Recent Posts

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.

Time Series MT 3.1.1 Update

Introduction The GAUSS TSMT application module provides a comprehensive suite of tools for MLE and state-space estimation, model diagnostics, and forecasting of univariate, multivariate, and nonlinear time series models. The latest Time Series MT (TSMT) 3.1. is now available. If you own TSMT 3.0 the update is available for free. Installation The TSMT update requires [...]

Installing the GAUSS Package Manager [Video]

GAUSS packages provide access to powerful tools for performing data analysis. Learn how to install the GAUSS Package Manager, and get the quickest access to the full suite of GAUSS packages, in this short video. Additional Resources GAUSS Package Manager Using GAUSS Packages a Complete Guide

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.

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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