In economics, the analysis of cross-sectional data is closely associated with applied microeconomics fields such as labor economics, public finance, industrial organization, urban economics, demography, and health economics. Efficiency can be attained from generalized least squares (GLS) estimation. Ordinary least squares (OLS) estimates that ignore heterogeneity across cross sections are unbiased but inefficient. is a 1 X ^vector of explanatory variables, often has stochastic errors £. Figure 1 illustrates the relationship between cross-sectional data on the price of houses sold within a two-week period and the houses’ size. In a pure cross-sectional analysis, such minor time differences in data collection are ignored. Sometimes the data on all units do not correspond to precisely the same time period. A cross-sectional dataset consists of a sample of individuals, households, firms, cities, states, countries, or any other micro- or macroeconomic unit taken at a given point in time. The cross-sectional, time series, and panel data are the most commonly used kinds of datasets. Economic datasets come in a variety of forms.
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