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Lecture 2 SVARs with Integrated Processes

In this lecture, Adrian Pagan from the University of Sydney explores the concept of Structural Vector Autoregressions (SVARs) with integrated processes. He addresses the significance of trends in time series analysis and discusses what occurs in the absence of a trend. By examining pure random walks and their components, the lecture delves into the implications of applying the Hodrick-Prescott (HP) filter, revealing how it generates a transitory component from what may initially appear trendless. This analysis is crucial for understanding fluctuations in economic data.

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Lecture 2 SVARs with Integrated Processes

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  1. Lecture 2 SVARs with Integrated Processes Adrian Pagan University of Sydney

  2. Where’s the Trend?

  3. Now we see it

  4. So what does it do if there is no Trend? • Useful to look at pure random walk yt =yt-1 + et • yt can be thought of sum of a permanent (ytp) and transitory (ytT) component • For random walk yt= ytp and no transitory component • Apply HP to yt • What does it produce?

  5. It creates a Transitory Component where there wasn’t one

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