计量经济分析 = Econometric analysis

Multivariate imputation by chained equations (MICE) has emerged as a principled method of dealing with missing data. Despite properties that make MICE particularly useful for large imputation procedures and advances in software development that now make it accessible to many researchers, many psychiatric researchers have not been trained in these methods and few practical resources exist to guide researchers in the implementation of this technique. This paper provides an introduction to the MICE method with a focus on practical aspects and challenges in using this method. A brief review of software programs available to implement MICE and then analyze multiply imputed data is also provided.

Multiple Imputation by Chained Equations: What is it and how does it work?

Survival Analysis: Techniques for Censored and Truncated Data (2nd ed.) (Book)

In this article, we introduce a Stata implementation of coarsened exact matching, a new method for improving the estimation of causal effects by reduc- ing imbalance in covariates between treated and control groups. Coarsened exact matching is faster, is easier to use and understand, requires fewer assumptions, is more easily automated, and possesses more attractive statistical properties for many applications than do existing matching methods. In coarsened exact match- ing, users temporarily coarsen their data, exact match on these coarsened data, and then run their analysis on the uncoarsened, matched data. Coarsened exact matching bounds the degree of model dependence and causal effect estimation er- ror by ex ante user choice, is monotonic imbalance bounding (so that reducing the maximum imbalance on one variable has no effect on others), does not require a separate procedure to restrict data to common support, meets the congruence prin- ciple, is approximately invariant to measurement error, balances all nonlinearities and interactions in sample (i.e., not merely in expectation), and works with mul- tiply imputed datasets. Other matching methods inherit many of the coarsened exact matching method's properties when applied to further match data prepro- cessed by coarsened exact matching. The cem command implements the coarsened exact matching algorithm in Stata.

/pdf/cem-coarsened-exact-matching-in-stata-26y7n9wqor.pdf

cem: Coarsened exact matching in Stata

As Long & Freese show, it can often be helpful to compute predicted/expected values for hypothetical or prototypical cases. Stata 11 introduced new tools for making such calculations â€“ factor variables and the margins command. These can do many of the things that were previously done by Stataâ€™s own adjust and mfx commands, as well as Long & Freeseâ€™s spost9 commands like prvalue. Unfortunately, the complexity of the margins syntax, the daunting 50 page reference manual entry that describes it, and a lack of understanding about what margins offers over older commands may have dissuaded researchers from using it. This paper therefore shows how margins can easily replicate analyses done by older commands. It demonstrates how margins provides a superior means for dealing with interdependent variables (e.g. X and X^2; X1, X2, and X1 * X2; multiple dummies created from a single categorical variable), and is also superior for data that are svyset. The paper explains how the new asobserved option works and the substantive reasons for preferring it over the atmeans approach used by older commands. The paper primarily focuses on the computation of adjusted predictions but also shows how margins has the same advantages for computing marginal effects.

Using the Margins Command to Estimate and Interpret Adjusted Predictions and Marginal Effects

Stata base reference manual

Stata multiple-imputation reference manual

Stata structural equation modeling reference manual , Stata structural equation modeling reference manual , کتابخانه مرکزی دانشگاه علوم پزشکی ایران

Stata structural equation modeling reference manual

Stata Multivariate Statistics Reference Manual

Stata longitudinal-data/panel-data reference manual : release 11.

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