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.
The GAUSS interface includes a number of often overlooked hotkeys and shortcuts. These features can help make programming more efficient and navigation seamless. In this blog I highlight my top five GAUSS hotkeys:
Quickly view data symbols using Ctrl+E.
Open floating command reference pages using Shift+F1.
Permutation Entropy (PE) is a robust time series tool which provides a quantification measure of the complexity of a dynamic system by capturing the order relations between values of a time series and extracting a probability distribution of the ordinal patterns (see Henry and Judge, 2019). Today, we will learn about the PE methodology and will demonstrate its use through a toy example.
Linear regression commonly assumes that the error terms of a model are independently and identically distributed (i.i.d) However, when datasets contain groups, the potential for correlated error terms within groups arises. In this blog, we explore how to remedy this issue with clustered error terms.
Though many standard econometric models assume that variance is constant, structural breaks in variance are well-documented, particularly in economic and finance data. If these changes are not accurately accounted for, they can hinder forecast inference measures, such as forecast variances and intervals. In this blog, we consider a tool that can be used to help locate structural breaks in variance — the iterative cumulative sum of squares algorithm(ICSS) (Inclan and Tiao, 1994).
`G0121: Matrix not positive definite` and `G0048: Matrix singular` are common errors encountered during estimation. Today we will learn how to diagnose these errors using GAUSS code to compute ordinary least squares estimates, using real data from some golf shots hit by this author and recorded by a launch monitor.
Last week we learned how to use the `date` keyword to load dates into GAUSS. Today, we extend our analysis of time series data to plot high-frequency Forex data.
