This video will guide you through: Creating folders for a GAUSS project. Opening your code in the Project Folders Window. Running the code. The Applications Installer. Setting your working directory. Error G0290 Library not found. Error G0014 File not found. Viewing workspace variables. Next: Running Public GAUSS Code: Part 2
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 bootstrap is a commonly used resampling technique which involves taking random samples with replacement to quantify uncertainty about a particular estimator or statistic. Goals In this post, we will apply the bootstrap procedure to asset returns. Our data will be annual returns from the S&P 500 and the 10 year US Treasury Bond [...]
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 Permutation Entropy (PE) is a robust time series tool which provides a quantification measure of the complexity of a dynamic system by capturing the order relations between values of a time series and extracting a probability distribution of the ordinal patterns (see Henry and Judge, 2018). Among its main features, the PE approach: Is [...]
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 G0121: Matrix not positive definite and G0048: Matrix singular are common errors encountered during estimation. Today we will run some code to compute OLS estimates, using real data from some golf shots hit by this author and recorded by a launch monitor. The data Our dataset, golf_ballflight.csv, contains 46 observations with the following variables: [...]
Introduction Last week we learned how to use the date keyword to load dates into GAUSS. Today, we will plot some high-frequency Forex data. The data Today's dataset (usdcad_tick.csv) contains tick data for a little over 30,000 observations of the bid price for the USD/CAD currency pair from January 2, 2018. This file has two [...]
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 [...]
