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Abstract: AbstractThe goal of this monograph is to very concisely outline the economic theory foundations and trends of the field of Effciency and Productivity Analysis, also sometimes referred to as Performance Analysis. I start with the profit maximization paradigm of mainstream economics, use it to derive a general profit effciency measure and then present its special cases: revenue maximization and revenue effciency, cost minimization and cost effciency. I then consider various types of technical and allocative effciencies (directional and Shephard’s distance functions and related Debreu–Farrell measures as well as non-directional measures of technical effciency), showing how they fit into or decompose the profit maximization paradigm. I then cast the effciency and productivity concepts in a dynamic perspective that is frequently used to analyze the productivity changes of economic systems (firms, hospitals, banks, countries, etc.) over time. I conclude this monograph with an overview of major results on aggregation in productivity and effciency analysis, where the aggregate productivity and effciency measures are theoretically connected to their individual analogues.Suggested CitationValentin Zelenyuk (2021), "Performance Analysis: Economic Foundations and Trends", Foundations and Trends® in Econometrics: Vol. 11: No. 3, pp 153-229. http://dx.doi.org/10.1561/0800000034 PubDate: Mon, 20 Sep 2021 00:00:00 +020
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Abstract: AbstractThis monograph aims to survey a range of econometric techniques that are currently being used by experimental economists. It is likely to be of interest both to experimental economists who are keen to expand their skill sets, and also the wider econometrics community who may be interested to learn the sort of econometric techniques that are currently being used by Experimentalists. Techniques covered range from the simple to the fairly advanced. The monograph starts with an overview of treatment testing. A range of treatment tests will be illustrated using the example of a dictator-game giving experiment in which there is a communication treatment. Standard parametric and non-parametric treatment tests, tests comparing entire distributions, and bootstrap tests will all be covered. It will then be demonstrated that treatment tests can be performed in a regression framework, and the important concept of clustering will be explained. The multilevel modelling framework will also be covered, as a means of dealing with more than one level of clustering. Power analysis will be covered from both theoretical and practical perspectives, as a means of determining the sample size required to attain a given power, and also as a means of computing ex-post power for a reported test. We then progress to a discussion of different data types arising in Experimental Economics (binary, ordinal, interval, etc.), and how to deal with them. We then consider the estimation of fully structural models, with particular attention paid to the estimation of social preference parameters from dictator game data, and risky choice models with between-subject heterogeneity in risk aversion. The method maximum simulated likelihood (MSL) is promoted as the most suitable method for estimating models with continuous heterogeneity. We then consider finite mixture models as a way of capturing discrete heterogeneity; that is, when the population of subjects divides into a small number of distinct types. The application used as an example will be the level-k model, in which subject types are defined by their levels of reasoning. We then consider other models of behaviour in games, including the Quantal Response Equilibrium (QRE) Model. The final area covered is models of learning in games.Suggested CitationPeter G. Moffatt (2021), "Experimetrics: A Survey", Foundations and Trends® in Econometrics: Vol. 11: No. 1–2, pp 1-152. http://dx.doi.org/10.1561/0800000035 PubDate: Mon, 15 Feb 2021 00:00:00 +010
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