| dc.description.abstract | Over the past decades, time series analysis has experienced a proliferous increase of
applications in economics, especially in macroeconomics and finance. Today these
tools have become indispensable to any empirically working economist.Whereas in
the beginning the transfer of knowledge essentially flowed from the natural sciences,
especially statistics and engineering, to economics, over the years theoretical and
applied techniques specifically designed for the nature of economic time series
and models have been developed. Thereby, the estimation and identification of
structural vector autoregressive models, the analysis of integrated and cointegrated
time series, and models of volatility have been extremely fruitful and far-reaching
areas of research. With the award of the Nobel Prizes to Clive W. J. Granger and
Robert F. Engle III in 2003 and to Thomas J. Sargent and Christopher A. Sims in
2011, the field has reached a certain degree of maturity. Thus, the idea suggests
itself to assemble the vast amount of material scattered over many papers into a
comprehensive textbook.
The book is self-contained and addresses economics students who have already
some prerequisite knowledge in econometrics. It is thus suited for advanced
bachelor, master’s, or beginning PhD students but also for applied researchers. The
book tries to bring them in a position to be able to follow the rapidly growing
research literature and to implement these techniques on their own. Although the
book is trying to be rigorous in terms of concepts, definitions, and statements
of theorems, not all proofs are carried out. This is especially true for the more
technically and lengthy proofs for which the reader is referred to the pertinent
literature. | en_US |
