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dc.contributor.authorOlejnik, Alicja
dc.contributor.authorOlejnik, Jakub
dc.date.accessioned2021-06-10T07:29:51Z
dc.date.available2021-06-10T07:29:51Z
dc.date.issued2020
dc.identifier.citationOlejnik A,, Olejnik J., Metody stochastyczne w ekonometrii przestrzennej – nowoczesna analiza asymptotyczna, WUŁ, Łódź 2020, https://doi.org/10.18778/8220-438-4pl_PL
dc.identifier.isbn978-83-8220-438-4
dc.identifier.urihttp://hdl.handle.net/11089/36137
dc.description.abstractW monografii zostały zaprezentowane najnowsze i w dużej mierze autorskie osiągnięcia z zakresu teorii asymptotycznych stochastycznych modeli ekonometrii przestrzennej. Rezultaty pracy naukowej autorów zostały poprzedzone przeglądem klasycznych, choć przedstawionych w nowoczesnym ujęciu, zagadnień ekonometrii przestrzennej. Ważnym elementem omawianej teorii jest nowe Centralne Twierdzenie Graniczne dla form liniowo-kwadratowych .Pozwala ono na przeprowadzanie formalnych dowodów własności granicznych statystyk testowych autokorelacji przestrzennej oraz estymatorów parametrów modeli ekonometrycznych z zależnościami przestrzennymi.pl_PL
dc.language.isoplpl_PL
dc.publisherWydawnictwo Uniwersytetu Łódzkiegopl_PL
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Międzynarodowe*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectekonometria przestrzennapl_PL
dc.subjectmetody stochastycznepl_PL
dc.subjectmodelowanie przestrzennepl_PL
dc.subjectautoregresja przestrzennapl_PL
dc.subjecttesty statystycznepl_PL
dc.subjectcentralne twierdzenie granicznepl_PL
dc.titleMetody stochastyczne w ekonometrii przestrzennej – nowoczesna analiza asymptotycznapl_PL
dc.typeBookpl_PL
dc.page.number168pl_PL
dc.contributor.authorAffiliationUniwersytet Łódzki, Wydział Ekonomiczno-Socjologiczny, Instytut Gospodarki Przestrzennej, Katedra Ekonometrii Przestrzennejpl_PL
dc.contributor.authorAffiliationUniwersytet Łódzki, Wydział Matematyki i Informatyki, Katedra Informatyki Stosowanejpl_PL
dc.identifier.eisbn978-83-8220-437-7
dc.referencesAnselin L. (1988a), Spatial Econometrics: Methods and Models, Kluwer Academic Publications, Dordrecht.pl_PL
dc.referencesAnselin L. (1988b), Lagrange multiplier test diagnostics for spatial dependence and spatial heterogeneity, „Geographical Analysis” 20: 1–17.pl_PL
dc.referencesAnselin L. (1996), The Moran Scatterplot as an ESDA Tool to Assess Local Instability in Spatial Association, [w:] M. Fischer, H. Scholten, D. Unwin (eds.), Spatial Analytical Perspectives on GIS in Environmental and Socio-Economic Sciences, Taylor and Francis, London, s. 111–125.pl_PL
dc.referencesAnselin L. (2001), Rao’s score test in spatial econometrics, „Journal of Statistical Planning and Inference” 97: 113–139.pl_PL
dc.referencesAnselin L. (2002), Under the hood: issues in the specification and interpretation of spatial regression models, „Agricultural Economics” 27: 247–267.pl_PL
dc.referencesAnselin L., Bera A. K. (1998), Spatial Dependence in Linear Regression Models with an Introduction to Spatial Econometrics, [w:] A. Ullah, D. E. A. Giles (eds.), Handbook of Applied Economic Statistics, Marcel Dekker, Inc., New York, s. 237–289.pl_PL
dc.referencesAnselin L., Rey S. J. (2014), Modern Spatial Econometrics in Practice: A Guide to GeoDa, GeoDaSpace and PySAL, GeoDa Press LLC, Chicago.pl_PL
dc.referencesArbia G. (1989), Spatial Data Configuration in Statistical Analysis of Regional Economic and Related Problems, Kluwer Academic Publishers, Boston.pl_PL
dc.referencesArbia G. (2006), Spatial Econometrics: Statistical Foundations and Applications to Regional Convergence, Advances in Spatial Science, Springer, Berlin.pl_PL
dc.referencesArbia G., Baltagi B. H. (eds.) (2009), Spatial Econometrics. Methods and Applications, Springer, Berlin.pl_PL
dc.referencesBadinger H., Egger P. (2013), Estimation and testing of higher-order spatial autoregressive panel data error component models, „Journal of Geographical Systems” 15 (4): 453–489.pl_PL
dc.referencesBaltagi B. H., Lesage J. P., Pace R. K. (2016), Spatial Econometrics: Qualitative and Limited Dependent Variables, „Advances in Econometrics” 37, Emerald.pl_PL
dc.referencesBeran J. (1972), Rank spectral processes and tests for serial dependence, „Annals of Mathematical Statistics” 43: 1749–1766.pl_PL
dc.referencesBesner C. (2002), A Spatial Autoregressive Specification with a Comparable Sales Weighting Scheme, „Journal of Real Estate Research” 24: 193–212.pl_PL
dc.referencesBhansali R. J., Giraitis L., Kokoszka P. S. (2007), Convergence of quadratic forms with non-vanishing diagonal, „Statistics and Probability Letters” 77: 726–734.pl_PL
dc.referencesBillingsley P. (2009), Prawdopodobieństwo i miara, przeł. K. Kizeweter, J. E. Roguski, wyd. 2, Wydawnictwo Naukowe PWN, Warszawa.pl_PL
dc.referencesBivand R., Hauke J., Kossowski T. (2013), Computing the Jacobian in Gaussian Spatial Autoregressive Models: An Illustrated Comparison of Available Methods, „Geographical Analysis” 45 (2): 150–179.pl_PL
dc.referencesBodson P., Peeters D. (1975), Estimations of the coefficients in a linear regression in the presence of spatial autocorrelation: an application to a Belgian labour–demand function. „Environment and Planning” 7 (4): 455–472.pl_PL
dc.referencesBorn B., Breitung J. (2011), Simple regression‐based tests for spatial dependence, „The Econometrics Journal” 14 (2): 330–342.pl_PL
dc.referencesCliff A. D., Ord J. K. (1972), Testing for spatial autocorrelation among regression residuals, „Geographical Analysis” 4: 267–284.pl_PL
dc.referencesCliff A. D., Ord J. K. (1973), Spatial Autocorrelation, Pion, London.pl_PL
dc.referencesCliff A. D., Ord J. K. (1981), Spatial Processes: Models and Applications, Pion, London.pl_PL
dc.referencesCorrado L., Fingleton B. (2011), Where is the economics in spatial econometrics?, „Journal of Regional Science” 52 (2): 210–239.pl_PL
dc.referencesCramér H., Wold H. (1936), Some Theorems on Distribution Functions, „Journal of the London Mathematical Society” 11 (4): 290–294.pl_PL
dc.referencesDacey M. F. (1968), A review on measures of contiguity for two and k–color maps. Technical Report No. 2, „Spatial Diffusion Study”, Department of Geography, Evanston, Northwestern University.pl_PL
dc.referencesde Jong P. (1987), A central limit theorem for generalized quadratic forms, „Probability Theory and Related Fields” 75: 261–277.pl_PL
dc.referencesde Jong P., Sprenger C., van Veen F. (1984), On Extreme Values of Moran’s I and Geary’s c, „Geographical Analysis” 16 (1): 17–24.pl_PL
dc.referencesDeng M. (2008), An anisotropic model for spatial processes, Geographical Analysis 40 (1): 26–51.pl_PL
dc.referencesDurbin J., Watson G. S. (1950), Testing for serial correlation in least-squares regression I, „Biometrika” 37: 159–178.pl_PL
dc.referencesDurbin J., Watson G. S. (1951), Testing for serial correlation in least-squares regression II, „Biometrika” 38: 409–428.pl_PL
dc.referencesElhorst J. P. (2001), Dynamic models in space and time, „Geographical Analysis” 33 (2): 119–140.pl_PL
dc.referencesElhorst J. P., Halleck S. (2013), On spatial econometric models, spillover effects, and W, 53rd Congress of the European Regional Science Association: „Regional Integration: Europe, the Mediterranean and the World Economy”, 27–31 August 2013, Palermo, Italy, http://hdl.handle.net/10419/123888 (dostęp 27.11.2020).pl_PL
dc.referencesElhorst J. P., Lacombe D. J., Piras G. (2012), On model specification and parameter space definitions in higher order spatial econometric models, „Regional Science and Urban Economics” 42 (1–2): 211–220.pl_PL
dc.referencesFeng C., Wang H., Han Y., Xia Y., Tu, X. M. (2014), The Mean Value Theorem and Taylor’s Expansion in Statistics. „The American Statistician” 67: 245–248.pl_PL
dc.referencesFingleton B. (1999), Spurious spatial regression: some Monte Carlo results with spatial unit Root and Spatial Co-integration, „Journal of Regional Science” 39: 1–19.pl_PL
dc.referencesFisher W. (1971), Econometric estimation with spatial dependence, „Regional and Urban Economics” 1: 19–40.pl_PL
dc.referencesFlorax R. J. G. M., Anselin L. (1995), New Directions in Spatial Econometrics, Springer, Berlin.pl_PL
dc.referencesFlorax R. J. G. M., Anselin L. (2004), Advances in Spatial Econometrics: Methodology, Tools and Applications, Springer, Berlin.pl_PL
dc.referencesFujita M., Krugman P., Mori T. (1999a), On the evolution of hierarchical urban systems, „European Economic Review” 43 (2): 209–251.pl_PL
dc.referencesFujita M., Krugman P., Venables A. J. (1999b), The spatial economy: Cities, regions and international trade. MIT Press, Cambridge MA.pl_PL
dc.referencesGetis A., Aldstadt J. (2004), Constructing the Spatial Weights Matrix Using A Local Statistic, „Geographical Analysis” 36: 90–114.pl_PL
dc.referencesGetis A., Ord J. K. (1992), The analysis of spatial association by distance statistics, „Geographical Analysis” 24: 189–206.pl_PL
dc.referencesGiraitis L., Taqqu M. (1998), Central limit theorems for quadratic forms with time-domain conditions, „Annals of Probability” 26: 377–398.pl_PL
dc.referencesGriffith D. A. (2003), Spatial Autocorrelation and Spatial Filtering. Gaining Understanding Through Theory and Scientific Visualization, Springer.pl_PL
dc.referencesGupta A., Robinson P. (2015), Inference on higher-order spatial autoregressive models with increasingly many parameters, „Journal of Econometrics” 186: 19–31.pl_PL
dc.referencesGupta A., Robinson P. (2018), Pseudo maximum likelihood estimation of spatial autoregressive models with increasing dimension, „Journal of Econometrics” 202: 92–107.pl_PL
dc.referencesHájek P., Johanis M. (2014), Smooth analysis in Banach spaces, De Gruyter Series in Nonlinear Analysis and Applications 19, De Gruyter, Berlin.pl_PL
dc.referencesHall P., Hyde C. C. (1980), Martingale limit theory and its application, Academic Press, Inc., New York.pl_PL
dc.referencesHan X., Hsieh C., Lee L. F. (2017), Estimation and model selection of higher-order spatial autoregressive model: An efficient Bayesian approach, „Regional Science and Urban Economics” 63: 97–120.pl_PL
dc.referencesHordijk L. (1974), Spatial correlation in the disturbances of a linear interregional model, „Regional Science and Urban Economics” 4 (3): 117–140.pl_PL
dc.referencesHorn R. A., Johnson C. R. (2013), Matrix Analysis, Cambridge University Press, New York.pl_PL
dc.referencesJakubowski J., Sztencel R. (2001), Wstęp do teorii prawdopodobieństwa, SCRIPT, Warszawa.pl_PL
dc.referencesKelejian H. H., Piras G. (2014), Estimation of spatial models with endogenous weighting matrices, and an application to a demand model for cigarettes, „Regional Science and Urban Economics” 46: 140–149.pl_PL
dc.referencesKelejian H. H., Piras G. (2017), Spatial Econometrics, Academic Press, London.pl_PL
dc.referencesKelejian H. H., Prucha I. R. (1998), A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances, „Journal of Real Estate Finance and Economics” 17: 99–121.pl_PL
dc.referencesKelejian H. H., Prucha I. R. (2001), On the Asymptotic Distribution of the Moran I Test Statistic with Applications, „Journal of Econometrics” 104: 219–257.pl_PL
dc.referencesKelejian H. H., Prucha I. R. (2010), Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances, „Journal of Econometrics” 157 (1): 53–67.pl_PL
dc.referencesKlaassen L. H., Paelinck J. H. P., Wagenaar S. (1979) Spatial Systems, Saxon House, Farnborough.pl_PL
dc.referencesKopczewska K. (2007), Ekonometria i statystyka przestrzenna z wykorzystaniem programu R CRAN, CeDeWu, Warszawa.pl_PL
dc.referencesKosfeld R., Lauridsen J. (2004), Dynamic spatial modeling of regional convergence processes, „Empirical Economics” 29: 705–722.pl_PL
dc.referencesKosfeld R., Lauridsen J. (2006), A test strategy for spurious regression, spatial nonstationarity, and spatial co-integration, „Papers in Regional Science” 85 (3): 363–377.pl_PL
dc.referencesKossowski T. (2010), Teoretyczne aspekty modelowania przestrzennego w badaniach regionalnych, „Rozwój Regionalny i Polityka Regionalna” 12: 9–26.pl_PL
dc.referencesKossowski T., Hauke J. (2011), The method of computing the Log-Jacobian of the variable transformation for spatial models – test and comments, „Acta Universitatis Lodziensis”. Folia Oeconomica 252: 161–173.pl_PL
dc.referencesKrugman P. (1991a), Increasing returns and economic geography. „Journal of Political Economy” 99 (3): 483–499.pl_PL
dc.referencesKrugman P. (1991b), Geography and Trade. MIT Press.pl_PL
dc.referencesLauridsen J. (1999), Spatial Co-integration Analysis in Econometric Modelling, „ERSA Conference Papers” ersa99pa181, European Regional Science Association.pl_PL
dc.referencesLauridsen J. (2006), Spatial autoregressively distributed lag models: equivalent forms, estimation and an illustrative commuting model, „The Annals of Regional Science” 40: 297–311.pl_PL
dc.referencesLa Vallée Poussin C. de (1915), Sur L’Integrale de Lebesgue, „Transactions of the American Mathematical Society” 16 (4): 435–501.pl_PL
dc.referencesLee L. F. (2002), Consistency and efficiency of least-squares estimation for mixed regressive spatial autoregressive models, „Econometric Theory” 18: 252–277.pl_PL
dc.referencesLee L. F. (2004), Asymptotic Distributions of Maximum Likelihood Estimators for Spatial Autoregressive Models, „Econometrica” 72: 1899–1925.pl_PL
dc.referencesLee L. F., Yu J. (2010), Estimation of spatial autoregressive panel data models with fixed effects, „Journal of Econometrics” 154 (2): 165–168.pl_PL
dc.referencesLee L. F., Liu X., Lin X. (2010), Specification and estimation of social interaction models with network structures. „The Econometrics Journal” 13 (2): 145–176.pl_PL
dc.referencesLehmann E. L., Casella G. (1998), Theory of Point Estimation, 2nd ed., Springer, New York.pl_PL
dc.referencesLeSage J. (1999), Spatial Econometrics: The Web Book of Regional Science, Regional Research Institute, West Virginia University, Morgantown.pl_PL
dc.referencesLeSage J., Pace, R. K. (2009), Introduction to Spatial Econometrics, Statistics: Textbooks and Monographs, Chapman and Hall, Boca Raton, Florida.pl_PL
dc.referencesLi K. (2017), Fixed-effects dynamic spatial panel data models and impulse response analysis, „Journal of Econometrics” 198 (1): 102–121.pl_PL
dc.referencesLiu S. F., Yang Z. (2015), Modified QML estimation of spatial autoregressive models with unknown heteroskedasticity and non-normality, „Regional Science and Urban Economics” 52: 50–70.pl_PL
dc.referencesŁaszkiewicz E. (2016), Ekonometria przestrzenna III. Modele wielopoziomowe – teoria i zastosowania, C.H. Beck, Warszawa.pl_PL
dc.referencesMeyer P. A. (1966), Probability and potentials, Blaisdell Publishing Co., Waltham, Mass.pl_PL
dc.referencesMoran P. (1950), Notes on Continuous Stochastic Phenomena, „Biometrika” 37: 17–23.pl_PL
dc.referencesMynbaev K. T. (2010), Asymptotic distribution of the OLS estimator for a mixed spatial model, „Journal of Multivariate Analysis” 101 (3): 733–748.pl_PL
dc.referencesMynbaev K. T. (2011), Short-Memory Linear Processes and Econometric Applications, John Wiley and Sons, Hoboken, NJ.pl_PL
dc.referencesMynbaev K. T., Ullah A. (2008), Asymptotic distribution of the OLS estimator for a purely autoregressive spatial model, „Journal of Multivariate Analysis” 99 (2): 245–277.pl_PL
dc.referencesNijkamp P., Fischer M. M. (eds.) (2014), Handbook of Regional Science, Springer, Heidelberg.pl_PL
dc.referencesOlejnik A. (2008), Using the spatial autoregressively distributed lag model in assessing the regional convergence of per-capita income in the EU25, „Papers in Regional Science” 87 (3): 371–384.pl_PL
dc.referencesOlejnik A. (2013), Wybrane metody testowania modeli regresji przestrzennej, „Przegląd Statystyczny” 60 (3): 381–393.pl_PL
dc.referencesOlejnik A., Olejnik J. (2019), Increasing returns to scale, productivity and economic growth – a spatial analysis of the contemporary EU economy, „Argumenta Oeconomica” 42 (1): 273–293.pl_PL
dc.referencesOlejnik J., Olejnik A. (2020), QML estimation with non-summable weight matrices, „Journal of Geographical Systems” 22 (4): 469–495.pl_PL
dc.referencesOlejnik A., Özyurt S., Olejnik J. (2020), Introducing multi-dimensional weighting factors into spatial econometric models, „Ekonomika Regiona / Economy of Region” [w recenzji].pl_PL
dc.referencesOrd J. K., Getis A. (1995), Local spatial autocorrelation statistics: distributional issues and an application, „Geographical Analysis” 27 (4): 286–306.pl_PL
dc.referencesPaelinck J. H. P., Klaassen L. H. (1979), Spatial Econometrics, Saxon House, Farnborough.pl_PL
dc.referencesPanak [Olejnik] A. (2006), Autokorelacja przestrzenna i kriging – metodologia i wybrane zastosowania, „Prace Naukowe Akademii Ekonomicznej im. Oskara Langego we Wrocławiu”, Taksonomia 13: 483–491.pl_PL
dc.referencesPinkse J. (1999), Asymptotic properties of Moran and related tests and testing for spatial correlation in probit models, Department of Economics, University of British Columbia and University College, London.pl_PL
dc.referencesPötscher M. B., Prucha I. R. (1997), Dynamic Non-linear Models: Asymptotic Theory, Springer-Verlag, Berlin, Heidelberg.pl_PL
dc.referencesPruss A. R. (1998), A bounded N-tuplewise independent and identically distributed counterexample to the CLT, „Probability Theory and Related Fields” 111: 323–332.pl_PL
dc.referencesQu X., Lee L. F. (2017), QML estimation of spatial dynamic panel data models with endogenous time varying spatial weights matrices, „Journal of Econometrics” 197: 173–201.pl_PL
dc.referencesRao C. R. (1948), Large sample tests of statistical hypotheses concerning several parameters with applications to problems of estimation, „Proceeding of the Cambridge Philosophical Society” 40: 50–57.pl_PL
dc.referencesSen A. (1976), Large sample-size distribution of statistics used in testing for spatial correlation, „Geographical Analysis” 9: 175–184.pl_PL
dc.referencesShi W., Lee L. F. (2017), Spatial dynamic panel data models with interactive fixed effects, „Journal of Econometrics” 197: 173–201.pl_PL
dc.referencesSilvey S. D. (1959), The Lagrangian multiplier test, „Annals of Mathematical Statistics” 30: 389–407.pl_PL
dc.referencesSuchecki B. (red.) (2010), Ekonometria przestrzenna. Metody i modele analizy danych przestrzennych, C.H. Beck, Warszawa.pl_PL
dc.referencesSuchecki B. (red.) (2012), Ekonometria przestrzenna II. Modele zaawansowane, C.H. Beck, Warszawa.pl_PL
dc.referencesSzulc E. (2007), Ekonometryczna analiza wielowymiarowych procesów gospodarczych, Wydawnictwo Uniwersytetu Mikołaja Kopernika, Toruń.pl_PL
dc.referencesTakesaki M. (1979), Theory of operator algebras I, Springer-Verlag, New York.pl_PL
dc.referencesTobler W. (1970), A computer movie simulating urban growth in the Detroit region, „Economic Geography Supplement” 46: 234–240.pl_PL
dc.referencesWhittle P. (1964), On the convergence to normality of quadratic forms of independent variables, „Theory of Probability and Its Applications” 9, 103–108.pl_PL
dc.referencesVega S. H., Elhorst J. P. (2015), The SLX model, „Journal of Regional Science” 55 (3): 339–363.pl_PL
dc.referencesYu 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 (1): 118–134.pl_PL
dc.referencesZeliaś A., Grabiński T., Ludwiczak B., Malina A. (1991), Ekonometria przestrzenna, PWE, Warszawa.pl_PL
dc.identifier.doi10.18778/8220-438-4


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