Mills T. Applied Time Series Analysis. A Practical Guide...2019 torrent

Textbook in PDF format

Written for those who need an introduction, Applied Time Series Analysis reviews applications of the popular econometric analysis technique across disciplines. Carefully balancing accessibility with rigor, it spans economics, finance, economic history, climatology, meteorology, and public health. Terence Mills provides a practical, step-by-step approach that emphasizes core theories and results without becoming bogged down by excessive technical details. Including univariate and multivariate techniques, Applied Time Series Analysis provides data sets and program files that support a broad range of multidisciplinary applications, distinguishing this book from others.
Key Features:
Focuses on practical application of time series analysis, using step-by-step techniques and without excessive technical detail.
Supported by copious disciplinary examples, helping readers quickly adapt time series analysis to their area of study.
Covers both univariate and multivariate techniques in one volume.
Provides expert tips on, and helps mitigate common pitfalls of, powerful statistical software including EVIEWS and R.
Written in jargon-free and clear English from a master educator with 30 years+ experience explaining time series to novices
Accompanied by a microsite with disciplinary data sets and files explaining how to build the calculations used in examples.
Time Series and Their Features
Transforming Time Series
ARMA Models for Stationary Time Series
ARIMA Models for Nonstationary Time Series
Unit Roots, Difference and Trend Stationarity, and Fractional Differencing
Breaking and Nonlinear Trends
An Introduction to Forecasting With Univariate Models
Unobserved Component Models, Signal Extraction, and Filters
Seasonality and Exponential Smoothing
Volatility and Generalized Autoregressive Conditional Heteroskedastic Processes
Nonlinear Stochastic Processes
Transfer Functions and Autoregressive Distributed Lag Modeling
Vector Autoregressions and Granger Causality
Error Correction, Spurious Regressions, and Cointegration
Vector Autoregressions With Integrated Variables, Vector Error Correction Models, and Common Trends
Compositional and Count Time Series
State Space Models
Some Concluding Remarks