Update Discrete Choice Application Module
The latest Discrete Choice Analysis Tools 2.1.0 is now available for release. If you own Discrete Choice 2.0 the update is available for free.
Fundamental Bayesian Samplers
The posterior probability distribution is the heart of Bayesian statistics and a fundamental tool for Bayesian parameter estimation. Naturally, how to infer and build these distributions is a widely examined topic, the scope of which cannot fit in one blog. In this blog, we examine bayesian sampling using three basic, but fundamental techniques, importance sampling, Metropolis-Hastings sampling, and Gibbs sampling.
Marginal Effects of Linear Models with Data Transformations
We use regression analysis to understand the relationships, patterns, and causalities in data. Often we are interested in understanding the impacts that changes in the dependent variables have on our outcome of interest. However, not all models provide such straightforward interpretations. Coefficients in more complex models may not always provide direct insights into the relationships we are interested in.
In this blog, we look more closely at the interpretation of marginal effects in three types of models:
- Purely linear models.
- Models with transformations in independent variables.
- Models with transformations of dependent variables.
Panel Data Basics: One-way Individual Effects
In this blog, we examine one of the fundamentals of panel data analysis, the one-way error component model. We cover the theoretical background of the one-way error component model, we examine the fixed-effects and random-effects models, and provide an empirical example of both.
Introduction to Difference-in-Differences Estimation
When policy changes or treatments are imposed on people, it is common and reasonable to ask how those people have been impacted. This is a more difficult question than it seems at first glance. In today’s blog, we examine difference-in-differences (DD) estimation, a common tool for considering the impact of treatments on individuals.
Panel data, structural breaks and unit root testing
In this blog, we extend our analysis of unit root testing with structural breaks to panel data. Using panel data unit roots tests found in the GAUSS tspdlib we consider if a panel of international current account balances collectively shows unit root behavior.
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Unit Root Tests with Structural Breaks
In this blog, we examine the issue of identifying unit roots in the presence of structural breaks. We will use the quarterly US current account to GDP ratio to compare results from a number of unit root test found in the GAUSS tspdlib library including the: Zivot-Andrews (1992) unit root test with a single structural break, Narayan and Popp (2010) unit root test with two structural breaks, Lee and Strazicich (2013, 2003) LM tests with one and two structural breaks, Enders and Lee Fourier (2012) ADF and LM tests.
Running publicly available GAUSS code: Part 2
This week’s blog brings you the second video in the series examining running publicly available GAUSS code. This video runs the popular code by Hatemi-J for testing cointegration with multiple structural breaks. In this video you will learn how to:
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- Substitute your own dataset.
- Modify the indexing commands for your data.
- Remove missing values.
- Preview your data after loading with the Ctrl+E keyboard shortcut.
The Basics of Quantile Regression
Classical linear regression estimates the mean response of the dependent variable dependent on the independent variables. There are many cases, such as skewed data, multimodal data, or data with outliers, when the behavior at the conditional mean fails to fully capture the patterns in the data. In these cases, quantile regression provides a useful alternative to linear regression. Today we explore quantile regression and use the GAUSS quantileFit procedure to analyze Major League Baseball Salary data.
Basic Bootstrapping in GAUSS
The bootstrap is a commonly used resampling technique which involves taking random samples with replacement to quantify uncertainty about a particular estimator or statistic. In this post, we will walk the how to apply the bootstrap procedure using asset returns.