Recent Posts

Fundamentals of Tuning Machine Learning Hyperparameters

Machine learning algorithms often rely on hyperparameters that can impact the performance of the models. These hyperparameters are external to the data and are part of the modeling choices that practitioners must make. An important step in machine learning modeling is optimizing model hyperparameters to improve prediction accuracy. In today’s blog, we will cover some fundamentals of parameter tuning and will look more specifically at fine-tuning our previous decision forest model.

Managing String Data with GAUSS Dataframes

Working with strings hasn’t always been easy in GAUSS. In the past, the only option in GAUSS was to store strings separately from numeric data. It made it difficult to work with datasets that contained mixed types. With the introduction of GAUSS dataframes in GAUSS 21 and the enhanced string capabilities of GAUSS 23, that has all changed! I would argue that GAUSS now offers one of the best environments for managing and cleaning mixed-type data. I recently used GAUSS to perform the very practical task of creating an email list from a string-heavy dataset – something I never would have chosen GAUSS for in the past. In this blog, we walk through this data cleaning task, highlighting several key features for handling strings.

Applications of Principal Components Analysis in Finance

Principal components analysis (PCA) is a useful tool that can help practitioners streamline data without losing information. In today’s blog, we’ll examine the use of principal components analysis in finance using an empirical example. We’ll look more closely at:
  • What PCA is.
  • How PCA works.
  • How to use the GAUSS Machine Learning library to perform PCA.
  • How to interpret PCA results.

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.

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.

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.

GAUSS 23

The new GAUSS 23 is the most practical GAUSS yet! It’s built with the intention to save you time on everyday research tasks like finding, importing, and modeling data. Learn about new features including:
  • New FRED and DBnomics data integrations.
  • Simplified data loading with intelligent type detection.
  • First-class dataframe storage.
  • Expanded quantile regressions.
  • Kernel density estimation.
  • and more …

Introduction to Efficient Creation of Detailed Plots

A few weeks ago, we showed you how to create a detailed plot from a recent article in the American Economic Review. That article contained several plots that contain quite a bit of similar and stylized formatting. Today we will show you how to efficiently create two of these graphs. Our main goals are to get you thinking about code reuse and how it can help you:
  • Get more results from your limited research time.
  • Avoid the frustration that comes from growing mountains of spaghetti code.

Addressing Conditional Heteroscedasticity in SVAR Models

Structural VAR models are powerful tools in macroeconomic time series modeling. However, given their vast applications, it is important that they are properly implemented to address the characteristics of their underlying data. In today’s blog, we build on our previous discussions of SVAR models to examine the use of SVAR in the special case of conditional heteroscedasticity. We will look more closely at:
  • Conditional heteroscedasticity.
  • The impacts of conditional heteroscedasticity on SVAR models.
  • Estimating structural impulse response functions (SIRF) in the presence of conditional heteroscedasticity.
  • An application to the global oil market.

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