{"title":"3 Dealing with heterogeneity and endogeneity: fixed effects, IV and GMM","authors":"","doi":"10.1515/9783110660739-003","DOIUrl":"https://doi.org/10.1515/9783110660739-003","url":null,"abstract":"","PeriodicalId":253603,"journal":{"name":"Panel Methods for Finance","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127003765","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}
{"title":"7 Estimating average treatment effects","authors":"","doi":"10.1515/9783110660739-007","DOIUrl":"https://doi.org/10.1515/9783110660739-007","url":null,"abstract":"","PeriodicalId":253603,"journal":{"name":"Panel Methods for Finance","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123374436","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}
{"title":"4 Outliers, missing values and other data issues","authors":"","doi":"10.1515/9783110660739-004","DOIUrl":"https://doi.org/10.1515/9783110660739-004","url":null,"abstract":"","PeriodicalId":253603,"journal":{"name":"Panel Methods for Finance","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114858705","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}
{"title":"5 Linear dynamic models","authors":"P. Sevestre, A. Trognon","doi":"10.1515/9783110660739-005","DOIUrl":"https://doi.org/10.1515/9783110660739-005","url":null,"abstract":"One of the main advantages of panel data is that it allows one to study the dynamics of economic behaviour at an individual level. Unfortunately, when dynamic models are estimated using time series of cross sections data, the usual least squares methods (such as those presented in Chapters 3 and 4) do not lead to consistent estimates for the parameters of the two most commonly used models for panel data (i.e., fixed effects and error components models). This inconsistency results from the fact that the disturbance terms are serially correlated in these models, which causes the lagged endogenous variable to be correlated with those disturbances. As in the time series context, we do not have analytical results about the small sample properties of the various estimators of these models. The only available results come from Monte-Carlo simulation studies (see Nerlove [1967], [1971]). Hence, one must rely on the asymptotic properties of these methods and assume that the size of the sample grows to infinity. Since a panel data set has two dimensions, it is possible to increase the size of the sample in various ways. Firstly one could increase the time dimension of the sample, i.e., make T tend to infinity as is done when working with time series data. As most available panel data sets contain a large number of observations on individuals (N) over a limited number of periods (T), this is not a very relevant option. It is therefore, probably more useful to study the so-called semi-asymptotic behaviour of the various estimators, by making N --+ 00 but keeping T finite. 1 This has two notable implications when one works with a dynamic model such as","PeriodicalId":253603,"journal":{"name":"Panel Methods for Finance","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125153278","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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