Recent Posts

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.

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.

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.

Advanced Formatting Techniques for Creating AER Quality Plots

This blog will show you how to reproduce one of the graphs from a paper in the June 2022 issue of the American Economic Review. You will learn how to:
  1. Add and style text boxes with LaTeX.
  2. Set the anchor point of text boxes.
  3. Add and style vertical lines.
  4. Automatically set legend text to use your dataframe’s variable names.
  5. Set the font for all or a subset of the graph text elements.
  6. Set the size of your graph.

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.

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