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Econometrics, Volume 1, Issue 3 (December 2013), Pages 207-280

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Research

Open AccessArticle The Geometric Meaning of the Notion of Joint Unpredictability of a Bivariate VAR(1) Stochastic Process
Econometrics 2013, 1(3), 207-216; doi:10.3390/econometrics1030207
Received: 21 August 2013 / Revised: 5 November 2013 / Accepted: 5 November 2013 / Published: 14 November 2013
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Abstract This paper investigates, in a particular parametric framework, the geometric meaning of joint unpredictability for a bivariate discrete process. In particular, the paper provides a characterization of the joint unpredictability in terms of distance between information sets in an Hilbert space. Full article
Open AccessArticle Ranking Leading Econometrics Journals Using Citations Data from ISI and RePEc
Econometrics 2013, 1(3), 217-235; doi:10.3390/econometrics1030217
Received: 15 October 2013 / Revised: 6 November 2013 / Accepted: 7 November 2013 / Published: 18 November 2013
Cited by 3 | PDF Full-text (131 KB) | HTML Full-text | XML Full-text
Abstract
The paper focuses on the robustness of rankings of academic journal quality and research impact of 10 leading econometrics journals taken from the Thomson Reuters ISI Web of Science (ISI) Category of Economics, using citations data from ISI and the highly accessible Research
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The paper focuses on the robustness of rankings of academic journal quality and research impact of 10 leading econometrics journals taken from the Thomson Reuters ISI Web of Science (ISI) Category of Economics, using citations data from ISI and the highly accessible Research Papers in Economics (RePEc) database that is widely used in economics, finance and related disciplines. The journals are ranked using quantifiable static and dynamic Research Assessment Measures (RAMs), with 15 RAMs from ISI and five RAMs from RePEc. The similarities and differences in various RAMs, which are based on alternative weighted and unweighted transformations of citations, are highlighted to show which RAMs are able to provide informational value relative to others. The RAMs include the impact factor, mean citations and non-citations, journal policy, number of high quality papers, and journal influence and article influence. The paper highlights robust rankings based on the harmonic mean of the ranks of 20 RAMs, which in some cases are closely related. It is shown that emphasizing the most widely-used RAM, the two-year impact factor of a journal, can lead to a distorted evaluation of journal quality, impact and influence relative to the harmonic mean of the ranks. Some suggestions regarding the use of the most informative RAMs are also given. Full article
Open AccessArticle Polynomial Regressions and Nonsense Inference
Econometrics 2013, 1(3), 236-248; doi:10.3390/econometrics1030236
Received: 6 August 2013 / Revised: 28 October 2013 / Accepted: 7 November 2013 / Published: 18 November 2013
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Abstract
Polynomial specifications are widely used, not only in applied economics, but also in epidemiology, physics, political analysis and psychology, just to mention a few examples. In many cases, the data employed to estimate such specifications are time series that may exhibit stochastic nonstationary
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Polynomial specifications are widely used, not only in applied economics, but also in epidemiology, physics, political analysis and psychology, just to mention a few examples. In many cases, the data employed to estimate such specifications are time series that may exhibit stochastic nonstationary behavior. We extend Phillips’ results (Phillips, P. Understanding spurious regressions in econometrics. J. Econom. 1986, 33, 311–340.) by proving that an inference drawn from polynomial specifications, under stochastic nonstationarity, is misleading unless the variables cointegrate. We use a generalized polynomial specification as a vehicle to study its asymptotic and finite-sample properties. Our results, therefore, lead to a call to be cautious whenever practitioners estimate polynomial regressions. Full article
(This article belongs to the Special Issue Econometric Model Selection)
Open AccessArticle Academic Rankings with RePEc
Econometrics 2013, 1(3), 249-280; doi:10.3390/econometrics1030249
Received: 28 October 2013 / Revised: 29 November 2013 / Accepted: 2 December 2013 / Published: 17 December 2013
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Abstract
This article describes the data collection and use of data for the computation of rankings within RePEc (Research Papers in Economics). This encompasses the determination of impact factors for journals and working paper series, as well as the ranking of authors, institutions, and
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This article describes the data collection and use of data for the computation of rankings within RePEc (Research Papers in Economics). This encompasses the determination of impact factors for journals and working paper series, as well as the ranking of authors, institutions, and geographic regions. The various ranking methods are also compared, using a snapshot of the data. Full article

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