伍德里奇計(jì)量經(jīng)濟(jì)學(xué)講義1.ppt

1、1,Time Series Data,yt = b0 + b1xt1 + . . .+ bkxtk + ut 1. Basic Analysis,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

2、, we have one realization of a stochastic (i.e. random) process,3,Examples of Time Series Models,A static model relates contemporaneous variables: yt = b0 + b1zt + ut A finite distributed lag (FDL) model allows one or more variables to affect y with a lag: yt = a0 + d0zt + d1zt-1 + d2zt-2 + ut More

3、generally, a finite distributed lag model of order q will include q lags of z,4,Finite Distributed Lag Models,We can call d0 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 d0 + d1 + dq the long-r

4、un propensity (LRP) it reflects the long-run change in y after a permanent change,5,Assumptions for Unbiasedness,Still assume a model that is linear in parameters: yt = b0 + b1xt1 + . . .+ bkxtk + ut Still need to make a zero conditional mean assumption: E(ut|X) = 0, t = 1, 2, , n Note that this imp

5、lies the error term in any given period is uncorrelated with the explanatory variables in all time periods,6,Assumptions (continued),This zero conditional mean assumption implies the xs are strictly exogenous An alternative assumption, more parallel to the cross-sectional case, is E(ut|xt) = 0 This

6、assumption would imply the xs are contemporaneously exogenous Contemporaneous exogeneity will only be sufficient in large samples,7,Assumptions (continued),Still need to assume that no x is constant, and that there is no perfect collinearity Note we have skipped the assumption of a random sample The

7、 key impact of the random sample assumption is that each ui is independent Our strict exogeneity assumption takes care of it in this case,8,Unbiasedness of OLS,Based on these 3 assumptions, when using time-series data, the OLS estimators are unbiased Thus, just as was the case with cross-section dat

8、a, under the appropriate conditions OLS is unbiased Omitted variable bias can be analyzed in the same manner as in the cross-section case,9,Variances of OLS Estimators,Just as in the cross-section case, we need to add an assumption of homoskedasticity in order to be able to derive variances Now we a

9、ssume Var(ut|X) = Var(ut) = s2 Thus, the error variance is independent of all the xs, and it is constant over time We also need the assumption of no serial correlation: Corr(ut,us| X)=0 for t s,10,OLS Variances (continued),Under these 5 assumptions, the OLS variances in the time-series case are the

10、same as in the cross-section case. Also, The estimator of s2 is the same OLS remains BLUE With the additional assumption of normal errors, inference is the same,11,Trending Time Series,Economic time series often have a trend Just because 2 series are trending together, we cant assume that the relati

11、on is causal Often, both will be trending because of other unobserved factors Even if those factors are unobserved, we can control for them by directly controlling for the trend,12,Trends (continued),One possibility is a linear trend, which can be modeled as yt = a0 + a1t + et, t = 1, 2, Another pos

12、sibility is an exponential trend, which can be modeled as log(yt) = a0 + a1t + et, t = 1, 2, Another possibility is a quadratic trend, which can be modeled as yt = a0 + a1t + a2t2 + et, t = 1, 2, ,13,Detrending,Adding a linear trend term to a regression is the same thing as using “detrended” series

13、in a regression Detrending a series involves regressing each variable in the model on t The residuals form the detrended series Basically, the trend has been partialled out,14,Detrending (continued),An advantage to actually detrending the data (vs. adding a trend) involves the calculation of goodness of fit Time-series regressions tend to have very high R2, as the trend is well explained The R2 from a regression on detrended data better reflects how well the xts explain yt,15,Seasonality,Often time-series data exhibits some periodicity, referred to seasonality Example: Qu