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.
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:Tagged in
- 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.
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.
GAUSS 22 brings many substantial new features that will save you hours of time and frustration with everyday tasks including:
- Data exploration
- Data cleaning and management
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.
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.
Learn everything you need to know to run the Fourier LM unit root test with your data and interpret the results.Tagged in
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.
Today we will help you to understand and resolveTagged in
Error G0058 Index Out-of-RangeWe will :
- Explain the cause of the index out-of-range error in GAUSS.
- Explain why performing index assignments past the end of your data can lead to bad outcomes.
- Show how to use some functions and operators that can assist with diagnosing and resolving this error.
- Work through an example to resolve an indexing problem.
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.