Applied Time Series Analysis: A Practical Guide to Modeling and ForecastingWritten 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. |
Contents
Time Series and Their Features | 1 |
Transforming Time Series | 13 |
ARMA Models for Stationary Time Series | 31 |
ARIMA Models for Nonstationary Time Series | 57 |
and Fractional Differencing | 71 |
Trend Versus Difference Stationarity | 77 |
Other Approaches to Testing for a Unit Root | 83 |
Fractional Differencing and Long Memory | 90 |
Conditional Heteroskedastic Processes | 161 |
Forecasting From an ARMAGARCH Model | 168 |
Transfer Functions and Autoregressive Distributed | 201 |
Vector Autoregressions and Granger Causality | 211 |
Error Correction Spurious Regressions | 233 |
Vector Autoregressions With Integrated Variables | 255 |
Identification of Vector Error Correction Models | 264 |
Vector Error Correction ModelX Models | 271 |
Estimating the Fractional Differencing Parameter | 96 |
Breaking and Nonlinear Trends | 103 |
An Introduction to Forecasting With Univariate | 121 |
Unobserved Component Models Signal Extraction | 131 |
Seasonality and Exponential Smoothing | 145 |
Other editions - View all
Common terms and phrases
a₁ alternative ARDL ARIMA ARMA models asymptotically autoregressive beer sales behavior Box-Cox transformed break date breaking trend Chapter coefficients cointegrating component conditional constant correlation critical values decomposition defined deterministic distribution ENDNOTES equation error correction example exchange rate exponential exponential smoothing FIGURE filter fitted Fractional Differencing frequency given global temperatures Granger implies impulse responses innovations intercept interest rates Kalman filter linear trend logarithms long-run matrix mean monthly moving average nonstationary null hypothesis observations obtained p-value p₁ parameters partial autocorrelation Perron random walk residuals restrictions root tests SACF and SPACF sample autocorrelation seasonal Series Analysis shown in Fig smooth standard error stationarity stationary stationary process stochastic stochastic process test statistic transformation trend function unit root unit root tests variables variance VECM vector Vx₁ white noise x₁ y₁ zero β₁ θο μ₁ σ² τμ