Statistical Software Components

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TFDIFF: Stata module to compute pre- and post-treatment estimation of the Average Treatment Effect (ATE) with fixed binary treatment

tfdiff estimates Average Treatment Effects (ATEs) when the treatment is binary and fixed to a specific point in time. It assumes the availability of a panel dataset where the same treated and untreated units are observed over time. Using tfdiff, the user can estimate the pre- and post-intervention effects by selecting the intervention time t. The results are plotted in an easy-to-read graphical representation. In order to assess the reliability of the causal results achieved by the user's specif...

SRTREE: Stata module to implement regression trees via optimal pruning, bagging, random forests, and boosting methods

srtree is a Stata wrapper for the R functions "tree()", "randomForest()", and "gbm()". It allows to implement the following regression tree models: (1) regression tree with optimal pruning, (2) bagging, (3) random forests, and (4) boosting.

The xtpsse command runs a conditional fixed-effects Poisson panel regression, computes sandwich and spatial standard errors, and tests for time-invariant spatial dependence according to Bertanha and Moser (2016).

icio measures trade in value-added and participation in GVCs of countries and sectors by exploiting Inter-Country Input-Output (ICIO) tables. It provides decompositions of aggregate, bilateral and sectoral exports and imports according to the source and the destination of their value-added content. The command allows to work directly with the most popular ICIO tables - WIOD (Timmer et al. 2015), TIVA (OECD), EORA (Lenzen et al. 2013); in addition, any other user-provided ICIO table can be loaded...

METAPREG: Stata module to compute fixed and random effects meta-analysis and meta-regression of proportions

This routine provides procedures for pooling proportions in a meta-analysis of multiple studies study and/or displays the results in a forest plot. The pooled estimates are a weighted averages or obtained parameter estimates after logistic regression or after fitting the logistic-normal random model. Metapreg extends the functionality of metaprop by allowing one or more covariates into the model to explain heterogeneity in the proportions. With paired data, a model with two additive components o...

xtss estimates the parameters of a linear latent variable model, where the observed outcome remains unchanged from the previous period, if the difference relative to the current value of the latent variable is within stochastic (S,s) thresholds. The upper-S and lower-s thresholds are normally distributed and truncated at zero and can depend on time-varying covariates. This estimator is based on Fougere et al (2010) and Dhyne et al (2011) who study price rigidity.

This package provides extended functionalities for extracting the trend and cyclical components from time series using the Hodrick-Prescott filter. It implements the popular two-sided version as well as the one-sided version of the HP filter described in Stock and Watson (1999). The package is also able to find the optimal smoothing parameter for each series as the ratio of the variance of innovations to the series and the variance of innovations to the trend, as originally proposed by Hodrick a...

ULTIMATCH: Stata module to implement Nearest Neighbor, Radius, Coarsened Exact, Percentile Rank and Mahalanobis Distance Matching

ultimatch implements various score and distance based matching methods, i.e. Nearest Neighbor, Radius, Coarsened Exact, Percentile Rank and Mahalanobis Distance Matching. It implements an efficient method for distance based matching like Mahalanobis matching preventing the quadratic increment of the runtime. Matched observations are marked individually allowing interactions between treated and counterfactuals. Different methods can be combined to improve the results and/or to impose external req...

frameappend appends the contents of {framename} to current frame. The new observations will be at the bottom of the current frame.

heatplot creates heat plots from variables or matrices. One example of a heat plot is a two-dimensional histogram in which the frequencies of combinations of binned Y and X are displayed as rectangular (or hexagonal) fields using a color gradient. Another example is a plot of a trivariate distribution where the color gradient is used to visualize the (average) value of Z within bins of Y and X. Yet another example is a plot that displays the contents of a matrix, say, a correlation matrix or a s...

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