Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

Criticism and the Growth of Knowledge. Edited by I. Lakatos and A. Musgrave. Pp. viii, 282. £3·50, paperback £1. 1970. (Cambridge University Press.)

There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.

Why most published research findings are false

Aim:There currently does not exist guidance for authors aiming to undertake systematic reviews of observational epidemiological studies, such as those reporting prevalence and incidence information. These reviews are particularly useful to measure global disease burden and changes in disease

Methodological guidance for systematic reviews of observational epidemiological studies reporting prevalence and cumulative incidence data.

We studied publication bias in the social sciences by analyzing a known population of conducted studies—221 in total—in which there is a full accounting of what is published and unpublished. We leveraged Time-sharing Experiments in the Social Sciences (TESS), a National Science Foundation–sponsored program in which researchers propose survey-based experiments to be run on representative samples of American adults. Because TESS proposals undergo rigorous peer review, the studies in the sample all exceed a substantial quality threshold. Strong results are 40 percentage points more likely to be published than are null results and 60 percentage points more likely to be written up. We provide direct evidence of publication bias and identify the stage of research production at which publication bias occurs: Authors do not write up and submit null findings.

http://science.sciencemag.org/content/sci/345/6203/1502.full.pdf

Publication bias in the social sciences: Unlocking the file drawer

The efficiency of Australian universities: a data envelopment analysis

Meta‐regression analysis (MRA) can provide objective and comprehensive summaries of economics research. Their use has grown rapidly over the last few decades. To improve transparency and to raise the quality of MRA, the meta‐analysis of economics research‐network (MAER‐Net) has created the below reporting guidelines. Future meta‐analyses in economics will be expected to follow these guidelines or give valid reasons why a meta‐analysis must deviate from them.

Meta-analysis of Economics Research Reporting Guidelines

Economic freedom and economic growth: Does specification make a difference?

There is growing concern and mounting evidence of selectivity in empirical economics. Most empirical economic literatures have a truncated distribution of results. The aim of this paper is to explore the link between publication selectivity and theory contests. This link is confirmed through the analysis of 87 distinct empirical economics literatures, involving more than three and a half thousand separate empirical studies, using objective measures of both selectivity and contests. Our meta–meta-analysis shows that publication selection is widespread, but not universal. It distorts scientific inference with potentially adverse effects on policy making, but competition and debate between rival theories reduces this selectivity and thereby improves economic inference.

Are all economic facts greatly exaggerated? theory competition and selectivity

. The impact of institutions on economic performance has attracted significant attention from researchers, as well as from policy reformers. A rapidly growing area in this literature is the impact of economic freedom on economic growth. The aim of this paper was to explore publication bias in this literature by means of traditional funnel plots, meta-significance testing, as well as by bootstrapping these meta-significance tests. When all the available estimates are combined and averaged, there seems to be evidence of a genuine and positive economic freedom – economic growth effect. However, it is also shown that the economic freedom – economic growth literature is tainted strongly with publication bias. The existence of publication bias makes it difficult to identify the magnitude of the genuine effect of economic freedom on economic growth. The paper explores the differences between aggregate and disaggregate measures of economic freedom and shows that selection effects are stronger when aggregate measures are used.

Chris Doucouliagos

Papers

The efficiency of Australian universities: a data envelopment analysis

Meta-analysis of Economics Research Reporting Guidelines

Economic freedom and economic growth: Does specification make a difference?

Are all economic facts greatly exaggerated? theory competition and selectivity

Publication Bias in the Economic Freedom and Economic Growth Literature