Category: Econometrics

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.

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.

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.

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.

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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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.

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