# Can I do panel regression if all my covariates are time-invariant?

We have a data set where our outcome of interest varies over 10 years, but the explanatory variable of interest and all of the potential confounders are time-invariant. I am quite certain that a panel regression is not possible with this data set but want to be sure I'm not missing something. With a fixed regression all the covariates drop out and the assumptions of random effects do not hold with these data. And practically speaking, does there not need to be at least some variation in the explanatory variables to model the relationship with a time-varying outcome?

• Are all your EVs & covariates identical, or different from each other but constant over time? There should be no problem w/ panel / longitudinal models if the EVs vary amongst the study units but do not vary over time. This situation is quite common in biomedical research. – gung - Reinstate Monica Jan 25 '15 at 16:47
• Fixed effects is one way of dealing with time-constant unobserved confounders, but not the only one. Perhaps you can think of an instrumental variable? But it may well be that the causal effect you are after is simply not identified in your data. If so, you would have to be content with prediction and description. – CloseToC Jan 25 '15 at 18:04

Under some assumptions you can recover the coefficients of time invariant variables in fixed effects regressions. The reference for this would be Abowd et al. (1999). Even though their model is for hierarchical panel data the logic should also apply to the standard panel data setting. If you have a regression of the form $$y_{it} = x_{it}\beta + \theta_i + \epsilon_{it}$$ where $y_{it}$ is the outcome, $x_{it}$ are the time-varying observed variables, $\theta_i$ are the individual fixed effects, and $\epsilon_{it}$ is a stochastic error term. Let $$\theta_i \equiv \alpha_i + u_i\eta$$ where $u_i$ are observed time-invariant individual characteristics with the corresponding vector of regression coefficients $\eta$, and $\alpha_i$ is the unobserved time-invariant heterogeneity. Estimate the first equation with the standard fixed effects estimator. Then Abowd et al. (1999) obtain the overall individual heterogeneity component as $$\widehat{\theta}_i = \overline{y}_i - \overline{x}_i\widehat{\beta}$$ The bar variables have been averaged over time for each individual. Now take the above equivalence and regress $$\widehat{\theta}_i = \text{constant} + u_i\eta + \text{error}_i$$ In order to identify the vector $\eta$ (the coefficients of your time-invariant observed variables), you must assume that $Cov(\alpha_i, u_i) = 0$. This is important. Given that $\alpha_i$ is unobserved it is included in the error term $\text{error}_i$ and therefore causes omitted variable bias if $Cov(\alpha_i, u_i) \neq 0$. Except for this you also need the strict exogeneity assumption $E[\epsilon_{it}|x_{it},\theta_i] = 0$ which you generally require in panel data methods.