This blog provides a non-technical look at impulse response functions and forecast error variance decomposition, both integral parts of vector autoregressive models.
If you’re looking to gain a better understanding of these important multivariate time series techniques you’re in the right place.
We cover the basics, including:
What is structural analysis?
What are impulse response functions?
How do we interpret impulse response functions?
What is forecast error variance decomposition?
How do we interpret forecast error variance decomposition?
In today’s blog, you’ll learn the basics of the vector autoregressive model. We lay the foundation for getting started with this crucial multivariate time series model and cover the important details including:
Learn why TSPDLIB 2.0 is the easiest and most comprehensive time series and panel data unit root and cointegration testing package on the market. The tspdlib 2.0 package includes expanded functions for time series and panel data testing in the presence of structural breaks. In addition, TSPDLIB 2.0 is easier than ever to use with new implementation of default parameter settings, updated output printing, and automatic date variable detection.
In today’s blog, we look at how to save time and reduce errors using GAUSS’s new data management tools.
Using the quarterly real GDP dataset from the FRED database we explore GAUSS’s new data management tools.
In particular, we examine how to:
In this blog, we will explore how to set up and interpret cointegration results using a real-world time series example. We will cover the case with no structural breaks as well as the case with one unknown structural break using tools from the GAUSS tspdlib library.
Cointegration is an important tool for modeling the long-run relationships in time series data. If you work with time series data, you will likely find yourself needing to use cointegration at some point. This blog provides an in-depth introduction to cointegration and will cover all the nuts and bolts you need to get started.
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 time series data unit root tests using GAUSS.
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.
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 tspdblib 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 presence of structural breaks, and panel data causality tests. It is available for direct installation using the GAUSS Package Manager.
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 structural break, Narayan and Popp (2010) unit root test with two structural breaks, Lee and Strazicich (2013, 2003) LM tests with one and two structural breaks, Enders and Lee Fourier (2012) ADF and LM tests.