How do you analyze cross-sectional data?
A cross sectional data is data collected by observing various subjects like (firms, countries, regions, individuals), at the same point in time. A cross sectional data is analyzed by comparing the differences within the subjects.
Can you have serial correlation in cross-sectional data?
Autocorrelation refers to the degree of correlation between the values of the same variables across different observations in the data. However, autocorrelation can also occur in cross-sectional data when the observations are related in some other way.
Why is heteroscedasticity common in cross sectional data?
Heteroscedasticity is more common in cross sectional types of data than in time series types of data. Due to the presence of heteroscedasticity, the variance that is obtained by the researcher should be of lesser value than the value of the variance of the best linear unbiased estimator (BLUE).
How to run OLS regression on cross sectional data?
For a (crude) example, assume that you collect data on number of cars that has stopped in various stop-in establishments along a highway, on a particular day (that’s the dependent variable). Your regressors measure the various facilities/services each stop-in offers, and perhaps other things like distance from highway exits/entrances.
What kind of data is cross sectional data?
There is also a type of data called cross-sectional data, where we are dealing with information about different individuals (or aggregates such as work teams, sales territories, stores, etc.) at the same point of time or during the same time period.
How to calculate OLS model with time series data?
Step 1: Run OLS model y t = β 0+β 1 x 1t + β 2 x 2t + . . . .β k X kt + t Step 2: Calculate predicted residuals Step 3: Form test statistic 2(1 ˆ) ( ˆ ) ( ˆ ) 1 2 2 2 1 T t t T t t t DW (See Gujarati pg 435 to derive) Assumptions: 1. Regression includes intercept term 2.
When to not use autocorrelation in OLS regression?
If on the other hand no such “natural” and meaningful ordering appears to be present for a specific data set, then the possible correlation between observations should not be designated as “autocorrelation” because it would be misleading, and the tools specifically developed for ordered data are inapplicable.