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Journal Article

Spectral Analysis and Time Series

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TLDR
In this article, the authors introduce the concept of Stationary Random Processes and Spectral Analysis in the Time Domain and Frequency Domain, and present an analysis of Processes with Mixed Spectra.
Abstract
Preface. Preface to Volume 2. Contents of Volume 2. List of Main Notation. Basic Concepts. Elements of Probability Theory. Stationary Random Processes. Spectral Analysis. Estimation in the Time Domain. Estimation in the Frequency Domain. Spectral Analysis in Practice. Analysis of Processes with Mixed Spectra.

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Citations
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Solution and Estimation Methods for DSGE Models

TL;DR: An overview of solution and estimation techniques for dynamic stochastic general equilibrium models and the foundations of numerical approximation techniques as well as statistical inference are covered.
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Modern signal processing

TL;DR: Some aspects of modern signal processing, which include stochastic modeling, linear prediction, and the interrelationship between them, are presented.
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Wavelets in Time Series Analysis

TL;DR: In this paper, the role of wavelets in statistical timeseries analysis is reviewed, such as estimation of variance, and the scale exponent of processes with a specifiability measure.
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The extremogram: a correlogram for extreme events

TL;DR: Basrak et al. as discussed by the authors define an analog of the autocorrelation function, the extremogram, which only depends on the extreme values in the sequence and study its asymptotic properties under mixing.
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