# ch10 - Time Series Data yt = 0 1xt1 kxtk ut 1 Basic...

• Notes
• 15

This preview shows pages 1–6. Sign up to view the full content.

Economics 20 - Prof. Anderson 1 Time Series Data y t = β 0 + β 1 x t1 + . . .+ β k x tk + u t 1. Basic Analysis

This preview has intentionally blurred sections. Sign up to view the full version.

Economics 20 - Prof. Anderson 2 Time Series vs. Cross Sectional Time series data has a temporal ordering, unlike cross-section data Will need to alter some of our assumptions to take into account that we no longer have a random sample of individuals Instead, we have one realization of a stochastic (i.e. random) process
Economics 20 - Prof. Anderson 3 Examples of Time Series Models A static model relates contemporaneous variables: y t = β 0 + β 1 z t + u t A finite distributed lag (FDL) model allows one or more variables to affect y with a lag: y t = α 0 + δ 0 z t + δ 1 z t-1 + δ 2 z t-2 + u t More generally, a finite distributed lag model of order q will include q lags of z

This preview has intentionally blurred sections. Sign up to view the full version.

Economics 20 - Prof. Anderson 4 Finite Distributed Lag Models We can call δ 0 the impact propensity – it reflects the immediate change in y For a temporary, 1-period change, y returns to its original level in period q +1 We can call δ 0 + δ 1 +…+ δ q the long-run propensity (LRP) – it reflects the long-run change in y after a permanent change
Economics 20 - Prof. Anderson 5 Assumptions for Unbiasedness Still assume a model that is linear in parameters: y t =

This preview has intentionally blurred sections. Sign up to view the full version.

This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### What students are saying

• As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

Kiran Temple University Fox School of Business ‘17, Course Hero Intern

• I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

Dana University of Pennsylvania ‘17, Course Hero Intern

• The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

Jill Tulane University ‘16, Course Hero Intern