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H2O integration
Start a new H2O cluster or connect to an existing one.
Manipulate data on an H2O cluster.
Access the capabilities of H2O directly in Stata.
One of the appeals for using Bayesian methods in econometric modeling is to incorporate the external information about model parameters often available in practice. This information may come from historical data, or it may come naturally from the knowledge of an economic process.
All we did to change from the first table to the second table was change the style. The two styles we applied to our collection produced distinctly different tables. The second style even puts the coefficient statistics side by side rather than above and below.
Nonparametric series regression
Stata 16's new npregress series command fits nonparametric series regressions that approximate the mean of the dependent variable using polynomials, B-splines, or splines of the covariates. This means that you do not need to specify any predetermined functional form. You specify only which covariates you wish to include in your model. For instance, type
. npregress series wineoutput rainfall temperature i.irrigation
Instead of reporting coefficients, npregress series reports effects, meaning average marginal effects for continuous variables and contrasts for categorical variables. The results might be that the average marginal effect of rainfall is 1 and the contrast for irrigation is 2. This contrast can be interpreted as the average treatment effect of irrigation.
Being a nonparametric regression, the unknown mean is approximated by a series function of the covariates. And yet we can still obtain the inferences that we could from a parametric model. We use margins. We could type
. margins irrigation, at(temperature=(40(5)90))
and obtain a table of the expected effect of having irrigation at temperatures of 40, 50, ..., 90 degrees. And we could graph the result using marginsplot.
Even more, npregress series can fit partially parametric (semiparametric) models.
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