Applied Econometric Times SeriesAmstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Applied Econometric Times Series was among those chosen. Unique in that it covers modern time series analysis from the sole prerequisite of an introductory course in multiple regression analysis. Describes the theory of difference equations, demonstrating that they are the foundation of all time-series models with emphasis on the Box-Jenkins methodology. Considers many recent developments in time series analysis including unit root tests, ARCH models, cointegration/error-correction models, vector autoregressions and more. There are numerous examples to illustrate various techniques, many of which concern econometric models of transnational terrorism. The accompanying disk provides data for students to work with. |
Contents
Difference Equations 1 TimeSeries Models | 1 |
Difference Equations and Their Solutions | 7 |
Solution by Iteration | 10 |
Copyright | |
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Common terms and phrases
a₁ a₂ ACF and PACF ahead forecast autocorrelations autoregressive B₁ b₂ Box-Jenkins characteristic roots Choleski decomposition coefficients cointegrating vector conditional variance constant converge correlation correlogram critical values cross-correlations data-generating process decomposition denoted deterministic trend Dickey-Fuller tests difference equation e₁ economic effect equal to zero error-correction estimated value example F-test Figure forecast error forecast error variance Granger graph Hence impulse response innovations intercept term irregular component lag length lag operators linear combination long-run equilibrium matrix nonstationary null hypothesis obtain P₁ parameters partial autocorrelations particular solution period Phillips-Perron Phillips-Perron test polynomial procedure random walk real exchange rate reject the null relationship restrictions sample sequence shocks significance level simulated skyjackings standard error stationary process statistic stochastic trend t-statistic time-series tion tourism unit root process unity variables walk plus drift walk plus noise white-noise white-noise process x₁ y₁ yields z₁