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Econometrics

tempdisagg

Temporal disaggregation methods are used to disaggregate and interpolate a low frequency time series to a higher frequency series. This can be done without additional information or by exploiting the information contained in one or more indicators. All disaggregation methods ensure that either the sum, the average, the first or the last value of the resulting high frequency series is consistent with the low frequency series.

mvProbit

Tools for multivariate probit models

cotrend

Implements cointegration/cotrending rank selection algorithm in Guo and Shintani(2011). Paper: "Consistant Cotrending rank selection when both stochastic and nonlinear deterministic trends are present", Preprint, Feb 2011.

lfe

Transforms away factors with many levels prior to doing an OLS. Useful for estimating linear models with multiple group fixed effects, and for estimating linear models which uses factors as pure control variables.

apt

This package focuses on asymmetric price transmission (APT) between two time series. It contains functions for linear and nonlinear threshold cointegration, and furthermore, symmetric and asymmetric error correction model.

erer

This package contains functions and datasets for the book of 'Empirical Research in Economics: Growing up with R' by Dr. Changyou Sun. These functions can calculate marginal effects for a binary probit or logit model, estimate static and dynamic Almost Ideal Demand System (AIDS) models, and conduct event analysis.

censReg

Estimation of censored regression (Tobit) models with cross-section and panel data

micEconSNQP

Production analysis with the Symmetric Normalized Quadratic (SNQ) profit function

micEconCES

Tools for economic analysis and economic modelling with a Constant Elasticity of Scale (CES) function

ordinal

This package implements cumulative link (mixed) models also known as ordered regression models, proportional odds models, proportional hazards models for grouped survival times and ordered logit/probit/... models. Estimation is via maximum likelihood and mixed models are fitted with the Laplace approximation and adaptive Gauss-Hermite quadrature. Multiple random effect terms are allowed and they may be nested, crossed or partially nested/crossed. Restrictions of symmetry and equidistance can be imposed on the thresholds (cut-points). Standard model methods are available (summary, anova, drop-methods, step, confint, predict etc.) in addition to profile methods and slice methods for visualizing the likelihood function and checking convergence.