Econ 399 Chapter10a - 10. Basic Regressions with Times...

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Unformatted text preview: 10. Basic Regressions with Times Series Data 10.1 The Nature of Time Series Data 10.2 Examples of Time Series Regression Models 10.3 Finite Sample Properties of OLS Under Classical Assumptions 10.4 Functional Form, Dummy Variables, and Index Numbers 10.5 Trends and Seasonality 10.1 Nature of Time Series Time series data is any data that follows one observation (location, person, etc) over time-temporal ordering is very important for time series data (higher observations correspond to more recent data)-this is due to the fact that the past can affect the future but not the other way around-recall that for cross-sectional data ordering was of little importance-a sequence of random variables indexed by time is call a STOCHASTIC (random) PROCESS or TIME SERIES PROCESS 10.1 Random Time Series How is time series data considered to be random? 1) We dont know the future. 2) There are a variety of variables that impact the future. 3) Future outcomes are thus random variables.-Each data point is one possible outcome, or realization-If certain conditions were different, the realization could have been different-but we dont have a time machine to go back in time and obtain this realization 10.2 Time Series Regressions-The simplest time series model, closest to cross- sectional models, is a STATIC MODEL relating two variables y and z: (10.1) ..., 2 , 1 , 1 n t u z y t t t = + + = -this equation models a contemporaneous relationship between y and z-here a change in z has an IMMEDIATE effect on y-for example, if eating chocolate each day made one (un)happy: t t t u chocolate U + + = 1 10.2 Time Series Regressions-If one or more variables affect our y variable in time periods after the current period, we have a FINITE DISTRIBUTED LAG (FDL) MODEL: t t t t t u z z z y + + + + + =-- ......
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Econ 399 Chapter10a - 10. Basic Regressions with Times...

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