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, 2019). 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 [...]

Introduction If you have run much publicly available GAUSS code, you have probably come across the #include command. Since it is used so much, it will be helpful to answer these questions: What does #include do? What is the most common error when using #include? How can I resolve the most common error? What does [...]

Introduction Most GAUSS users keep their code and data from different projects in separate directories. This is a good practice since it helps keep your work organized. However, since it is your code and your computer, it can seem like the path of least resistance is to add full path references to any data that [...]

Introduction Autocomplete is becoming a common feature in the tools we use in all aspects of our lives, because of it's ability to help us to type more accurately and quickly. When programming in GAUSS, the autocomplete can also show you new functions you were not aware of. Today we will discuss how to use [...]

Introduction The key to getting the most performance from a matrix language is to vectorize your code as much as possible. Vectorized code performs operations on large sections of matrices and vectors in a single operation, rather than looping over the elements one-by-one. For example, we could scale a vector by looping over each element: [...]