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Author: Eric

Articles by: Eric
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.
Dec 14 by Eric Posted in Graphics, Panel data

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:
  • 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.
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Dec 02 by Eric Posted in Econometrics, Programming, User Interface

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.
Oct 21 by Eric Posted in Econometrics, Time Series

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:
  1. The intuition behind the QARDL model.
  2. How to estimate the QARDL model in GAUSS.
  3. How to interpret the QARDL results.
Sep 29 by Eric Posted in Econometrics, Time Series

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:
  • 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.
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Sep 03 by Eric Posted in Time Series

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:
  • 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.
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Aug 20 by Eric Posted in Econometrics, Programming

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.
Aug 02 by Eric Posted in Econometrics, Time Series

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:
  • 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?
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Jun 29 by Eric Posted in Econometrics, Time Series

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:
  1. What is Granger-causality?
  2. When to use Granger causality?
  3. How to use Granger causality?
Jun 18 by Eric Posted in Econometrics, Programming, Time Series

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:
  1. Loading and viewing dates side-by-side with other data types.
  2. Viewing and displaying dates in easy-to-read formats.
  3. Easily changing the date format.
  4. Using familiar date formats for filtering data.

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