Category: Time Series

How to Conduct Unit Root Tests in GAUSS

Introduction In time series modeling we often encounter trending or nonstationary time series data. Understanding the characteristics of such data is crucial for developing proper time series models. For this reason, unit root testing is an essential step when dealing with time series data. In this blog post, we cover everything you need to conduct [...]

Introduction to the Fundamentals of Time Series Data and Analysis

The statistical characteristics of time series data often violate the assumptions of conventional statistical methods. Because of this, analyzing time series data requires a unique set of tools and methods, collectively known as time series analysis. This article covers the fundamental concepts of time series analysis and should give you a foundation for working with time series data. Everything is covered from time series plotting to time series modeling.

New release of tspdlib 1.0

The preliminary econometric package for Time Series and Panel Data Methods has been updated and functionality has been expanded in this first official release of tspdlib 1.0. The tspdlib 1.0 package includes functions for time series unit root tests in the presence of structural breaks, time series and panel data unit root tests in the [...]

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 [...]

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, 2019). Among its main features, the PE approach: Is [...]

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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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 [...]

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