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).
Tagged in
ICSS, structural breaks, time series
`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.
Tagged in
error, positive definite, singular matrix
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`?
- How can I resolve the most common error?
