Introduction to GAUSS for Stata Users
Our new Introduction to GAUSS for Stata Users offers a guide for Stata Users who are looking to get started quickly in GAUSS. It offers side-by-side comparisons of essential analysis tasks in GAUSS and Stata.
Eric has been working to build, distribute, and strengthen the GAUSS universe since 2012. He is an economist skilled in data analysis and software development. He has earned a B.A. and MSc in economics and engineering and has over 18 years of combined industry and academic experience in data analysis and research.
GAUSS 22.1.0 Maintenance Release Now Available
The latest GAUSS 22.1.0 update is available now and is free if you own GAUSS 22. This maintenance release is one of our most extensive with over 40 enhancements, new functions, and new examples, and bug fixes.
Visualizing COVID-19 Panel Data With GAUSS 22
When they’re done right, graphs are a useful tool for telling compelling data stories and supporting data models. However, too often graphs lack the right components to truly enhance understanding.
In this blog, we look at how a few quick customizations help make graphs more impactful. In particular, we will consider:
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- Using grid lines without cluttering a graph.
- Changing tick labels for readability.
- Using clear axis labels.
- Marking events and outcomes with lines, bars, and annotations.
Getting to Know Your Data With GAUSS 22
There is no getting around the fact that data wrangling, cleaning, and exploring plays an important role in any empirical research. Data management can be time-consuming, error-prone, and can make or break results.
GAUSS 22 is built to take the pain out of dealing with your data and to let you move seamlessly towards tackling your important research questions.
In today’s blog, we walk through how to efficiently prepare and explore real-world data before modeling or estimation. We’ll look at:
- Loading and merging data.
- Cleaning data to eliminate misentries, missing values, and more.
- Exploring data.
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:
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- 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.
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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:
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- 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.
Introduction to Handling Missing Values
Handling missing values is an important step in data cleaning that can impact model validity and reliability.
Despite this, it can be difficult to find examples and resources about how to deal with missing values. This blog helps to fill that void and covers:
- Types of missing values.
- Dealing with missing values.
- Missing values in practice.
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:
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- 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?