Dummy variables are a common econometric tool, whether working with time series, cross-sectional, or panel data. Unfortunately, raw datasets rarely come formatted with dummy variables that are regression ready.
In today’s blog, we explore several options for creating dummy variables from categorical data in GAUSS, including:
- Creating dummy variables from a file using formula strings.
- Creating dummy variables from an existing vector of categorical data.
- Creating dummy variables from an existing vector of continuous variables.
In this blog, we will explore how to set up and interpret cointegration results using a real-world time series example. We will cover the case with no structural breaks as well as the case with one unknown structural break using tools from the GAUSS tspdlib library.
Learn how to create reusable graphics profiles with a few clicks of your mouse.
Learn how to create a simple output table with variable names, parameter estimates and standard errors using sprintf in GAUSS.
We know that many of you need to work from home, unexpectedly, for an extended period of time. To help you keep working, Aptech is providing free temporary GAUSS 20 licenses.
Optional input arguments can make your statistical computing more efficient and enjoyable. GAUSS version 20
added a new suite of tools to make it easy for you to add optional input arguments to your GAUSS procedures. This blog lays the foundation to start using optional arguments in your GAUSS programs.
Cointegration is an important tool for modeling the long-run relationships in time series data. If you work with time series data, you will likely find yourself needing to use cointegration at some point. This blog provides an in-depth introduction to cointegration and will cover all the nuts and bolts you need to get started.