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Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation

This book explores widely used seasonal adjustment methods and recent developments in real time trend-cycle estimation. It discusses;

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Economic Time Series

series are misspecified in some sense, and the broader implications this has for seasonal adjustment and business cycle estimation Section III;

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Introduction to Time Series Modeling

, the trend model, the seasonal adjustment model, and the time-varying coefficient AR model. With a focus on the description, modeling;

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The Econometric Analysis of Seasonal Time Series

nonstationary seasonal stochastic processes. They also cover the latest contributions to the theory and practice of seasonal adjustment, together with;

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Themes in Modern Econometrics

nonstationary seasonal stochastic processes. They also cover the latest contributions to the theory and practice of seasonal adjustment, together with;

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State-Space Models with Regime Switching

distributions from data. The authors present numerous applications of these approaches in detail: decomposition of time series into trend and cycle, a;

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Introduction to Time Series Modeling with Applications in R

model, the seasonal adjustment model, the time-varying coefficient AR model and nonlinear non-Gaussian state-space models. About the Author;

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Modeling and Forecasting Primary Commodity Prices

structural breaks, unobserved components, chaotic discovery, long memory, heteroskedasticity, wavelet estimation and fractional integration. Relevant;

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Modeling and Forecasting Primary Commodity Prices

structural breaks, unobserved components, chaotic discovery, long memory, heteroskedasticity, wavelet estimation and fractional integration. Relevant;

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Nonlinear Time Series

semiparametric estimation and specification methods and then applies these approaches to a class of nonlinear continuous-time models with real-world data;

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Nonlinear Time Series

semiparametric estimation and specification methods and then applies these approaches to a class of nonlinear continuous-time models with real-world data;

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State Estimation for Robotics

. This book presents common sensor models and practical advice on how to carry out state estimation for rotations and other state variables. It;

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Time Series Analysis and Adjustment

In Time Series Analysis and Adjustment the authors explain how the last four decades have brought dramatic changes in the way researchers;

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Time Series Analysis and Adjustment

In Time Series Analysis and Adjustment the authors explain how the last four decades have brought dramatic changes in the way researchers;

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Estimation of the Time since Death

> Contains real case studies that underscore key estimation concepts Demonstrates the changing role of technology and advances;

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Trends and Changes in Hydroclimatic Variables

state-of-the-art methods, such as non-linear trend estimation (including spline smoothing and local regression) and handling persistence (or;

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Adjustment Models in 3D Geomatics and Computational Geophysics

Adjustment Models in 3D Geomatics and Computational Geophysics: With MATLAB Examples, Volume Four introduces a complete package;

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Econometric Evaluation of Socio-Economic Programs

of the treatment effects. By using both real and simulated data, readers will become familiar with evaluation techniques, such as regression;

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Reduced Order Methods for Modeling and Computational Reduction

in real-time with applications in engineering; data assimilation, geometry registration, and parameter estimation with special attention to real;

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Recent Econometric Techniques for Macroeconomic and Financial Data

, in the estimation of state space models and new classes of volatility models. The book is divided into two parts: The first part applies;

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Life Cycle Reliability Engineering

automotive systems. Focusing on real-world problems and solutions, Life Cycle Reliability Engineering covers the gamut of the techniques used for;

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Understanding Least Squares Estimation and Geomatics Data Analysis

; least squares adjustments of parametric, conditional and general models; confidence region estimation; problems of network design and pre;

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Time Series Analysis by State Space Methods

of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements;

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Modelling Seasonality

already mentioned, it presents and discusses the X-11 method of seasonal adjustment and the introduction includes a description and assessment;

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Estimates of Stochastic Processes with Stationary Increments and Cointegrated Sequences

processing. This book gives a deep understanding of spectral theory and estimation techniques for stochastic processes with stationary increments. It;

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The Soul's Calendar

the seasonal cycle, of either hemisphere, and the significance of this for human beings. A page of commentary on each verse is included, which;

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Kernel Smoothing

natural and the environmental sciences, and others. Nonparametric density estimation, nonparametric and semiparametric regression, trend and;

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