# Ch10 - Time Series Data yt = 0 1xt1 kxtk ut 1 Basic Analysis Economics 20 Prof Anderson 1 Time Series vs Cross Sectional Time series data has a

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

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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 + 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 + 1 z t-1 + 2 z t-2 + u t More generally, a finite distributed lag model of order q will include q lags of z

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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 0 + 1 +…+ 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 = β 0 + 1 x t1 + . . .+ k x tk + u t

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## This note was uploaded on 03/03/2009 for the course ECON 382 taught by Professor Sun during the Spring '08 term at CUNY Queens.

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Ch10 - Time Series Data yt = 0 1xt1 kxtk ut 1 Basic Analysis Economics 20 Prof Anderson 1 Time Series vs Cross Sectional Time series data has a

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