An Extensible Suite of High-Performance and Low-Dependency Packages for Statistical Computing and Data Manipulation in R

R package for automation of machine learning, forecasting, model evaluation, and model interpretation

R package to easily build panel data sets from the PSID

Bayesian Inference of Complex Panel Data

an R package for testing, estimating and evaluating the Panel Smooth Transition Regression (PSTR) model.

TVP panel data model featuring time-varying network dependence introduced in "Bayesian state-space modeling for analyzing heterogeneous network effects of US monetary policy"

R tools related to panel data analysis and optimization

Replication code for "RNN-based counterfactual prediction, with an application to homestead policy and public schooling"

Solution for ENS - Societe Generale Challenge (1st place).

Scripts to the build a balanced panel of the 2013 NYC Taxi Data

provides R codes for all the experiments in the paper "Panel Smooth Transition Regression Models"

code repo for "Performance evaluation for forecasting modeling with spatiotemporal structures in data"

Approximate Bayesian inference for mixed effects models with heterogeneity

semiparametric 2-period DiD estimators for the ATT

Using Fixed Effect, Random Effect and Hausman Taylor IV to estimate the impacts on wage

This repository includes different R scripts (with the data used) for the study and application of different topics from the study of Econometrics.

Function for creating time-series plots using ggplot. Updated from legacy project r-ggplotspaghetti

As part of this project, we have used Regression Analysis on top of a panel data on Guns in USA to determine the "Effect of Shall-Carry Law on Violence Rate and Incarceration Rate in United States".

This repository contains code for 2400-DS1AE-Advanced-Econometrics class project, which titled: Family Background in Returns to Education: An Empirical Analysis of Family’s Impact on Personal Achievement.

Dynamic Panel Multiple Threshold Model with Fixed Effects Based on Maximum Likelihood Estiamtion and MCMC