Category: Time Series

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.

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 [...]

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.

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 Markov-Switching Models

Markov-switching models offer a powerful tool for capturing the real-world behavior of time series data. Today’s blog provides an introduction to Markov-switching models including:
  • What a regime switching model is and how it differs from a structural break model.
  • When we should use the regime switching model.
  • What a Markov-switching model is.
  • What tools we use to estimate Markov-switching models.
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Understanding and Solving the Structural Vector Autoregressive Identification Problem

The structural vector autoregressive model is a crucial time series model used to understand and predict economic impacts and outcomes. In this blog, we look closely at the identification problem posed by structural vector autoregressive models and its solution. In particular, we cover:
  • What is the structural VAR model and what is the reduced form VAR?
  • What is the relationship between structural VAR and reduced form VAR models?
  • What is the structural VAR identification problem?
  • What are common solutions to the structural VAR identification problem?
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