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).
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.
The key to getting the most performance from a matrix language is to vectorize your code as much as possible. Vectorized code performs operations on large sections of matrices and vectors in a single operation, rather than looping over the elements one-by-one. In this blog, we learn how to use the GAUSS recserar function to vectorize code and simulate a time series AR(1) model.
Many estimations and forecasting methods are not valid if the mean and variance are not constant across time. Today we examine how to test for both using GLS-unit root tests with multiple structural breaks.
While structural breaks are a widely examined topic in pure time series, their impacts on panel data models have garnished less attention.
However, in their forthcoming paper Chowdhury and Russell (2018)] demonstrate that structural breaks can cause bias in the instrumental variable panel estimation framework.
This work highlights that structural breaks shouldn’t be limited to pure time series models and warrant equal attention in panel data models.
