces CO 2 , SO 2 , NO x , CO, Metallic and Particle Matter (PM10 & PM2.5). The presence of these chemical compounds in the atmosphere in close vicinity to humans, livestock, and agriculture carries detrimental health consequences. The goal of the research was to develop a methodology to minimize the public’s exposure to harmful emissions from coal power plants while maintaining minimal operational costs related to electric distribution losses and coal logistics. The objective was...
发表评论
阅读全文

Evaluation of the Safety and Efficacy of Continuous Use of a Home-Use High-Frequency Facial Treatment Appliance

At present, many home-use beauty devices are available in the market. In particular, many products developed for facial treatment use light, e.g., a flash lamp or a light-emitting diode (LED). In this study, the safety of 4 weeks’ continuous use of NEWA TM , a high-frequency facial treatment appliance, every alternate day at home was verified, and its efficacy was evaluated in Japanese individuals with healthy skin aged 30 years or older who complained of sagging of the facial skin.  Transepidermal water loss (TEWL), melanin levels, erythema levels, sebum secretion levels, skin color changes and wrinkle improvement in the facial skin were measured before the appliance began to be used (study baseline), at 2 and 4 weeks after it had begun to be used, and at 2 weeks after completion of the 4-week treatment period (6 weeks from the study baseline). In addition, data obtained by subjective evaluation by the subjects themselves on a visual analog scale (VAS) were also analyzed. Fur...
发表评论
阅读全文