Learn how to work with matrices, the building block of the GAUSS programming language, in this third video in our GAUSS Basics series. Today we will explore how to:
GAUSS includes a plethora of tools for creating publication-quality graphics. Unfortunately, many people fail to use these tools to their full potential. Today we unlock five advanced GAUSS hacks for building beautiful graphics:
Using HSL, and Colorbrewer color palettes.
Controlling graph exports.
Changing the plot canvas size.
Annotating graphs with shapes, text boxes, and lines.
Using LaTeX for GAUSS legends, labels and text boxes.
This is the first video in our new GAUSS Basics series. This series is designed to teach you everything you need to know to be productive with GAUSS. This video covers interactive commands and is designed to be your first step in GAUSS!
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.
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.
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:
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.
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.
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.
