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Applied Time Series Econometrics (Themes in Modern by Helmut Lütkepohl, Markus Krätzig PDF

By Helmut Lütkepohl, Markus Krätzig

ISBN-10: 0521547873

ISBN-13: 9780521547871

ISBN-10: 052183919X

ISBN-13: 9780521839198

Time sequence econometrics is a speedily evolving box. fairly, the cointegration revolution has had a considerable influence on utilized research. as a result, no textbook has controlled to hide the complete diversity of tools in present use and clarify how one can continue in utilized domain names. This hole within the literature motivates the current quantity. The equipment are sketched out, reminding the reader of the tips underlying them and giving enough historical past for empirical paintings. The remedy can be used as a textbook for a path on utilized time sequence econometrics. issues contain: unit root and cointegration research, structural vector autoregressions, conditional heteroskedasticity and nonlinear and nonparametric time sequence types. the most important to empirical paintings is the software program that's on hand for research. New technique is usually purely steadily integrated into present software program applications. as a result a versatile Java interface has been created, permitting readers to copy the purposes and behavior their very own analyses.

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Extra resources for Applied Time Series Econometrics (Themes in Modern Econometrics)

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For example, to avoid numerical problems it may be helpful to pay attention to a roughly similar order of magnitude in the actual time series numbers. For instance, it may not be a good idea to measure the GNP in billions of euros and another variable of similar order of magnitude in cents. The required operations for making the data more homogenous are often easy to perform with the software tool available. More details on data handling with the software JMulTi frequently referred to in this volume are discussed in Chapter 8.

5 Deterministic Terms So far we have considered purely stochastic processes with zero mean. In practice, such processes are rarely sufficient for an adequate representation of reallife time series. S. 1, which may be generated by a stationary process. Its mean is not likely to be zero, however. Consequently, we have to allow at least for a nonzero mean term. For many series, more general deterministic terms may be required. For example, a polynomial trend term or seasonal dummy variables may have to be included.

4 (see D4 log income). The series clearly has important characteristics of a stationary series. 4 will be discussed later in this section. Generally, from a statistical point of view, taking logs may be a useful transformation to stabilize the variance of a time series if the variance of the original series increases with the level of the series. Such a transformation has implications for the distribution of the DGP, which may be important in some situations. Univariate Time Series Analysis 19 It is not uncommon, however, for the log transformation also to result in a series more in line with a normally distributed (Gaussian) DGP.

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Applied Time Series Econometrics (Themes in Modern Econometrics) by Helmut Lütkepohl, Markus Krätzig

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