We show how the rootogram - a graphical tool associated with the work of J. W. Tukey and originally used for assessing goodness of fit of univariate distributions - can help to diagnose and treat issues such as overdispersion and/or excess zeros in regression models for count data. Two empirical...

The R package partykit provides a flexible toolkit for learning, representing, summarizing, and visualizing a wide range of tree-structured regression and classification models. The functionality encompasses: (a) basic infrastructure for representing trees (inferred by any algorithm) so that...

In explaining wage or income by personal attributes (e.g. educational attainment, age, and ethnicity) in a regression model, many researchers choose to use the log of wage or income as the dependent variable and then to estimate the unknown coefficients by some version of the least-squares...

When doing two-way fixed effects OLS estimations, both the variances and covariance of the fixed effects are biased. A formula for a bias correction is known, but in large datasets it involves inverses of impractically large matrices. We detail how to compute the bias correction in this case.

Stock economic time series, such as end-of-month inventories, arise as the cumulative sum of monthly inflows and outflows over time, i.e., as accumulations of monthly net flows. In this article, we derive holiday regressors for stock series from cumulative sums of flow-series holiday regressors....

This paper addresses the public and regulatory discussions on whether firms use derivatives to reduce or increase risk. The investigation is carried out to see whether firms hedge or speculate with derivatives by using a regression quantitative analysis of a sample of 433 firm quarters of 31...

Using Gretl, I apply ARMA, Vector ARMA, VAR, state-space model with a Kalman filter, transfer-function and intervention models, unit root tests, cointegration test, volatility models (ARCH, GARCH, ARCH-M, GARCH-M, Taylor-Schwert GARCH, GJR, TARCH, NARCH, APARCH, EGARCH) to analyze quarterly time...

This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the high-dimensional setting where the number of...

This paper extends wavelet methodology to handle co-movement dynamics of multivariate time series via moving weighted regression on wavelet coefficients. The concept of wavelet local multiple correlation is used to produce one single set of multi-scale correlations along time, in contrast with...

This article describes qregsel, a Stata module to implement a copula-based sample selection correction for quantile regression recently proposed by Arellano and Bonhomme (2017, Econometrica 85(1): 1-28). The command allows the user to model selection in quantile regressions using either a...