Author: Erica

Marginal Effects of Linear Models with Data Transformations

Introduction We use regression analysis to understand the relationships, patterns, and causalities in data. Often we are interested in understanding the impacts that changes in the dependent variables have on our outcome of interest. Marginal effects measure the impact that an instantaneous unit change in one variable has on the outcome variable while all other [...]

Panel Data Basics: One-way Individual Effects

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 to Difference-in-Differences Estimation

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 [...]

Five Hacks For Creating Custom GAUSS Graphics

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, [...]

Unit Root Tests with Structural Breaks

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 [...]

The Basics of Quantile Regression

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 [...]

Top five hotkeys to get more done in GAUSS

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+/. [...]
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Apples to Apples: The case for cluster-robust standard errors

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. [...]
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A Simple Test for Structural Breaks in Variance

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).
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