—also known as longitudinal data—tracks the same cross-sectional units (such as individuals, firms, or countries) over multiple periods. This structure allows researchers to control for unobserved time-invariant characteristics, drastically reducing omitted variable bias.
Before analysis, you must declare the data to be panel data.
Alternative to RE. Does not model the individual effect explicitly but accounts for the correlation structure within panels. stata panel data
xtreg ln_wage hours age tenure, fe xtcd
gen L_wage = L.wage // Lag (previous period) gen F_wage = F.wage // Lead (next period) gen D_wage = D.wage // Difference gen D2_wage = D2.wage // Second difference Panel data —also known as longitudinal data—tracks the
Use the xtset command to tell Stata which variables define the panels and the time. xtset country_id year Use code with caution. Copied to clipboard
reshape wide income_, i(id) j(year)
Before modeling, explore the data using panel-specific commands.
—also known as longitudinal data—tracks the same cross-sectional units (such as individuals, firms, or countries) over multiple periods. This structure allows researchers to control for unobserved time-invariant characteristics, drastically reducing omitted variable bias.
Before analysis, you must declare the data to be panel data.
Alternative to RE. Does not model the individual effect explicitly but accounts for the correlation structure within panels.
xtreg ln_wage hours age tenure, fe xtcd
gen L_wage = L.wage // Lag (previous period) gen F_wage = F.wage // Lead (next period) gen D_wage = D.wage // Difference gen D2_wage = D2.wage // Second difference
Use the xtset command to tell Stata which variables define the panels and the time. xtset country_id year Use code with caution. Copied to clipboard
reshape wide income_, i(id) j(year)
Before modeling, explore the data using panel-specific commands.