Spatial econometrics in an age of CyberGIScience

References

• Anselin , L. 1988 . Spatial econometrics: methods and models , Dordrecht : Kluwer Academic .
   Google Scholar
• Anselin , L. 1989 . Spatial regression analysis on the PC: spatial econometrics using GAUSS , Santa Barbara : Department of Geography, University of California, Technical report .
   Google Scholar
• Anselin , L. 1991 . SpaceStat, a software program for analysis of spatial data , Santa Barbara : National Center for Geographic Information and Analysis (NCGIA), University of California, Technical report .
   Google Scholar
• Anselin , L. 1992 . SpaceStat tutorial: a workbook for using spaceStat in the analysis of spatial data , Santa Barbara : National Center for Geographic Information and Analysis (NCGIA), University of California, Technical report .
   Google Scholar
• Anselin , L. 1995 . SpaceStat version 1.80 user's guide , Morgantown : Regional Research Institute, West Virginia University, Technical report .
   Google Scholar
• Anselin , L. 2003 . Spatial externalities, spatial multipliers and spatial econometrics . International Regional Science Review , 26 ( 2 ) : 153 – 166 .
   Web of Science ®Google Scholar
• Anselin , L. 2006 . “ Spatial econometrics ” . In Palgrave handbook of econometrics: volume 1, econometric theory , Edited by: Mills , A.T.C. and Patterson , K. New York : Palgrave McMillan .
   Google Scholar
• Anselin , L. 2010 . Thirty years of spatial econometrics . Papers in Regional Science , 89 ( 1 ) : 3 – 25 .
   Web of Science ®Google Scholar
• Anselin , L. From SpaceStat to CyberGIS, twenty years of spatial data analysis software . International Regional Science Review (35) .
   Google Scholar
• Anselin , L. and Hudak , S. 1992 . Spatial econometrics in practice: a review of software options . Regional Science and Urban Economics , 22 : 509 – 536 .
   Web of Science ®Google Scholar
• Anselin , L. and McCann , M. OpenGeoDa, open source software for the exploration and visualization of geospatial data . Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems . November 4–6 2009 , New York . pp. 550 – 551 . Seattle , WA : ACM .
   Google Scholar
• Anselin , L. and Smirnov , O. 1996 . Efficient algorithms for constructing proper higher order spatial lag operators . Journal of Regional Science , 36 : 67 – 89 .
   Web of Science ®Google Scholar
• Anselin , L. , Syabri , I. and Kho , Y. 2006 . GeoDa: an introduction to spatial data analysis . Geographical Analysis , 38 : 5 – 22 .
   Web of Science ®Google Scholar
• Systems , Aptech . 2011 . GAUSS user guide , Black Diamond , WA : Aptech Systems .
   Google Scholar
• Armbrust , M. 2009 . Above the clouds: a Berkeley view of cloud computing , Berkeley : EECS Department, University of California, Technical Report UCB/EECS-2009-28 .
   Google Scholar
• Belsley , D. and Kontoghiorghes , E. 2009 . Handbook of computational econometrics , New York : Wiley .
   Google Scholar
• Beron , K.J. and Vijverberg , W.P. 2004 . “ Probit in a spatial context: a Monte Carlo analysis ” . In Advances in spatial econometrics , Edited by: Anselin , L. , Florax , R.J. and Rey , S.J. 169 – 195 . Heidelberg : Springer .
   Google Scholar
• Bivand , R. 2002 . Spatial econometrics functions in R: classes and methods . Journal of Geographical Systems , 4 ( 4 ) : 405 – 421 .
   Google Scholar
• Bivand , R.S. 2006 . Implementing spatial data analysis software in R . Geographical Analysis , 38 : 23 – 40 .
   Web of Science ®Google Scholar
• Bivand , R.S. , Pebesma , E.J. and Gómez-Rubio , V. 2008 . Applied spatial data analysis with R , New York : Springer .
   Google Scholar
• Breslaw , J. 1993 . GAUSSX: desktop econometric analysis for GAUSS , Montreal : Econotron Software .
   Google Scholar
• Buchinsky , M. and Hahn , J. 1998 . An alternative estimator for the censored quantile regression model . Econometrica , 66 : 653 – 671 .
   Web of Science ®Google Scholar
• Cahill , I. 2001 . Maximum likelihood estimation in C++ User's manual, version 2.0 , Ottawa : Cahill Software .
   Google Scholar
• Chebotko , A. , Simmhan , Y. and Missier , P. 2011 . Guest editorial: scientific workflows, provenance and their application . International Journal of Computers and Their Applications , 18 ( 3 ) : 130 – 132 .
   Google Scholar
• Creel , M. 2005 . User-friendly parallel computations with econometric examples . Computational Economics , 26 ( 2 ) : 107 – 128 .
   Google Scholar
• Davidson , R. and MacKinnon , J.G. 1992 . Regression-based methods for using control variates in Monte Carlo experiments . Journal of Econometrics , 54 : 203 – 222 .
   Web of Science ®Google Scholar
• Deelman , E. 2009 . Workflows and e-Science: an overview of workflow system features and capabilities . Future Generation Computer Systems , 25 ( 5 ) : 528 – 540 .
   Web of Science ®Google Scholar
• Doornik , J.A. , Hendry , D.F. and Sheppard , N. 2002 . Computationally-intensive econometrics using a distributed-matrix-programming language . Philosophical Transactions of the Royal Society of London, Series A , 360 : 1245 – 1266 .
   Web of Science ®Google Scholar
• Doornik , J. and Ooms , M. 2001a . A package for estimating, forecasting and simulating ARFIMA models: ARFIMA package 1.0 for Ox , London : Timberlake Consultants Press .
   Google Scholar
• Doornik , J. and Ooms , M. 2001b . Introduction to Ox: Ox version 3 , London : Timberlake Consultants Press .
   Google Scholar
• Drukker , D.M. 2011a . Creating and managing spatial-weighting matrices using the spmat command , College Station , TX : Stata Corp, Technical report .
   Google Scholar
• Drukker , D.M. , Prucha , I.R. and Raciborski , R. 2011b . A command for estimating spatial-autoregressive models with spatial-autoregressive disturbances and additional endogenous variables , College Station , TX : Stata Corp, Technical report .
   Google Scholar
• Drukker , D.M. , Prucha , I.R. and Raciborski , R. 2011c . Maximum-likelihood and generalized spatial two-stage least-squares estimators for a spatial-autoregressive model with spatial-autoregressive disturbances , College Station , TX : Stata Corp, Technical report .
   Google Scholar
• Estima . 2011 . RATS (Regression Analysis of Time Series) , Evanston , IL : Estima .
   Google Scholar
• Florax , R.J.G.M. , Folmer , H. and Rey , S.J. 2003 . Specification searches in spatial econometrics: the relevance of Hendry's methodology . Regional Science and Urban Economics , 33 : 557 – 579 .
   Web of Science ®Google Scholar
• Fotheringham , A.S. , Brunsdon , C. and Charlton , M. 2002 . Geographically weighted regression , Chichester : Wiley .
   Google Scholar
• Gentleman , R. and Temple Lang , D. 2007 . Statistical analyses and reproducible research . Journal of Computational and Graphical Statistics , 16 ( 1 ) : 1 – 23 .
   Web of Science ®Google Scholar
• Geweke , J. 2005 . Contemporary Bayesian econometrics and statistics , New York : Wiley .
   Google Scholar
• Goldfeld , S. and Quandt , R. 1972 . Nonlinear methods in econometrics , London : North-Holland .
   Google Scholar
• Goodchild , M.F. 1992 . Geographical information science . International Journal of Geographical Information Science , 6 ( 1 ) : 31 – 45 .
   Web of Science ®Google Scholar
• Goodchild , M.F. 2010a . Twenty years of progress: GIScience in 2010 . Journal of Spatial Information Science , 1 : 3 – 20 .
   Google Scholar
• Goodchild , M.F. 2010b . “ Whose hand on the tiller? Revisiting ‘spatial statistical analysis and GIS’ ” . In Perspectives on spatial analysis , Edited by: Anselin , L. and Rey , S.J. 49 – 59 . Heidelberg : Springer .
   Google Scholar
• Goodchild , M.F. 2000 . Toward spatially integrated social science . International Regional Science Review , 23 ( 2 ) : 139 – 159 .
   Web of Science ®Google Scholar
• Gourieroux , M. 1997 . Simulation based econometric methods , Cambridge : Cambridge University Press .
   Google Scholar
• Greene , W. 1980 . Maximum likelihood estimation of econometric frontier functions . Journal of Econometrics , 13 ( 1 ) : 27 – 56 .
   Web of Science ®Google Scholar
• Greene , W.H. 2007 . LIMDEP 9 user's guide , Plainview , TX : Econometric Software .
   Google Scholar
• Hahn , J. and Newey , W.K. 2004 . Jackknife and analytical bias reduction for nonlinear panel models . Econometrica , 72 : 1295 – 1319 .
   Web of Science ®Google Scholar
• Hajivassilou , V. 2008 . “ Computational methods in econometrics ” . In The new Palgrave dictionary of econometrics , Edited by: Durlauf , S. and Blume , L. Basingstoke : Palgrave .
   Google Scholar
• Harris , R. 2010 . Grid-enabling geographically weighted regression: a case study of participation in higher education in England . Transactions in GIS , 14 : 43 – 61 .
   Web of Science ®Google Scholar
• Harrison , T. and Renfro , C. 2004 . TSD and TSX: a proposal for an extended time series data interchange and transfer format . Journal of Economic and Social Measurement , 29 : 339 – 358 .
   Google Scholar
• Harvey , A. and Oryschenko , V. 2010 . Kernel density estimation for time series data , Cambridge : University of Cambridge, Technical report .
   Google Scholar
• Hendry , D.F. 1984 . “ Monte Carlo experimentation in econometrics ” . In Handbook of econometrics , Edited by: Griliches , Z. and Intriligator , M. Vol. 2–3 , 937 – 976 . Amsterdam : North-Holland .
   Google Scholar
• Jacho-Chávez , D. and Trivedi , P. 2009 . “ Computational considerations in empirical microeconometrics: selected examples ” . In Palgrave handbook of econometrics: Volume 2, applied econometrics , Edited by: Mills , T.C. and Patterson , K. Vol. 2 , 775 – 817 . Basingstoke : Palgrave .
   Google Scholar
• Koenker , R. and Hallock , K. 2001 . Quantile regression . The Journal of Economic Perspectives , 15 ( 4 ) : 143 – 156 .
   Web of Science ®Google Scholar
• LeSage , J.P. 1997 . Bayesian estimation of spatial autoregressive models . International Regional Science Review , 20 : 113 – 129 .
   Web of Science ®Google Scholar
• LeSage , J.P. 1999 . Applied econometrics using MATLAB , University of Toledo, Technical report .
   Google Scholar
• LeSage , J.P. and Pace , R.K. 2009 . Introduction to spatial econometrics , Boca Raton , FL : CRC Press .
   Google Scholar
• MacKinnon , J. 2006 . Bootstrap methods in econometrics . Economic Record , 82 : S2 – S18 .
   Web of Science ®Google Scholar
• MathSoft . 1996 . S+SpatialStats user's manual for windows and unix , Seattle , WA : MathSoft .
   Google Scholar
• McCausland , W. 2004 . Using the BACC software for Bayesian inference . Computational Economics , 23 ( 3 ) : 201 – 218 .
   Google Scholar
• Nagurney , A. 1996 . “ Parallel computation ” . In Handbook of computational econometrics , Edited by: Amman , H. , Kendrik , D. and Rust , J. Amsterdam : North-Holland .
   Google Scholar
• NSF . 2003 . Revolutionizing science and engineering through cyberinfrastructure, Report of the National Science foundation blue-ribbon Advisory Panel on Cyberinfrastructure , Washington , DC : National Science Foundation .
   Google Scholar
• Ord , J.K. 1975 . Estimation methods for models of spatial interaction . Journal of the American Statistical Association , 70 : 120 – 126 .
   Web of Science ®Google Scholar
• Pace , R.K. and LeSage , J.P. 2004 . Chebyshev approximation of log-determinants of spatial weights matrices . Computational Statistics and Data Analysis , 45 : 179 – 196 .
   Web of Science ®Google Scholar
• Pagan , A. and Ullah , A. 1999 . Nonparametric econometrics , Cambridge : Cambridge University Press .
   Google Scholar
• Piras , G. 2010 . Sphet: spatial models with heteroskedastic innovations in R . Journal of Statistical Software , 35 : 1 – 21 .
   PubMed Web of Science ®Google Scholar
• Plasmans , J. 2006 . Modern linear and nonlinear econometrics , Berlin : Springer .
   Google Scholar
• Quantitative Micro Software . 2010 . Eviews 7 user's guide , Irvine , CA : Quantitative Micro Software .
   Google Scholar
• Renfro , C. 2004a . Computational econometrics: its impact on the development of quantitative economics , Landsdale : IOS Press .
   Google Scholar
• Renfro , C. 2004b . Econometric software: the first fifty years in perspective . Journal of Economic and Social Measurement , 29 : 9 – 108 .
   Google Scholar
• Rey , S.J. 2009 . Show me the code: spatial analysis and open source . Journal of Geographical Systems , 11 : 191 – 207 .
   Web of Science ®Google Scholar
• Rey , S.J. and Anselin , L. 2010 . “ PySAL: a Python library of spatial analytical methods ” . In Handbook of applied spatial analysis , Edited by: Fischer , M.M. and Getis , A. 175 – 193 . Berlin : Springer .
   Google Scholar
• Rose , C. and Smith , M. 2002 . Mathematical statistics with Mathematica , New York : Springer .
   Google Scholar
• Schmidberger , M. 2009 . State-of-the-art in parallel computing with R . Journal of Statistical Software , 31 ( 1 )
   Web of Science ®Google Scholar
• Schrage , L. 2001 . Optimization modeling with LINGO , Chicago , IL : LINDO Systems .
   Google Scholar
• Smirnov , O. 2005 . Computation of the information matrix for models with spatial interaction on a lattice . Journal of Computational and Graphical Statistics , 14 : 910 – 927 .
   Web of Science ®Google Scholar
• Smirnov , O. and Anselin , L. 2001 . Fast maximum likelihood estimation of very large spatial autoregressive models: a characteristic polynomial approach . Computational Statistics and Data Analysis , 35 : 301 – 319 .
   Web of Science ®Google Scholar
• Smirnov , O. and Anselin , L. 2009 . An O(N) parallel method of computing the Log-Jacobian of the variable transformation for models with spatial interaction on a lattice . Computational Statistics and Data Analysis , 53 : 2980 – 2988 .
   Web of Science ®Google Scholar
• StataCorp . 2011 . Stata statistical software: release 12 , College Station , TX : StataCorp .
   Google Scholar
• Train , K.E. 2003 . Discrete choice methods with simulation , Cambridge : Cambridge University Press .
   Google Scholar
• Wand , M. and Jones , M. 1995 . Kernel smoothing , Boca Raton , FL : Chapman & Hall/CRC Press .
   Google Scholar
• Wang , S. 2010 . A cyberGIS framework for the synthesis of cyberinfrastructure, GIS, and spatial analysis . Annals of the Association of American Geographers , 100 ( 3 ) : 535 – 557 .
   Web of Science ®Google Scholar
• Wang , S. and Armstrong , M.P. 2009 . A theoretical approach to the use of cyberinfrastructure in geographical analysis . International Journal of Geographical Information Science , 23 : 169 – 193 .
   Web of Science ®Google Scholar
• Wang , S. and Liu , Y. 2009 . TeraGrid GIScience gateway: bridging cyberinfrastructure and GIScience . International Journal of Geographical Information Science , 23 : 631 – 656 .
   Web of Science ®Google Scholar
• Wang , S. , Cowles , M. and Armstrong , M.P. 2008 . Grid computing of spatial statistics: using the terraGrid for Gi*(d) analysis . Concurrency and Computation: Practice and Experience , 20 : 1697 – 1720 .
   Web of Science ®Google Scholar
• Wang , S. 2009 . SimpleGrid toolkit: enabling geosciences gateways to cyberinfrastructure . Computers and Geosciences , 35 : 2283 – 2294 .
   Web of Science ®Google Scholar
• Wright , D. and Wang , S. 2011 . The emergence of spatial cyberinfrastructure . Proceedings of the National Academy of Sciences of the United States of America , 108 ( 14 ) : 5488
   PubMed Web of Science ®Google Scholar
• Yalta , A.T. and Yalta , A.Y. 2010 . Should economists use open source software for doing research? . Computational Economics , 35 : 371 – 394 .
   Web of Science ®Google Scholar
• Yan , J. 2007 . Parallelizing MCMC for Bayesian spatiotemporal statistical models . Statistics and Computing , 17 : 323 – 335 .
   Web of Science ®Google Scholar
• Yang , C. and Raskin , R. 2009 . Introduction to distributed geographic information processing research . International Journal of Geographical Information Science , 23 : 553 – 560 .
   Web of Science ®Google Scholar
• Yang , C. 2008 . Distributed geospatial information processing: sharing distributed geospatial resources to support Digital Earth . International Journal of Digital Earth , 1 : 259 – 278 .
   Web of Science ®Google Scholar
• Yang , C. 2010 . Geospatial cyberinfrastructure: past, present and future . Computers, Environment and Urban Systems , 34 ( 4 ) : 264 – 277 .
   Web of Science ®Google Scholar
• Yue , P. 2011 . Sharing geospatial provenance in a service-oriented environment . Computers, Environment and Urban Systems , 35 : 333 – 343 .
   Web of Science ®Google Scholar
• Zeileis, A., 2011. CRAN task view: computational econometrics [online]. WU Wien: The R Project for Statistical Computing. http://cran.r-project.org/web/views/econometrics.html (http://cran.r-project.org/web/views/econometrics.html) (Accessed: 4 November 2011 ).
   Google Scholar