All the material presented here, to the extent it is original, is available under CC-BY-SA. Parts build on joint tutorials with Edzer Pebesma.
9/11 (I) Spatial data representation, (II) Support+topology, input/output
10/11 (III) Coordinate reference systems, (IV) Visualization
11/11 (VI) Spatial autocorrelation, (preVII) Introducing Spatial Econometrics, project surgery
12/11 (VII) Spatial regression, (VIII) Spatial multilevel regression
13/11 (IX) Interpolation, point processes, project surgery, presentation
14/11 Presentations
In the same way that (Fujita, Krugman, and Venables 1999) begin their study of the spatial economy by looking at the antecedents of their subject, it is helpful to place spatial econometrics in its temporal and academic context. This context is sufficiently different from the contemporary setting that it may be hard to grasp the background for many of the features of spatial econometrics that came into being during its earlier years. Indeed, the ranges of topics that were studied in economics in the 1960’s and 1970’s differ markedly from those in focus today. If we can sketch the context within which spatial econometrics was created, and its methods developed, we should be able to illuminate choices made then which influence our understanding and application of spatial econometric methods.
Critics of the practice of spatial econometrics, such as (Gibbons and Overman 2012), appear to overlook these antecedents, and consequently judge the potential of the field on a partial, perhaps anachronistic, understanding, viewing phenomena with a history in ahistorical way. Since we are attempting to provide an introduction to applied spatial econometrics, we need to throw light on the original motivations and concerns of the first scholars engaged in the field. (Anselin 2010) indicates clearly and repeatedly [Anselin (1988); anselin:06; anselin:10a] that we should acknowledge Spatial Econometrics by (Paelinck and Klaassen 1979) of the Netherlands Economic Institute as our starting point, and so celebrates thirty years of spatial econometrics in 2009. This firm confirmation of the importance of Jean Paelinck’s contributions as scholar and community-builder is fully justified. We should then turn to the motivations given in (Paelinck and Klaassen 1979) to indicate which contextual factors were of importance at that time, and the breadth of the academic communities with which they were in contact.
In a recent short commentary, (Paelinck 2013) recalls his conviction, expressed in 1967, that “early econometric exercises \(\ldots\) relating only variables posessing the same regional index \(\ldots\) were inadequate to represent the correct spatial workings of the economy, which would then be reflected in the policy outcomes.” A year before, (Paelinck 2012) points to salient isomorphisms linking spatial regression models, simultaneous equation models and input-output models; these were known of and discussed in the early formative period of spatial econometrics. We will return in subsequent chapters to the ways in which spatial regression models may be specified, but for now, a simple presentation of these isomorphisms as perceived in the early period is sufficient:
\[ {\mathbf y} = {\mathbf A}{\mathbf y} + {\mathbf X}{\mathbf b} + {\mathbf \varepsilon} \]
is a spatial regression model where \({\mathbf A}\) is a matrix expressing the mutual first order spatial dependencies between regions — the similarity of this form and the Koyck distributed lag model is striking [Koyck (1954); Klein (1958); Griliches (1967)};
\[ {\mathbf A}{\mathbf y} + {\mathbf X}{\mathbf b} = {\mathbf \varepsilon} \]
is a simultaneous equation model where \({\mathbf A}\) is a matrix expressing the dependencies between the equations; and:
\[ {\mathbf y} = {\mathbf A}{\mathbf y} + {\mathbf f} \]
is an input-output model where \({\mathbf A}\) is a matrix of sectoral input-output coefficients, and \({\mathbf f}\) is final demand.
Input-output models, simultaneous equation models, and the importance of policy outcomes were all known intimately at the Netherlands Economic Institute at this time, and elsewhere among applied economists. The isomporhisms flowed from the known to the unknown, from the stuff of contemporary research and policy advice to doubts about the calibration of aspatial models, and on to what became termed spatial econometrics. If we compare these topics with those described for Regional Science by (Boyce 2004), we can see the outlines of research priorities at the time: including urban and regional models for planning, regional and interregional input-output models, transport and location models. During the 1960s and 1970s, many of these models were enhanced — matching needs for policy advice — to cover environmental questions, adding natural resources as inputs and pollution to outputs. Paelinck’s co-author in a key paper in spatial econometrics (Hordijk and Paelinck 1976), went on to work in environmental management and research.
Reading (Paelinck and Klaassen 1979), we see that the programme of research into the space economy undertaken at the Netherlands Economic Institute led first to the publication of (Paelinck and Nijkamp 1975), and then to (Klaassen, Paelinck, and Wagenaar 1979), published in the same year as Spatial Econometrics. All three books were published in the same series and appear to reflect the core concerns of economists at the Institute doing reasearch on regionalised national macro-economic models. The direct link to Jan Tinbergen is evident in the account of the context of economic research in the Netherlands given by (Theil 1964). If we take Paelinck at his word, he and his colleagues were aware that an aspatial regionalisation of national accounts, of input-output models, or transport models, might prejudice policy advice and outcomes through inadequate and inappropriate calibration.
(Klaassen, Paelinck, and Wagenaar 1979) is mainly concerned with model construction, while about a third of (Paelinck and Nijkamp 1975) is devoted to input-output analysis. Both books show sustained concern for economic measurement, especially of national accounts data, intersectoral transactions, and many other topics. Considerable attention is also paid to the data collection units, be they sectors or regions. The need to attempt to define regions that match the underlying economic realities was recognised clearly, and a key part of (Paelinck and Nijkamp 1975) is devoted to regionalisation, and the distinction between functional regions and homogeneous regional classifications is made. In the motivation for spatial econometric models given in (Paelinck and Klaassen 1979), consumption and investment in a region are modelled as depending on income both in the region itself and in its contiguous neighbours, termed a “spatial income-generating model.” It became important to be able to calibrate planning models of this kind to provide indications of the possible outcomes of alternative policy choices, hence the need for spatial econometrics.
Economic planning was widespread in Europe at the time, and was also central in the development of Regional Science, in particular input-output models; as (Boyce 2004) recounts, Walter Isard worked closely with Wassily Leontief. Operational and planning motivations for applied economics were unquestioned, as economists in the post-war period saw their role, beyond educating young economists, as providing rational foundations for economic policy. It is worth noting that Jean Paelinck participated actively in the Association de Science Régionale De Langue Française, becoming president in 1973–1976. The first president of the association was François Perroux, who had founded it with Walter Isard in 1961 (Bailly, Derycke, and Torre 2012).
Until the 1980s, it was not at all unusual to publish original results in other languages than English. French spatial economic research, for example (Ponsard 1983), while making little impact in Anglophone countries, was widely used in teaching and research elsewhere (Billot and Thisse 1992). They contrast, though, the “word wizardry of François Perroux with the rigour of Claude Ponsard” (Billot and Thisse 1992), echoing the views expressed by (Drèze 1964) with regard to the work of Perroux. Even if we accept that “word wizardry” deserves more rigour and recasting in normative and empirically testable forms, it is also part of the context within which spatial econometrics came into being. A reading of (Perroux 1950) is worthwhile, because it not only gives the reader a vignette of the context in the post-war period, but also provides a discussion of economic space, as opposed to banal, unreflected space — mere position — that has largely disappeared from our considerations.
The title of the journal: Regional and Urban Economics, Operational Methods , founded by Jean Paelinck in 1971, and which was renamed as Regional Science and Urban Economics in 1975 (Boyce 2004), points to the perceived importance of “operational methods”, a version of the term “operational theory and method” used in the title of (Paelinck and Nijkamp 1975). Spatial econometrics does not seem to have come into being as a set of estimation techniques as such, as perhaps we might think today, but rather as an approach addressing open research questions both in space economy and in the enhancement of interregional models to be used in offering policy advice.
Were motivations of this kind common during the 1960s and early 1970s? Not only was the spread of Regional Science extensive and firmly established (Boyce 2004), but public bodies were concerned to regionalise economic measurement and policy advice (Graham and Romans 1971). In Britain, Environment and Planning was started in 1969 with Alan Wilson as founding editor and published by Pion; he was assistant director at the Centre for Environmental Studies at this time before moving to the University of Leeds. In a recently published lecture series, (Wilson 2012) cites (Paelinck and Nijkamp 1975) as giving principles for contributions from economics to urban and regional analysis (Wilson 2000). The papers presented at annual Regional Science meetings were published in a series by Pion; the first number in the series included contributions by (Granger 1969) and (Cliff and Ord 1969).
In a contribution to a panel session at the 2006 annual meeting of the American Association of Geographers (co-panelists Luc Anselin and Daniel Griffith), Keith Ord pointed to the continued relevance of Granger’s remarks at the meeting almost fourty years earlier (Ord 2010); we will return to these concerns below. As noted by (Bivand 2008), communities of researchers working in and near mathematical and theoretical geography was more integrated in the pre-internet and pre-photocopier age than one might expect, with duplicated working papers prepared using stencils circulating rapidly between collaborating academic centres. Knowledge of the preliminary results of other researchers then fed through into rapid innovation in an exciting climate for those with access to these meetings and working papers.
There was considerable overlap between quantitative geography and regional science, so that work like (Cliff and Ord 1969) is cited by (Hordijk 1974), and was certainly known at the Netherlands Economic Institute several years earlier. Although it has not been possible to find out who participated in the August 1968 conference of the British and Irish Section of the Regional Science Association at which (Cliff and Ord 1969) was read, it was not unusual for members of other sections to be present, and to return home with bundles of duplicated papers. Up to the 1990s, presenters at conferences handed out copies of their papers, and conference participants posted home parcels of these hand-outs, indexed using the conference programme.
Leslie Hepple was among the more thorough scholars working on the underpinnings of spatial econometrics prior to the publication of (Paelinck and Klaassen 1979). His wide-ranging review (Hepple 1974) is cited by (Bartels and Hordijk 1977), again demonstrating the close links between those working in this field. We will be returning to the review paper, and to (Hepple 1976), which studies methods of estimation for spatial econometrics models in some depth, building on and extending (Ord 1975).
(Hepple 1974), like (Cliff and Ord 1973), saw no distinction between spatial statistics and the antecedents to spatial econometrics. Obviously, spatial econometrics was strongly influenced by the research tasks undertaken by regional and urban economists and regional scientists. As (Griffith and Paelinck 2007) point out, spatial statistics and spatial econometrics continue to share most topics of interests, with each also possessing shorter lists of topics that have been of less concern to the other. They advocate a “non-standard” spatial econometrics, which is inclusive to wider concerns. It seems appropriate in this context to mention the somewhat heterodox position taken by (McMillen 2010), who draws attention to the crucial issue of functional form, which he argues may well lie behind observed spatial autocorrelation; we will return to this in later chapters.
Having described some of the contextual issues suggesting how specific research concerns influenced how spatial econometrics came into being, it may now be helpful to turn to broader econometrics. It will become clearer that the research concerns of broader econometrics at that time, apart from spatial interdependence and interaction, were generally similar to those of spatial econometrics. Indeed, econometrics and the provision of national economic data for analysis, modelling, and for the provision of policy advice were intimately linked, as were mathematical economics and econometrics. The place of econometrics within economics, as (Sandmo 2011) shows, was a matter of some contention from the very beginning.
Both (Morgan 1990) and (Qin 1993) conclude their historical accounts of the beginnings of econometrics in pessimistic ways. Econometrics had begun by addressing a range of research topics, including the expression of economic theories in mathematical form, the building of operational econometric models, the exploration of testing and estimation techniques, and statistical data preparation. “By the 1950s the founding ideal of econometrics, the union of mathematical and statistical economics into a truly synthetic economics, had collapsed” (Morgan 1990). The flavour of (Paelinck and Klaassen 1979) seems somewhat anachronistic compared to late 1950s and early 1960s “textbook” econometrics. One might conclude that while spatial econometrics was aligned with Haavelmo’s probabilistic revolution (Qin 1993), it retained, like Haavelmo, adherence to the founding ideals [Bjerkholt (2007); hendry+johansen:12].
It appears from the research programme culminating in (Paelinck and Klaassen 1979) that spatial econometrics could be seen as “creative juggling in which theory and data came together to find out about the real world” (Morgan 1990). (Morgan 1990) and (Qin 1993) indicate that, from the 1950s, the depth of econometrics was flattened , with “[D]ata taken less seriously as a source of ideas and information for econometric models, and the theory-development role of applied econometrics was downgraded to the theory-testing role” (Morgan 1990). The history of econometric ideas is of assistance in illuminating key components of what has come to be the practice of spatial econometrics:
With Haavelmo, the profession seemed to have arrived at a consensus: a statistical model of plural economic causes and errors in the relations. But in the meantime, the old scientific ideal had also changed, from a deterministic to a probabilistic view of the way the world worked. This left the explanatory level of Haavelmo’s model open to new doubts. Was it really based on underlying random behaviour of the economic variables (as in contemporary evolutionary biology and quantum mechanics), or was it, after all, only a convenient way of formally dealing with inference in the non-experimental framework? (Morgan 1990).
The study of the history of econometrics has been actively promoted by David Hendry. It is desirable to motivate our choice of the term “applied spatial econometrics”; to do this we turn to (Hendry 2009), the introductory chapter in the “Applied Econometrics” volume of the Palgrave Handbook of Econometrics. If we can shed light on how “applied econometrics” is understood, then we may be able to provide adequate underpinnings for what is to follow. This, however, turns out to be hard to do, as Hendry challenges simple definitions:
At the superficial level, “Applied Econometrics” is “any application of econometrics, as distinct from theoretical econometrics. \(\ldots\) Some applied econometricians would include any applications involving analyses of”real economic data" by econometric methods, making “Applied econometrics” synonymous with empirical econometrics. However, such a view leads to demarcation difficulties from applied economics on the one hand, and applied statistics on the other. \(\ldots\) Outsiders might have thought that “Applied Econometrics” was just the application of econometrics to data, but that is definitely not so \(\ldots\) Rather, the notion of mutual penetration dominates, but as a one-way street. Economic theory comes first, almost mandatorially. Perhaps this just arises from a false view of science, namely that theory precedes evidence, \(\ldots\) (Hendry 2009).
He butresses his views using the history of econometrics to caricature empirical econometric research in the light of his view that applied economics has a “false” view of science:
\(\ldots\) cumulative critiques \(\ldots\) led to an almost monolithic approach to empirical econometric research: first postulate an individualistic, intertemporal optimization theory; next derive a model therefrom; third, find data with the same names as the theory variables; then select a recipe from the econometrics cookbook that appropriately blends the model and the data, or if necessary, develop another estimator; finally report the newly forged economic law. \(\ldots\) Instead of progress, we find fashions, cycles and “schools” in research. \(\ldots\) At about the same time that a priori theory-based econometrics became dominant, data measurement and quality issues were also relegated as a central component of empirical publications (Hendry 2009).
Hendry’s scepticism with regard to the practice of applied econometrics finds ample support in the proposal by (Anderson et al. 2008) that econometric results be acknowledged only to the extent that they are open for replication. They point out that theoretical results in economics (and econometrics) are much easier to check, and that referees routinely question formal proofs, because the relevant equations are included in journal submissions. Further contributions to the discussion on reproducible econometric research results have been made by (Koenker and Zeileis 2009) and (Yalta and Yalta 2010). As we will see later on, spatial econometrics is in the fortunate position of having relatively many open source software implementations.
In order to complete this brief review of the antecedents to spatial econometrics, it makes sense to point to descriptions of the development of econometric software. Both in a handbook chapter (Renfro 2009a), and in book form (Renfro 2009b), we can benefit from Charles Renfro’s experience and insights. He does admit to expressing distinct views, but they are backed both by experience and evidence. One concern is that econometric software development has been seen less and less as academic achievement, but rather as a practical, technical concern only:
To take an interest in programming and econometric software development would seem therefore to be the graveyard of any academic economist’s professional ambitions, justifiable only as a minimally diverting hobby, spoken of only to trusted colleagues. (Renfro 2009b)
He is not alone in drawing attention to the view that economics suffers from a skewed distribution of attention to the actual components of knowledge creation, placing theory firmly in first place, with commensurately much less weight given to measurement, data preparation and programming. As he says, applied economics researchers would benefit from a more even distribution of academic acknowledgement to those who, in terms of the division of labour within the discipline, develop software:
As a software developer, the econometrician who incorporates new theoretical econometric results may therefore be faced with the often-difficult task of not only evaluating the relevance, hence the operational validity of the theoretical solution, but also implementing these results in a way that is contextually meaningful. This operationally focused econometrician consequently not only needs to understand the theoretical advances that are made but also to exercise independent judgment in the numerical implementation of new techniques, for in fact neither are blueprints provided nor are the building blocks prefabricated. (Renfro 2009b)
Since this division of labour is arguably more uneven in economics than in other subjects, it leads to the restriction of research questions that applied economists can address to those that match estimation functions available in software implementations for which they have licences and which they know how to use:
One of the consequences of this specialization has been to introduce an element of user dependence, now grown to a sufficiently great degree that for many economists whichever set of econometric operations can be performed by their choice of program or programs has for them in effect become the universal set. (Renfro 2009b)
This deplorable situation has arisen over time, and certainly did not characterise the research context when spatial econometrics came into being. At that time, graduate students simply regarded learning to program, often in Fortran, as an essential part of their preparation as researchers. This meant that researchers were moch “closer” to their tools, and could adapt them to suit their research needs. Nowadays, few economists feel confident as programmers despite the fact that modern high-level languages such as Matlab, Python, or R are easy to learn and very flexible, and many econometric and statistical software applications offer scripting languages (such as SAS, Stata, SPSS and specifically econometric programs).
The links between methodological advance and the evolution of spatial economic theory are only touched upon in (Anselin 2010) — in that sense, his review is concerned with theoretical spatial econometrics (statistical methods) rather than applied spatial econometrics (economic models). Over time, applied spatial econometrics has tended to become synonymous with regression modelling applied to spatial data where spatial autocorrelation and spatial heterogeneity in particular are present and need to be accommodated. Its treatment of spatial effects reflects the growing “legitimization of space and geography” (Anselin 2010) in the quantitative social sciences more generally. But the subfield perhaps needs to be more than that if it is to justify its separate identity from spatial statistics and fully justify its “econometric” label. A close link with mainstream economic theory would seem essential in order to provide economic legitimacy to models (systems of equations) within which geography and spatial relationships have been, in economic terms, rigorously embedded (Fingleton 2000). (Haining 2014)
Anderson, Richard G., William H. Greene, B. D. McCullough, and H. D. Vinod. 2008. “The Role of Data/Code Archives in the Future of Economic Research.” Journal of Economic Methodology 15: 99–119.
Anselin, L. 1988. Spatial Econometrics: Methods and Models. Dordrecht: Kluwer Academic Publishers.
Anselin, Luc. 2010. “Thirty Years of Spatial Econometrics.” Papers in Regional Science 89: 3–25.
Bailly, Antoine, Pierre-Henri Derycke, and André Torre. 2012. “50 Ans de Science Régionale Francophone.” l’Association de Science Régionale de Langue Française. http://www.asrdlf.org/telechargements/50ansASRDLF.pdf.
Bartels, Cornelis P. A., and Leen Hordijk. 1977. “On the Power of the Generalized Moran Contiguity Coefficient in Testing for Spatial Autocorrelation Among Regression Disturbances.” Regional Science and Urban Economics 7: 83–101.
Billot, Antoine, and Jacques-Francois Thisse. 1992. “Claude Ponsard (1927-1990): A Biographical Essay.” Annals of Regional Science 26: 191–98.
Bivand, R. S. 2008. “Implementing Representations of Space in Economic Geography.” Journal of Regional Science 48: 1–27.
Bjerkholt, Olav. 2007. “Writing ‘the Probability Approach’ with Nowhere to Go: Haavelmo in the United States, 1939–1944.” Econometric Theory 23: 775–837.
Boyce, David. 2004. “A Short History of the Field of Regional Science.” Papers in Regional Science 83: 31–57.
Cliff, A. D., and J. K. Ord. 1969. “The Problem of Spatial Autocorrelation.” In Studies in Regional Science, edited by A. J. Scott, 25–55. London Papers in Regional Science. London: Pion.
———. 1973. Spatial Autocorrelation. London: Pion.
Drèze, Jacques H. 1964. “Some Postwar Contributions of French Economists to Theory and Public Policy: With Special Emphasis on Problems of Resource Allocation.” The American Economic Review 54 (4): 2–64.
Fingleton, B. 2000. “Spatial Econometrics, Economic Geography, Dynamics and Equilibrium: A ‘Third Way’?” Environment and Planning A 32: 1481–98.
Fujita, Masahisa, Paul Krugman, and Anthony J. Venables. 1999. The Spatial Economy. Cambridge, MA: MIT Press.
Gibbons, Stephen, and Henry G. Overman. 2012. “Mostly Pointless Spatial Econometrics?” Journal of Regional Science 52: 172–91.
Graham, Robert E., and J. Thomas Romans. 1971. “Regional Economic Accounting in the U.s. Office of Business Economics — an Appraisal.” Review of Income and Wealth 17: 1–34.
Granger, Clive W. J. 1969. “Spatial Data and Time Series Analysis.” In Studies in Regional Science, edited by A. J. Scott, 1–25. London Papers in Regional Science 1. London: Pion.
Griffith, Daniel A., and Jean H. P. Paelinck. 2007. “An Equation by Any Other Name Is Still the Same: On Spatial Econometrics and Spatial Statistics.” Annals of Regional Science 41: 209–27.
Griliches, Zvi. 1967. “Distributed Lags: A Survey.” Econometrica 35: 16–49.
Haining, Robert. 2014. “Spatial Data and Statistical Methods: A Chronological Overview.” In Handbook of Regional Science, edited by M. M. Fischer and P. Nijkamp, 1277–94. Berlin: Springer-Verlag.
Hendry, David. 2009. “The Methodology of Empirical Econometric Modelling: Applied Econometrics Through the Looking-Glass.” In Palgrave Handbook on Econometrics: Applied Econometrics, edited by T. C. Mills and K. Patterson, 3–67. Basingstoke: Palgrave Macmillan.
Hepple, Leslie W. 1974. “The Impact of Stochastic Process Theory Upon Spatial Analysis in Human Geography.” Progress in Geography 6: 89–142.
———. 1976. “A Maximum Likelihood Model for Econometric Estimation with Spatial Series.” In Theory and Practice in Regional Science, edited by I. Masser, 90–104. London Papers in Regional Science. London: Pion.
Hordijk, Leen. 1974. “Spatial Correlation in the Disturbances of a Linear Interregional Model.” Regional and Urban Economics 4: 117–40.
Hordijk, Leen, and Jean H. P. Paelinck. 1976. “Some Principles and Results in Spatial Econometrics.” Recherches Economiques de Louvain 42: 175–97.
Klaassen, Leo H., Jean H. P. Paelinck, and Sjoerd Wagenaar. 1979. Spatial Systems. Farnborough: Saxon House.
Klein, L. R. 1958. “The Estimation of Distributed Lags.” Econometrica 26: 553–65.
Koenker, Roger, and Achim Zeileis. 2009. “On Reproducible Econometric Research.” Journal of Applied Econometrics 24: 833–47.
Koyck, L. M. 1954. Distributed Lags and Znoestment Analysis. Amsterdam: North-Holland.
McMillen, D. P. 2010. “Issues in Spatial Data Analysis.” Journal of Regional Science 50: 119–41.
Morgan, Mary S. 1990. The History of Econometric Ideas. Cambridge: Cambridge University Press.
Ord, J. K. 1975. “Estimation Methods for Models of Spatial Interaction.” Journal of the American Statistical Association 70: 120–26.
Ord, J. Keith. 2010. “Spatial Autocorrelation: A Statistician’s Reflections.” In Perspectives on Spatial Data Analysis, edited by L. Anselin and S. J. Rey, 165–80. Berlin: Springer-Verlag.
Paelinck, Jean H. P. 2012. “Specification and Identification in Spatial Econometric Models.” Estadística Española 54: 35–52.
———. 2013. “Some Challenges for Spatial Econometricians.” In Spatial Econometrics and Regional Economic Analysis, edited by Bogdan Suchecki, 292:11–20. Acta Universitas Lodziensis, Folia Oeconomica. Wydawnictwa Uniwersytetu Łódzkiego.
Paelinck, Jean H. P., and Leo H. Klaassen. 1979. Spatial Econometrics. Farnborough: Saxon House.
Paelinck, Jean H. P., and Peter Nijkamp. 1975. Operational Theory and Method in Regional Economics. Farnborough: Saxon House.
Perroux, François. 1950. “Economic Space: Theory and Applications.” Quarterly Journal of Economics 64: 89–104.
Ponsard, Claude. 1983. History of Spatial Economic Theory. Berlin: Springer-Verlag.
Qin, Duo. 1993. The Formation of Econometrics: A Historical Perspective. Oxford: Oxford University Press.
Renfro, Charles G. 2009a. “Econometric Software.” In Handbook of Computational Econometrics, edited by David A. Belsley and Erricos Kontoghiorghes, 1–53. Chichester, U.K: John Wiley & Sons.
———. 2009b. The Practice of Econometric Theory: An Examination of the Characteristics of Econometric Computation. Berlin: Springer-Verlag.
Sandmo, Agnar. 2011. Evolving Economics: A History of Economic Thought. Princeton: Princeton University Press.
Theil, H. 1964. “Some Developments of Economic Thought in the Netherlands.” The American Economic Review 54: pp. 34–55.
Wilson, Alan G. 2000. Complex Spatial Systems: The Modelling Foundations of Urban and Regional Analysis. Harlow: Prentice Hall.
———. 2012. The Science of Cities and Regions: Lectures on Mathematical Model Design. Dordrecht: Springer.
Yalta, A.Talha, and A.Yasemin Yalta. 2010. “Should Economists Use Open Source Software for Doing Research?” Computational Economics 35: 371–94.