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Panel (data) analysis is a statistical method, widely used in social science, epidemiology, and econometrics, which deals with two and "n"-dimensional (in and by the - cross sectional/times series time) panel data. The data are usually collected over time and over the same individuals and then a regression is run over these two dimensions. Multidimensional analysis is an econometric method in which data are collected over more than two dimensions (typically, time, individuals, and some third dimension). A common panel data regression model looks like , where ''y'' is the dependent variable, ''x'' is the independent variable, ''a'' and ''b'' are coefficients, ''i'' and ''t'' are indices for individuals and time. The error is very important in this analysis. Assumptions about the error term determine whether we speak of fixed effects or random effects. In a fixed effects model, is assumed to vary non-stochastically over or making the fixed effects model analogous to a dummy variable model in one dimension. In a random effects model, is assumed to vary stochastically over or requiring special treatment of the error variance matrix. Panel data analysis has three more-or-less independent approaches: *independently pooled panels; *random effects models; *fixed effects models or first differenced models. The selection between these methods depends upon the objective of our analysis, and the problems concerning the exogeneity of the explanatory variables. == Independently pooled panels == Key Assumption: There are no unique attributes of individuals within the measurement set, and no universal effects across time. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Panel analysis」の詳細全文を読む スポンサード リンク
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