Category: Time Series

Permutation Entropy

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 [...]

A Simple Test for Structural Breaks in Variance

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 [...]

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 [...]

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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