Recent Posts

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.