Decision Sciences
8 years ago

New method simplifies forecasting economic trends for better decision-making!

Elsevier eBooks
FORMULATION AND ESTIMATION OF MIXED MOVING-AVERAGE AUTOREGRESSIVE MODELS FOR SINGLE TIME SERIES: EXAMPLES
Marc Nerlove, David M. Grether, José L. Carvalho

Paper Summary

The article discusses how to analyze economic time series data by using mixed autoregressive moving-average models. These models help remove trends in the data to make it easier to understand. The researchers show how to estimate the model's parameters in either the time or frequency domain. They found that estimating parameters in the frequency domain is simpler and leads to better forecasts.

Related papers

Theme background image
Computer Science
2 years ago

Non-autoregressive neural networks outperform autoregressive for faster system identification.

Neural Networks
Anomaly Detection
Image Denoising
1 view
Economics, Econometrics and Finance
4 years ago

New model accurately predicts unemployment trends, revolutionizing economic forecasting.

Macroeconomic Analysis
Regularization Methods
1 view
Theme background image
Computer Science
8 years ago

New method distinguishes complex systems with time-varying parameters effortlessly.

Neural Networks
Blind Source Separation
Process Fault Detection