Getting Started with Time Series in GAUSS
In this video, you’ll learn the basics of time series analysis in GAUSS. See how quick and easy it is to get started with everything from data loading to ARIMA analysis!
You’ll see first hand how to :
- Load and verify time series data.
- Filter observations by date.
- Merge data from different sources.
- Create basic time series plots.
- Perform stationarity testing.
- Fit a basic ARIMA model.
The Quantile Autoregressive-Distributed Lag Parameter Estimation and Interpretation in GAUSS
The QARDL model has grown increasingly popular in time series analysis. It is a convenient model for addressing autocorrelation, disentangling long-term and short-term relationships, and addressing asymmetric relationships.
In today’s blog, we look at the basics of the QARDL model including:
- The intuition behind the QARDL model.
- How to estimate the QARDL model in GAUSS.
- How to interpret the QARDL results.
The Structural VAR Model at Work: Analyzing Monetary Policy
In today’s blog, we put the building blocks of the structural vector autoregressive (SVAR) model to work in a practical application. We’ll use one of the most common applications of SVAR models, monetary policy analysis, to see the SVAR in action. After this blog, you should have a stronger understanding of:
Tagged in
- How to use Granger causality testing to inform model selection.
- How to implement short-run identification restrictions.
- How to conduct and interpret structural VAR analysis.
How to Run the Fourier LM Test (Video)
Learn everything you need to know to run the Fourier LM unit root test with your data and interpret the results.
Tagged in
Introduction to Markov-Switching Models
Markov-switching models offer a powerful tool for capturing the real-world behavior of time series data. Today’s blog provides an introduction to Markov-switching models including:
Tagged in
- What a regime switching model is and how it differs from a structural break model.
- When we should use the regime switching model.
- What a Markov-switching model is.
- What tools we use to estimate Markov-switching models.
Understanding and Solving the Structural Vector Autoregressive Identification Problem
The structural vector autoregressive model is a crucial time series model used to understand and predict economic impacts and outcomes. In this blog, we look closely at the identification problem posed by structural vector autoregressive models and its solution. In particular, we cover:
Tagged in
- What is the structural VAR model and what is the reduced form VAR?
- What is the relationship between structural VAR and reduced form VAR models?
- What is the structural VAR identification problem?
- What are common solutions to the structural VAR identification problem?
Introduction to Granger Causality
Multivariate time series analysis turns to VAR models not only for understanding the relationships between variables but also for forecasting. In today’s blog, we look at how to improve VAR model selection and achieve better forecasts using Granger-causality.
We explore the questions:
- What is Granger-causality?
- When to use Granger causality?
- How to use Granger causality?
Dates and Times Made Easy
Working with dates in data analysis software can be tedious and error-prone. The new GAUSS date type, introduced in GAUSS 21, can save you time and prevent frustration and errors.
The date data type is part of the GAUSS dataframe alongside the category, string, and numeric type.
In this blog, we will explore the advantages the date type has to offer, including:
- Loading and viewing dates side-by-side with other data types.
- Viewing and displaying dates in easy-to-read formats.
- Easily changing the date format.
- Using familiar date formats for filtering data.
How to Run the Maki Cointegration Test (Video)
Learn everything you need to know to run the Maki cointegration test with your data and interpret the results in this new GAUSS video.