1. 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

2. 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

3. 10.1 Random Time Series How is time series data considered to be random? 1)We don’t 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 don’t have a time machine to go back in time and obtain this realization

4. 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: -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:

5. 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: -In this case the variable z has an impact on y now and in as many future time periods as is included in the model -For example, if chocolate consumption affected (un)happiness today AND tomorrow:

6. 10.2 Time Series Regressions -If our model lags two periods in the future, it is an FDL of order two: -to interpret our delta coefficients, assume a one- time, one unit, increase in z today: -and so on in all preceding and proceeding time periods

7. 10.2 Time Series Regressions -Assuming zero error, we have a situation of: -Where this one-time increase affects 3 time periods

8. 10.2 Time Series Regressions -We can then calculate that: -Therefore delta 0 is the immediate change in y due to a one-unit change in z -delta 0 is often called the IMPACT PROPENSITY or IMPACT MULTIPLIER -likewise delta 1 is the change in y one period after z’s change and delta 2 is the change in y two periods after z’s change

9. 10.2 Time Series Regressions -We can also analyze the effect on y due to a PERMANENT one unit increase in z: -Immediately, y increases by delta 0 -After 1 period, y has increased by delta 0 +delta 1 -After 2 periods, y has increased by delta 0 +delta 1 +delta 2 …

10. 10.2 Time Series Regressions -After 3 periods, y has increased by delta 0 +delta 1 +delta 2 +delta 3 -this long-run change in y given a permanent increase in z is called the LONG-RUN PROPENSITY (LRP) or LONG-RUN MULTIPLIER -a finite distributed lag model of order q and the corresponding LRP would be:

11. 10.2 Time Series Regressions -Note that the long-run propensity (LRP) of a time series regression can cause high multicollinearity -Therefore it is often not possible to obtain precise estimates of each delta, but rather we obtain a good estimate of the LRP. -note that different sources use either t=0 or t=1 as the base year -our text considers t=1 the base year

12. 10.3 Finite Sample Properties of OLS under Classical Assumptions -in this section we will see how the 6 Classical Linear model (CLM) assumptions are modified from their time-series form in order to imply to finite (small) sample properties of OLS in time series regressions Note that x tj refers to the t’th time period, where j is labels the x variable X t will refer to all x observations at time t X will refer to a matrix including all x observations over all times t

13. Assumption TS.1 (Linear in Parameters) The stochastic process {(x t1, x t2,…,x tk, y t ): t=1, 2,…,n} follows the linear model Where {u t : t=1,2,…,n} is the sequence of error disturbances. Here, n is the number of observations (time periods). (Note: TS stands for time series)

14. Assumption TS.2 (No Perfect Collinearity) In the sample (and therefore in the underlying time series process), no independent variable is constant nor a perfect linear combination of the others

15. 10.3 Assumption Notes -Our first two assumptions are almost identical to their cross-sectional counterparts -Note that TS.2 allows for correlation between variables, it only disallows PERFECT correlation -the final assumption for time series OLS unbiasedness replaces MLR.4 and obviates the need for a random sampling assumption:

16. Assumption TS.3 (Zero Conditional Mean) For each t, the expected value of the error u t, given the explanatory variables for all time periods, is zero. Mathematically,

17. 10.3 Assumption TS.3 Notes -TS.3 assumes that our error term (unaccounted for variables) is uncorrelated with our included variables IN EVERY TIME PERIOD -this requires us to correctly specify the functional form (static or lag) between y and z -if u t is independent of X and E(u t )=0, TS.3 automatically holds -such a strong assumption was not needed in cross sectional data because each observation was random; in time series each observation is sequential

18. 10.3 Assumption TS.3 Notes -if u t is uncorrelated with all independent variables of time t: -We say that x tj are CONTEMPORANEOUSLY EXOGENOUS -therefore u t and X t are contemporaneously uncorrelated: Corr(x tj,u t )=0 for all j -TS.3 requires more than contemporaneous exogeneity however, it requires STRICT EXOGENEITY across time periods

19. 10.3 Assumption TS.3 Notes -Note that TS.3 puts no restrictions on correlation between independent variables across time -Note that TS.3 puts no restrictions on correlation between error terms across time TS.3 can fail due to: 1)Omitted variables 2)Measurement error 3)Misspecified Model 4)Other

20. 10.3 TS.3 Failure -If a variable z has a LAGGED effect on y, its lag must be included in the model or TS.3 is violated -never use a static model if a lag model is more appropriate -ie: overeating (z) last month (ie: Christmas) causes more exercise in this month (y) -TS.3 also fails if u t affects future z t (since only past z t are controlled for) -ie: cold weather last month (u) will cause depression thus under eating next month (z)

21. Theorem 10.1 (Unbiasedness of OLS) Under assumptions TS.1 through TS.3, the OLS estimators are unbiased conditional on X, and therefore unconditionally as well: Note: The proof is very similar to the cross- sectional case.