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关 键 词：stata软件
行 业：IT 软件 教学管理软件
发布时间：2024-04-17
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What is the probability of default for a low default portfolio? Such questions can be answered naturally within the Bayesian paradigm, which can estimate the probability of any hypothesis of interest. See What is Bayesian analysis? to learn more.
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.
Stata 16 has a new suite of commands for performing meta-analysis. This suite lets you explore and combine the results from different studies. For instance, if you have collected results from 20 studies about the effect of a particular drug on blood pressure, you can summarize these studies and estimate the overall effect using meta-analysis.
Galbraith plots
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