Empirical specification template

A helpful, five-step template!¹

1. First, I state what in broad terms type of model I am using (probably OLS!) and what I am going to do with it. If I’m using a fancy speciifcation like a difference-in-differences or panel data, now is a good time to mention this.

I use ordinary least squares to estimate the relationship between ice cream flavor consumed and risk aversion, using state-level fixed effects to control for time-invariant determinants of risk aversion that may vary by state.

2. Then, I’m going to write the population model I am estimating in equation form. I’m going to use appropriate subscripts:

\(outcome_{is} = \beta_0 + \beta_1 VariableName_{is} + \beta_2 AnotherVariableName_{2,is} + f_s + u_{is}\)

• Use descriptive names (these don’t actually have to be your variable names)
• If you use fixed effects, you can notate these without having to expand them:
  • adding state fixed effects could be written as $ … + f_s + …$, for example
  • or, you could add them this way: \(\sum_{s=1}^{50}D_s\) for a set of 50 state-level fixed effects (with one omitted)
• List covariates you include. If you have a ton of these, you could include a vector of individual-specific covariates (\(X'\gamma\), for example) and then list them in paragraph form. However, this isn’t likely for most papers.

3. Then, I’m going to define my independent and dependent variables in a list or paragraph form. I’m going to also describe what the subscripts are

Where \(outcome_{is}\) is measured risk aversion for individual \(i\) living in state \(s\), following the scale described above. \(VariableName_{is}\) is a measure of flavor intensity, standardized around Edy’s French Vanilla …

I’ll want to include any controls and fixed effects.

4. I’ll mention any special things I do when coding (missing value flags, etc), and what types of standard errors I’m using

5. Now, I write how I will interpret my coefficient of interest (causal, correlational, etc.) - what will the coefficients on my key indicator variables tell me? Are there any key identifying assumptions at play?

If you are using multiple specifications, then you have two options:

• If it’s a matter of adding additional controls, then mention that you will also add them in a second model in part (3).
• If it’s a fundamentally different population model, then include a second model, and define any terms that were not previously defined. Make sure to discuss how the interpretation of results would differ.

Working with outreg2

Download sample code, which uses graduation.dta

Video

The introduction formula

Listen to Keith Head and follow “The Introduction Formula”

Links