A social power index for hierarchically structured populations of economic agents

Econometrics;econometrics

On proximity and hierarchy : exploring and modelling space using multilevel modelling and spatial econometrics

Spatial econometrics and also multilevel modelling techniques are increasingly part of the regional scientists‟ toolbox. Both approaches are used to model spatial autocorrelation in a wide variety of applications. However, it is not always clear on which basis researchers make a choice between spatial econometrics and spatial multilevel modelling. Therefore it is useful to compare both techniques. Spatial econometrics incorporates neighbouring areas into the model design; and thus interprets spatial proximity as defined in Tobler‟s first law of geography. On the other hand, multilevel modelling using geographical units takes a more hierarchical approach. In this case the first law of geography can be rephrased as „everything is related to everything else, but things in the same region are more related than things in different regions‟. The hierarchy (multilevel) and the proximity (spatial econometrics) approach are illustrated using Belgian mobility data and productivity data of European regions. One of the advantages of a multilevel model is that it can incorporate more than two levels (spatial scales). Another advantage is that a multilevel structure can easily reflect an administrative structure with different government levels. Spatial econometrics on the other hand works with a unique set of neighbours which has the advantage that there still is a relation between neighbouring municipalities separated by a regional boundary. The concept of distance can also more easily be incorporated in a spatial econometrics setting. Both spatial econometrics and spatial multilevel modelling proved to be valuable techniques in spatial research but more attention should go to the rationale why one of the two approaches is chosen. We conclude with some comments on models which make a combination of both techniques

ECONOMETRICS AND REALITY

Starting with a realist ontology the economic methodologist, Tony Lawson, argues that econometrics is a failed project. Apparently more sympathetic to econometrics, the philosopher of science, Nancy Cartwright, again from a realist perspective, nonetheless argues for conditions of applicability that are so stringent that she must seriously doubt the usefulness of econometrics. In this paper, I reconsider Lawson''s and Cartwright''s analyses and argue that realism supports rather than undermines econometrics properly interpreted and executed.

Econometrics

Since the last decade we live in a digitalized world where many actions in human and economic life are monitored. This produces a continuous stream of new, rich and high quality data in the form of panels, repeated cross-sections and long time series . These data resources are available to many researchers at a low cost. This new erais fascinating for econometricians who can adress many open economic questions. To do so, new models are developed that call for elaborate estimation techniques. Fast personal computers play an integral part in making it possible to deal with this increased complexity. --

Modelling of the Inflation-Unemployment Tradeoff from the Perspective of the History of Econometrics

This paper examines the history of econometrics through a particular case study - modelling the tradeoff between inflation and unemployment. It focuses on the questions of what econometric tools modellers would choose to model the tradeoff, how their choices helped shape the ways that they obtained, interpreted and theorised the empirical evidence and how their different concerns and the different problems that they encountered has fed back into the development of econometrics. The study reveals that much of the interaction between econometrics and economics involved modellers taking certain tradeoffs between theory and data, and their different positions generated disputes, factions as well as confusions. It also reveals that the history of modelling the tradeoff mirrors the evolving process of how the Cowles structural modelling paradigm in econometrics became consolidated, challenged, reformed or abandoned.Phillips curve, History of econometrics

The econometrics of the Holy Grail: A critique (Revised version)

This paper discusses the methodological views of David Hendry. A critique is given on the reification of the so-called Data Generating Process. The merits of general to specific modelling are analyzed. Hendry's neo-Popperian philosophy is examined. It is argued that this philosophy is not able to transform econometrics from `alchemy' to `science'.Econometrics;Testing;Data Generation Process;econometrics

Econometrics in R: Past, Present, and Future

Recently, computational methods and software have been receiving more attention in the econometrics literature, emphasizing that they are integral components of modern econometric research. This has also promoted the development of many new econometrics software packages written in R and made available on the Comprehensive R Archive Network. This special volume on "Econometrics in R" features a selection of these recent activities that includes packages for econometric analysis of cross-section, time series and panel data. This introduction to the special volume highlights the contents of the contributions and embeds them into a brief overview of other past, present, and future projects for econometrics in R.

Great Expectatrics: Great Papers, Great Journals, Great Econometrics

The paper discusses alternative Research Assessment Measures (RAM), with an emphasis on the Thomson Reuters ISI Web of Science database (hereafter ISI). The various ISI RAM that are calculated annually or updated daily are defined and analysed, including the classic 2-year impact factor (2YIF), 5-year impact factor (5YIF), Immediacy (or zero-year impact factor (0YIF)), Eigenfactor score, Article Influence, C3PO (Citation Performance Per Paper Online), h-index, Zinfluence, and PI-BETA (Papers Ignored - By Even The Authors). The ISI RAM data are analysed for 8 leading econometrics journals and 4 leading statistics journals. The application to econometrics can be used as a template for other areas in economics, for other scientific disciplines, and as a benchmark for newer journals in a range of disciplines. In addition to evaluating high quality research in leading econometrics journals, the paper also compares econometrics and statistics, alternative RAM, highlights the similarities and differences in alternative RAM criteria, finds that several ISI RAM capture similar performance characteristics for the leading econometrics and statistics journals while the new PI-BETA criterion is not highly correlated with any of the other ISI RAM, and hence conveys additional information regarding ISI RAM, highlights major research areas in leading journals in econometrics, and discusses some likely future uses of RAM.Research assessment measures; impact factors; Immediacy; Eigenfactor score; Article influence; h-index; C3PO; Zinfluence; PI-BETA

Growth Econometrics

This paper provides a survey and synthesis of econometric tools that have been employed to study economic growth. While these tools range across a variety of statistical methods, they are united in the common goals of first, identifying interesting contemporaneous patterns in growth data and second, drawing inferences on long-run economic outcomes from cross-section and temporal variation in growth. We describe the main stylized facts that have motivated the development of growth econometrics, the major statistical tools that have been employed to provide structural explanations for these facts, and the primary statistical issues that arise in the study of growth data. An important aspect of the survey is attention to the limits that exist in drawing conclusions from growth data, limits that reflect model uncertainty and the general weakness of available data relative to the sorts of questions for which they are employed.