Applied Time Series Econometrics
Helmut Lütkepohl, Markus Krätzig
Cambridge University Press, Aug 4, 2004 - Business & Economics - 323 pages
Time series econometrics is a rapidly evolving field. In particular, the cointegration revolution has had a substantial impact on applied analysis. As a consequence of the fast pace of development there are no textbooks that cover the full range of methods in current use and explain how to proceed in applied domains. This gap in the literature motivates the present volume. The methods are sketched out briefly to remind the reader of the ideas underlying them and to give sufficient background for empirical work. The treatment can also be used as a textbook for a course on applied time series econometrics. The coverage of topics follows recent methodological developments. Unit root and cointegration analysis play a central part. Other topics include structural vector autoregressions, conditional heteroskedasticity and nonlinear and nonparametric time series models. A crucial component in empirical work is the software that is available for analysis. New methodologyis typically only gradually incorporated into the existing softwarepackages. Therefore a felxible Java interface has been created that allows readers to replicate the applications and conduct their own analyses.
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1 Initial Tasks and Overview
2 Univariate Time Series Analysis
3 Vector Autoregressive and Vector Error Correction Models
4 Structural Vector Autoregressive Modeling and Impulse Responses
5 Conditional Heteroskedasticity
6 Smooth Transition Regression Modeling
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ˆut analysis assumptions asymptotic distribution autoregressive model bandwidth bootstrap Chapter coefficient cointegrating rank cointegration relation computed conditional mean function conditional volatility confidence intervals considered constant corresponding covariance matrix Critical values denotes deterministic terms discussed dummy variables dynamic econometric equation example Figure forecast error given Hence homoskedastic impulse responses interest rate JMulTi Johansen L¨utkepohl lag order lag selection lag vector lagged differences linear estimator linear model linear trend long-run ML estimator multivariate nonlinear autoregressive nonparametric normal distribution null hypothesis observations obtained p-value parameters partial autocorrelations plotted possible procedure recursive regression rejected residual autocorrelation restrictions root tests Saikkonen seasonal dummies Section shocks specification spectral density standard stationary stationary process stochastic stochastic process STR model structural subset t-ratios Table Ter¨asvirta test statistic Tschernig unit root unit root tests univariate variance VECM white noise zero