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A literature search often involves time series, cross-sectional, or panel data. Cross-panel data (CPD) is an innovative yet underappreciated source of information in the mathematical and statistical sciences. CPD stands out from other research methods because it vividly illustrates how independent and dependent variables may shift between ...
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 the analysis, and the problems concerning the exogeneity of the explanatory variables.
In statistics and econometrics, cross-sectional data is a type of data collected by observing many subjects (such as individuals, firms, countries, or regions) at a single point or period of time. Analysis of cross-sectional data usually consists of comparing the differences among selected subjects, typically with no regard to differences in time.
In econometrics, a multidimensional panel data is data of a phenomenon observed over three or more dimensions. This comes in contrast with panel data, observed over two dimensions (typically, time and cross-sections). An example is a data set containing forecasts of one or multiple macroeconomic variables produced by multiple individuals (the ...
The estimator requires data on a dependent variable, , and independent variables, , for a set of individual units =, …, and time periods =, …,. The estimator is obtained by running a pooled ordinary least squares (OLS) estimation for a regression of Δ y i t {\displaystyle \Delta y_{it}} on Δ x i t {\displaystyle \Delta x_{it}} .
In statistics, econometrics and related fields, multidimensional analysis (MDA) is a data analysis process that groups data into two categories: data dimensions and measurements. For example, a data set consisting of the number of wins for a single football team at each of several years is a single-dimensional (in this case, longitudinal) data set.
A pooled analysis is a statistical technique for combining the results of multiple epidemiological studies. It is one of three types of literature reviews frequently used in epidemiology, along with meta-analysis and traditional narrative reviews. Pooled analyses may be either retrospective or prospective. [1]
Partial (pooled) likelihood estimation for panel data is a quasi-maximum likelihood method for panel analysis that assumes that density of given is correctly specified for each time period but it allows for misspecification in the conditional density of = (, …,) given = (, …,).