This 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.
{"title":"Experimetrics: A Survey","authors":"Peter G. Moffatt","doi":"10.1561/0800000035","DOIUrl":"https://doi.org/10.1561/0800000035","url":null,"abstract":"This 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.","PeriodicalId":294596,"journal":{"name":"Foundations and Trends in Econometrics","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116981882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Climate econometrics is a new sub-discipline that has grown rapidly over the last few years. As greenhouse gas emissions like carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) are a major cause of climate change, and are generated by human activity, it is not surprising that the tool set designed to empirically investigate economic outcomes should be applicable to studying many empirical aspects of climate change. Economic and climate time series exhibit many commonalities. Both data are subject to non-stationarities in the form of evolving stochastic trends and sudden distributional shifts. Consequently, the well-developed machinery for modeling economic time series can be fruitfully applied to climate data. In both disciplines, we have imperfect and incomplete knowledge of the processes actually generating the data. As we don’t know that data generating process (DGP), we must search for what we hope is a close approximation to it. The data modeling approach adopted at Climate Econometrics (http://www.climateeconometrics.org/) is based on a model selection methodology that has excellent properties for locating an unknown DGP nested within a large set of possible explanations, including dynamics, outliers, shifts, and non-linearities. The software we use is a variant of machine learning which implements multi-path block searches commencing from very general specifications to discover a well-specified and undominated model of the processes under analysis. To do so requires implementing indicator saturation estimators designed to match the problem faced, such as impulse indicators for outliers, step indicators for location shifts, trend indicators for trend breaks, multiplicative indicators for parameter changes, and indicators specifically designed for more complex phenomena that have a common reaction ‘shape’ like the impacts of volcanic eruptions on temperature reconstructions. We also use combinations of these, inevitably entailing settings with more candidate variables than observations. Having described these econometric tools, we take a brief excursion into climate science to provide the background to the later applications. By noting the Earth’s available atmosphere and water resources, we establish that humanity really can alter the climate, and is doing so in myriad ways. Then we relate past climate changes to the ‘great extinctions’ seen in the geological record. Following the Industrial Revolution in the mid-18th century, building on earlier advances in scientific, technological and medical knowledge, real income levels per capita have risen dramatically globally, many killer diseases have been tamed, and human longevity has approximately doubled. However, such beneficial developments have led to a global explosion in anthropogenic emissions of greenhouse gases. These are also subject to many relatively sudden shifts from major wars, crises, resource discoveries, technology and policy interventions. Consequently, s
{"title":"Climate Econometrics: An Overview","authors":"Jennifer L. Castle, D. Hendry","doi":"10.1561/0800000037","DOIUrl":"https://doi.org/10.1561/0800000037","url":null,"abstract":"Climate econometrics is a new sub-discipline that has grown rapidly over the last few years. As greenhouse gas emissions like carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) are a major cause of climate change, and are generated by human activity, it is not surprising that the tool set designed to empirically investigate economic outcomes should be applicable to studying many empirical aspects of climate change. Economic and climate time series exhibit many commonalities. Both data are subject to non-stationarities in the form of evolving stochastic trends and sudden distributional shifts. Consequently, the well-developed machinery for modeling economic time series can be fruitfully applied to climate data. In both disciplines, we have imperfect and incomplete knowledge of the processes actually generating the data. As we don’t know that data generating process (DGP), we must search for what we hope is a close approximation to it. The data modeling approach adopted at Climate Econometrics (http://www.climateeconometrics.org/) is based on a model selection methodology that has excellent properties for locating an unknown DGP nested within a large set of possible explanations, including dynamics, outliers, shifts, and non-linearities. The software we use is a variant of machine learning which implements multi-path block searches commencing from very general specifications to discover a well-specified and undominated model of the processes under analysis. To do so requires implementing indicator saturation estimators designed to match the problem faced, such as impulse indicators for outliers, step indicators for location shifts, trend indicators for trend breaks, multiplicative indicators for parameter changes, and indicators specifically designed for more complex phenomena that have a common reaction ‘shape’ like the impacts of volcanic eruptions on temperature reconstructions. We also use combinations of these, inevitably entailing settings with more candidate variables than observations. Having described these econometric tools, we take a brief excursion into climate science to provide the background to the later applications. By noting the Earth’s available atmosphere and water resources, we establish that humanity really can alter the climate, and is doing so in myriad ways. Then we relate past climate changes to the ‘great extinctions’ seen in the geological record. Following the Industrial Revolution in the mid-18th century, building on earlier advances in scientific, technological and medical knowledge, real income levels per capita have risen dramatically globally, many killer diseases have been tamed, and human longevity has approximately doubled. However, such beneficial developments have led to a global explosion in anthropogenic emissions of greenhouse gases. These are also subject to many relatively sudden shifts from major wars, crises, resource discoveries, technology and policy interventions. Consequently, s","PeriodicalId":294596,"journal":{"name":"Foundations and Trends in Econometrics","volume":"331 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123203968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Here we present a selected survey in which we attempt to break down the ever burgeoning literature on inference in the presence of weak instruments into issues of estimation, hypothesis testing and confidence interval construction. Within this literature a variety of different approaches have been adopted and one of the contributions of this survey is to examine some of the links between them. The vehicle that we will use to establish these links will be the small concentration results of Poskitt and Skeels (2007), which can be used to characterize various special cases when instruments are weak. We make no attempt to provide an exhaustive survey of all of the literature related to weak instruments. Contributions along these lines can be found in, inter alia , Stock et al. (2002), Dufour (2003), Hahn and Hausman (2003), and Andrews and Stock (2007), and we view this survey as complementary to those earlier works.
{"title":"Inference in the presence of weak instruments : a selected survey","authors":"D. Poskitt, Christopher L Skeels","doi":"10.1561/0800000017","DOIUrl":"https://doi.org/10.1561/0800000017","url":null,"abstract":"Here we present a selected survey in which we attempt to break down the ever burgeoning literature on inference in the presence of weak instruments into issues of estimation, hypothesis testing and confidence interval construction. Within this literature a variety of different approaches have been adopted and one of the contributions of this survey is to examine some of the links between them. The vehicle that we will use to establish these links will be the small concentration results of Poskitt and Skeels (2007), which can be used to characterize various special cases when instruments are weak. We make no attempt to provide an exhaustive survey of all of the literature related to weak instruments. Contributions along these lines can be found in, inter alia , Stock et al. (2002), Dufour (2003), Hahn and Hausman (2003), and Andrews and Stock (2007), and we view this survey as complementary to those earlier works.","PeriodicalId":294596,"journal":{"name":"Foundations and Trends in Econometrics","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115517650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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