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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.
Feb 21 by Eric Posted in Econometrics, Machine learning

Predicting Recessions with Machine Learning Techniques
Forecasts have become a valuable commodity in today’s data-driven world. Unfortunately, not all forecasting models are of equal caliber, and incorrect predictions can lead to costly decisions. Today we will compare the performance of several prediction models used to predict recessions. In particular, we’ll look at how a traditional baseline econometric model compares to machine learning models. Our models will include:
  • A baseline probit model.
  • K-nearest neighbors.
  • Decision forests.
  • Ridge classification.
Jan 17 by Eric Posted in Econometrics

The Fundamentals of Kernel Density Estimation
Today’s blog looks closely at the fundamentals of kernel density estimation. After reading this blog you should have an understanding of:
  • What kernel density estimation is.
  • How kernel density estimation works.
  • How to perform kernel density estimation in GAUSS.
Dec 15 by Eric Posted in Best Practices, Programming, Time Series

Importing FRED Data to GAUSS
The GAUSS FRED database integration, introduced in GAUSS 23, is a time-saving feature that allows you to import FRED data directly into GAUSS. This means you have thousands of datasets at your fingertips without ever leaving GAUSS. These tools also ensure that FRED data is imported directly into a GAUSS dataframe format, which can eliminate hours of data cleaning and the headaches that come with it. In today’s blog, we will learn how to use the FRED import tools to:
  • Search for a FRED data series.
  • Import FRED data to GAUSS, including merging multiple series.
  • Use advanced import tools to perform data transformations.
Sep 05 by Eric Posted in Econometrics, Time Series

Unobserved Components Models; The Local Level Model
In today’s blog, we explore a simple but powerful member of the unobserved components family – the local level model. This model provides a straightforward method for understanding the dynamics of time series data. This blog will examine:
  • Time series decomposition.
  • Unobserved components and the local level model.
  • Understanding the estimated results for a local level model.
Aug 03 by Eric Posted in Econometrics, Time Series

Understanding State-Space Models (An Inflation Example)
State-space models provide a powerful environment for modeling dynamic systems. Their flexibility has resulted in a wide variety of applications across fields including radar tracking, 3-D modeling, monetary policy modeling, weather forecasting, and more. In this blog, we look more closely at state-space modeling using a simple time series model of inflation. We cover:
  • The components of state-space models.
  • Representing state-space models in GAUSS.
  • Estimating model parameters using state-space models.
May 16 by Eric Posted in Econometrics, Time Series

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.
Apr 18 by Eric Posted in Econometrics, Programming

How to Load Excel Data into GAUSS
Loading data is often the first step to your data analysis in GAUSS. In this video, you’ll learn how to save time and avoid data loading errors when working with Excel files. Our video demonstration shows just how quick and easy it can be to load time series, categorical and numeric variables from Excel files into GAUSS. You’ll learn how to:
  • Interactively load Excel data files.
  • Perform advanced loading steps, Such as loading specific sheets, or specifying values as missing values.
  • Use autogenerated code in a program file.
  • Change variable names
  • Set up categoical labels and and base cases.

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