£121.65

Springer Seasonal Adjustment and Trend-Cycle Estimation Book

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Description

Master the complexities of time series analysis with this specialized text from Springer. This book provides a deep look into widely used seasonal adjustment methods and the latest developments in real time trend-cycle estimation. Designed for researchers and professionals in social and behavioral sciences, it offers a detailed examination of the properties and limitations found in X12ARIMA, TRAMO-SEATS, and STAMP. These are the primary methods used by statistical agencies worldwide. To help bridge the gap between theory and practice, the text includes several real-world cases and real data examples. You can follow the application of these methods through practical illustrations. The discussion on trend-cycle estimation covers nonparametric techniques, including moving averages, linear filters, and reproducing kernel Hilbert spaces. By incorporating recent advances, this book serves as a comprehensive system for understanding how to handle seasonal data and estimate trends accurately in modern statistical work.

Key Features

Detailed analysis of X12ARIMA, TRAMO-SEATS, and STAMP methods used by major statistical agencies.

Practical learning through real-world cases and real data examples provided throughout the text.

In-depth exploration of trend-cycle estimation using nonparametric techniques and moving averages.

Coverage of advanced mathematical concepts including linear filters and reproducing kernel Hilbert spaces.

Focus on recent developments and advances in real time trend-cycle estimation for modern research.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
31 May 2018
Listed Since
31 May 2018

Barcode

No barcode data available

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