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.
Time series data with inconsistently formatted dates and times can make your work frustrating. Dates and times are often stored as strings or text data and converting to a consistent, numeric format might seem like a daunting task. Fortunately, GAUSS includes an easy tool for loading and converting dates and times – the `date` keyword.
If you have run much publicly available GAUSS code, you have probably come across the `#include` command. In this blog, we answer some important questions about #include:
What does `#include` do?
What is the most common error when using `#include`?
In this blog, we explore data path best practices for making GAUSS code more portable and replicable. Using variables and predefined GAUSS path definitions, we show how to simplify code and easily customize data loading.
