Interaction term stata 12
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- #Interaction term stata 12 install
- #Interaction term stata 12 code
- #Interaction term stata 12 series
Apply asreg command with fmb option An Example
#Interaction term stata 12 install
Install asreg from ssc with this line of code: ssc install asregģ. Arrange the data as panel data and use xtset command to tell Stata about it.Ģ. Consider the following three steps for estimation of FMB regression in Stata.ġ. The Fama-McBeth (FMB) can be easily estimated in Stata using asreg package. However, if both cross-sectional and time-series dependencies are suspected in the data set, then Newey-West consistent standard errors can be an acceptable solution.
#Interaction term stata 12 series
This is generally an acceptable solution when there is a large number of cross-sectional units and a relatively small time series for each cross-sectional unit. The standard errors are adjusted for cross-sectional dependence. The first step involves estimation of N cross-sectional regressions and the second step involves T time-series averages of the coefficients of the N-cross-sectional regressions. The Fama-McBeth (1973) regression is a two-step procedure. Paid Help – Frequently Asked Questions (FAQs).This might be somewhat counterintuitive to the overall regression syntax, as outside of interaction terms, Stata’s -regression- command assumes variables are continuous.
#Interaction term stata 12 code
The code above does this with the education variable. Because the hashtag code assumes the variables in the interaction term are categorical, it is necessary to define numerical variables as numerical with the -c.- prefix. In Stata, -i.- indicates that the variable is categorical, and -c.- indicates a continuous variable. While using hashtags is simpler than generating the interaction term as a new variable, there is a necessary rule to remember: use the variable prefixes. However, a simpler way is to use two hashtags: To include the main effects using hashtags, we can write them in as -reg wage grade i.race i.race#c.grade. But if we include the main effects, then we can see the pure relationship between wages and the interaction of education and minority status, since the model will hold the main effects constant in calculating the interaction coefficient. In other words, some of the effect we see from the interaction term may be from an independent main predictor “hiding” in the interaction term. If we only include the interaction term without the main effects, then the observed effect of the interaction term might be masking the true effect from one of the main predictors. Running a model like this however, is generally ill-advised. Although the coding for this output is relatively painless, Stata offer a quicker way to run models with interaction terms using hashtags:Īs the figure shows, if one hashtag is used, Stata runs a model only with the interaction term. The output suggests that minorities gain 15 cents more per hour than whites for every additional year of education they receive, ceteris paribus, even though minorities make $2.47 less per hour than whites overall. The most intuitive way to do so is to generate the interaction term as a new variable: This doesn’t mean that minorities have higher wages than whites (β 2 tells us that), but that minorities derive more wage-generating value from education than whites.Ĭonducting analysis with interaction terms is straightforward in Stata. If β 3 > 0, then minorities earn more per hour than Caucasians for every additional unit of education they receive, controlling for the other predictors. Β 3 tells us the effect of education on hourly wage by race. Wage = β 0 + β 1Education + β 2Minority + β 3Education*Minority + ε To consider an interaction term, we simply create a new variable with the two terms multiplied together: It’s possible that minority wages rises higher for every additional “unit” of education than it does for whites. For instance, when testing how education and race affect wage, we might want to know if educating minorities leads to a better wage boost than educating Caucasians. In regression analysis, it is often useful to include an interaction term between different variables.