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.
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Contents
ARMA Models for Stationary Time Series | 31 |
General AR and MA Processes | 37 |
AutoregressiveMoving Average Models | 43 |
Endnotes | 55 |
Unit Roots Difference and Trend Stationarity | 71 |
Breaking and Nonlinear Trends | 103 |
An Introduction to Forecasting With Univariate | 121 |
Unobserved Component Models Signal Extraction | 131 |
13 | 211 |
23 | 225 |
Error Correction Spurious Regressions | 233 |
Vector Autoregressions With Integrated Variables | 255 |
Identification of Vector Error Correction Models | 264 |
Vector Error Correction ModelX Models | 271 |
Endnotes | 279 |
State Space Models | 299 |
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Common terms and phrases
Àd exchange rate alternative ARDL model ARIMA ARMA model assumed asymptotically autoregressive Autoregressive Conditional Heteroskedastic beer sales behavior bilinear break date breaking trend Chapter coefficients cointegrating component conditional variance correlation critical values decomposition defined deterministic ENDNOTES equation error correction error variance estimated example exponential smoothing filter fitted forecast error frequency GARCH given global temperatures Granger HoltÀWinters I0ðÞ I1ðÞ implies impulse response innovations intercept interest rate Kalman filter linear trend logarithms long-run matrix moving average nonlinear nonstationary null hypothesis observations obtained p-value Perron polynomial procedure radiative forcing random walk residuals restrictions root tests SACF sample autocorrelations seasonal shocks shown in Fig squared standard error stationary stationary process stochastic test statistic tion trend function unit root unit root tests variables VECM vector Vector Autoregressions white noise zero τμ