In statistics and econometrics, a cross-sectional regression is a type of regression in which the explained and explanatory variables are all associated with the same single period or point in time.
This type of cross-sectional analysis is in contrast to a time-series regression or longitudinal regression in which the variables are considered to be associated with a sequence of points in time.
For example, in economics a regression to explain and predict money demand (how much people choose to hold in the form of the most liquid assets) could be conducted with either cross-sectional or time series data.
A cross-sectional regression would have as each data point an observation on a particular individual's money holdings, income, and perhaps other variables at a single point in time, and different data points would reflect different individuals at the same point in time.
In contrast, a regression using time series would have as each data point an entire economy's money holdings, income, etc.