Category: Time Series

In structural vector autoregressive (SVAR) modeling, one of the core challenges is identifying the structural shocks that drive the system’s dynamics.
Traditional identification approaches often rely on short-run or long-run restrictions, which require strong theoretical assumptions about contemporaneous relationships or long-term behavior.
Sign restriction identification provides greater flexibility by allowing economists to specify only the direction, positive, negative, or neutral, of variable responses to shocks, based on theory.
In this blog, we’ll show you how to implement sign restriction identification using the new GAUSS procedure, **svarFit**, introduced in TSMT 4.0.

Structural Vector Autoregressive (SVAR) models provide a structured approach to modeling dynamics and understanding the relationships between multiple time series variables. Their ability to capture complex interactions among multiple endogenous variables makes SVAR models fundamental tools in economics and finance. However, traditional software for estimating SVAR models has often been complicated, making analysis difficult to perform and interpret. In today’s blog, we present a step-by-step guide to using the new GAUSS procedure, svarFit, introduced in TSMT 4.0. We will cover: Estimating reduced form models. Structural identification using short-run restrictions. Structural identification using long-run restrictions. Structural identification using sign restrictions.

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.

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.

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.

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.