Eric has been working to build, distribute, and strengthen the GAUSS universe since 2012. He is an economist skilled in data analysis and software development. He has earned a B.A. and MSc in economics and engineering and has over 18 years of combined industry and academic experience in data analysis and research.
Classical linear regression estimates the mean response of the dependent variable dependent on the independent variables. There are many cases, such as skewed data, multimodal data, or data with outliers, when the behavior at the conditional mean fails to fully capture the patterns in the data. In these cases, quantile regression provides a useful alternative to linear regression. Today we explore quantile regression and use the GAUSS quantileFit procedure to analyze Major League Baseball Salary data.
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.
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).
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.
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.
