Recent Posts

Graph high frequency Forex data

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 [...]

Reading dates and times in GAUSS

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 [...]

What you need to know about #include

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 [...]
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Make your code portable: Data paths

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 [...]
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Controlling the GAUSS Autocomplete Behavior

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 [...]
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Repeating simulations from older versions of GAUSS

Introduction Starting in GAUSS version 12, a new suite of random number generators was introduced. GAUSS now contains several options of high quality and high-performance random number generators (RNG), such as: The Mersenne-Twister (MT-19937, SFMT-19937 and MT-2208). Pierre L'ECuyer's MRG32K3a. Niederreiter and Sobol quasi-random number generators. GAUSS version 11 and older used a linear congruential [...]

The Effects of Structural Breaks on GMM models

Introduction 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 [...]
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