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Time series models based on generalized linear models: some further results

¹Department of Statistics, University of Hong Kong.

|June 1, 1994

Summary

This study extends moving average models for time series data using generalized linear models, offering easier construction and estimation for applications in longitudinal data analysis.

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Area of Science:

Statistics
Time Series Analysis
Econometrics

Background:

Classical moving average (MA) models are foundational in time series analysis.
Extending MA models to accommodate diverse conditional distributions is crucial for real-world data.
Generalized linear models (GLMs) offer a flexible framework for modeling various response variable distributions.

Purpose of the Study:

To extend classical moving average models to time series with conditional distributions specified by generalized linear models.
To introduce statistical modeling techniques for these extended models.
To demonstrate the practical utility and performance of the proposed methodology.

Main Methods:

Development of a novel class of moving average models incorporating generalized linear models.
Application of statistical modeling and estimation techniques tailored for the extended framework.
Utilizing simulation studies to assess model performance under various conditions.

Main Results:

The proposed extended moving average models are shown to be easily constructed and estimated.
Simulation results validate the effectiveness of the statistical modeling techniques.
An illustrative example demonstrates the practical application of the methodology.

Conclusions:

The extended moving average models provide a powerful tool for time series analysis with non-normal conditional distributions.
The methodology offers potential applications in analyzing complex longitudinal data.
The ease of construction and estimation makes these models attractive for practical use.

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