Model formulas are the standard approach for specifying the variables in statistical models in the S language. Although being eminently useful in an extremely wide class of applications, they have certain limitations including being confined to single responses and not providing convenient support for processing formulas with multiple parts. The latter is relevant for models with two or more sets of variable, e.g., regressors/instruments in instrumental variable regressions, two-part models such as hurdle models, or alternative-specific and individual-specific variables in choice models among many others. The R˜package Formula addresses these two problems by providing a new class “Formula ” (inheriting from “formula”) that accepts an additional formula operator | separating multiple parts and by allowing all formula operators (including the new |) on the left-hand side to support multiple responses. Keywords:˜formula processing, model frame, model matrix, R. 1.

This paper shows how to estimate models by the generalized method of moments and the generalized empirical likelihood using the R package gmm. A brief discussion is offered on the theoretical aspects of both methods and the functionality of the package is presented through several examples in economics and finance. Keywords:˜generalized empirical likelihood, generalized method of moments, empirical likelihood, continuous updated estimator, exponential tilting, exponentially tilted empirical likelihood, R. 1.

This introduction to the R package meboot is a (slightly) modified version of Vinod and López-de-Lacalle (2009), published in the Journal of Statistical Software. The maximum entropy bootstrap is an algorithm that creates an ensemble for time series inference. Stationarity is not required and the ensemble satisfies the ergodic theorem and the central limit theorem. The meboot R package implements such algorithm. This document introduces the procedure and illustrates its scope by means of several guided applications.

These are exercises to accompany the above-mentioned book having the

These are exercises to accompany the above-mentioned book having the

This introduction to the R package pcse is a (slightly) modified version of Bailey and Katz (2011), published in the Journal of Statistical Software. Time-series–cross-section (TSCS) data are characterized by having repeated observations over time on some set of units, such as states or nations. TSCS data typically display both contemporaneous correlation across units and unit level heteroskedasity making inference from standard errors produced by ordinary least squares incorrect. Panel-corrected standard errors (PCSE) account for these these deviations from spherical errors and allow for better inference from linear models estimated from TSCS data. In this paper, we discuss an implementation of them in the R system for statistical computing. The key computational issue is how to handle unbalanced data.

We demonstrate how censored regression models (including standard Tobit models) can be estimated in R using the add-on package censReg. This package provides not only the usual maximum likelihood (ML) procedure for cross-sectional data but also the random-effects maximum likelihood procedure for panel data using Gauss-Hermite quadrature. Keywords:˜censored regression, Tobit, econometrics, R. 1.

Logo für den Einsatz in nationalen bzw.

Funding for this research was provided by the NEXTRANS Center, Purdue University under