Erica has been working to build, distribute, and strengthen the GAUSS universe since 2012. She is an economist skilled in data analysis and software development. She has earned a B.A. and MSc in economics and engineering and has over 15 years combined industry and academic experience in data analysis and research.
Introduction In this blog, we examine one of the fundamentals of panel data analysis, the one-way error component model. Today we will: Explain the theoretical one-way error component model. Consider fixed effects vs. random effects. Estimate models using an empirical example. The theoretical one-way error component model The one-way error-component model is a panel data [...]
Introduction When policy changes or treatments are imposed on people, it is common and reasonable to ask how those people have been impacted. This is a more difficult question than it seems at first glance. In order to truly know how those individuals have been impacted, we need to consider how those individuals would be [...]
Introduction GAUSS includes a plethora of tools for creating publication-quality graphics. Unfortunately, many people fail to use these tools to their full potential. Today we unlock five advanced GAUSS hacks for building beautiful graphics: Using HSL, HSLuv, and Colorbrewer color palettes. Controlling graph exports. Changing the plot canvas size. Annotating graphs with shapes, text boxes, [...]
Introduction In this blog, we extend last week's analysis of unit root testing with structural breaks to panel data. We will again use the quarterly current account to GDP ratio but focus on a panel of data from five countries: United States, United Kingdom, Australia, South Africa, and India. Using panel data unit roots tests [...]
Introduction In this blog, we examine the issue of identifying unit roots in the presence of structural breaks. We will use the quarterly US current account to GDP ratio to compare results from a number of unit root test found in the GAUSS tspdlib library including the: Zivot-Andrews (1992) unit root test with a single [...]
Introduction 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 [...]
Introduction 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. Toggle block comments on and off using Ctrl+/. [...]
Introduction 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. Example: Weather shocks to apple orchards For example, consider a model of the supply of apples from various orchards across the United States. [...]
Introduction 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 [...]
Introduction 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 [...]
