Bayesian Estimation of Nelson-Siegel model using rjags R package

This post shows how to use rjags R package to estimate Nelson-Siegel yield curve model based using a Bayesian MCMC in a compact way.

Bayesian Estimation by using rjags R Package

This post shows how to use rjags R package to perform Bayesian MCMC estimation. As an example, we just select a multiple linear regression but rjags can handle lots of highly non-linear models so that it can be extended to various modelings including hierarchical models.

Multiple Linear Regression using Tensorflow Optimization

This post implements the optimization based estimation of multiple linear regression model using Tensorflow. With this example, we can learn basic implementations of functions in Python and a numerical optimization in Tensorflow.

Multiple Linear Regression using Tensorflow

This post implements the standard matrix based estimation of multiple linear regression model using Tensorflow. With this example, we can learn some basic vector or matrix operations in Tensorflow and also Python.

Understanding Hamilton Regime Switching Model using R package

This post explains how to implement a regime (Markov) switching model which is based on Hamilton (1989). The estimation is done by 1) predicting states by each regimes, 2) constructing a likelihood from data and state predictions, and 3) updating states. This is the Hamilton filter which is a kind of Bayesian updating procedure.

Vector Autoregressive Model (VAR) using R

This post gives a brief introduction to the estimation and forecasting of a Vector Autoregressive Model (VAR) model using R . We use vars and tsDyn R package and compare these two estimated coefficients. We also consider VAR in level and VAR in difference and compare these two forecasts.

Understanding Logistic Regression

This post explains the logistic regression and implements R code for the estimation of its parameters.

Sign Constrained Lasso with R code

This post explains how to implement the sign constrained lasso with ridge, and linear regression model. The restrictions of expected sign is of great importance in the case when building an econometric model with meaningful interpretation. We can easily incorporate sign restrictions to the above regression models using glmnet R package.

Lasso Regression Model with R code

Tibshirani (1996) introduces the so called LASSO (Least Absolute Shrinkage and Selection Operator) model for the selection and shrinkage of parameters. This model is very useful when we analyze big data. In this post, we learn how to set up the Lasso model and estimate it using glmnet R package.

Bayesian Linear Regression with Gibbs Sampling using R code

Sang-Heon Lee

This article explains how to estimate parameters of the linear regression model using the Bayesian inference. Our focus centers on user-friendly intuitive understanding of Bayesian estimation. From some radical point of view, we regard the Bayesian model as the average of multiple models generated with slightly different parameter set. We derive posterior distributions of parameters and perform estimation and simulation via Gibbs sampling using R code.