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.
Starting in GAUSS version 12, a new suite of high quality and high-performance random number generators was introduced. While new projects should always use one of the modern RNG’s, it is sometimes necessary to exactly reproduce some work from the past. GAUSS has retained a set of older LCG’s, which will allow you to reproduce the random numbers from older GAUSS versions for many distributions.
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.
