10 Further Time Series OLS IssuesChapter 10 covered OLS properties for finite

(small) sample time series data-If our Chapter 10 assumptions fail, we need to

derive large sample time series data OLS properties-for example, if strict exogeneity fails (TS.3)

-Unfortunately large sample analysis is more complicated since observations may be correlated over time-but some cases still exist where OLS is valid

11. Further Issues in Using OLS with Time Series Data

11.1 Stationary and Weakly Dependent Time Series

11.2 Asymptotic Properties of OLS

11.3 Using Highly Persistent Time Series in Regression Analysis

11.4 Dynamically Complete Models and the Absence of Serial Correlation

11.5 The Homoskedasticity Assumption for Time Series Models

11.1 Key Time Series ConceptsTo derive OLS properties in large time series

data sample, we need to understand two properties:

1)Stationary Process-x distributions are constant over time-a weaker form is Covariance Stationary

Process-x variables differ with distance but not

with time2) Weakly Dependent Time Series

-variables lose connection when separated by time

11.1 Stationary Stochastic Process

The stochastic process {xt: t=1,2…} is stationary if for every collection of time indices 1≤t1<t2<…<tm, the joint distribution of (xt1, xt2,…,xtm) is the same as the joint distribution (xt1+h, xt2+h,…,xtm+h) for all integers h≥1

11.1 Stationary Stochastic ProcessThe above definition has two implications:

1) The sequence {xt: t=1,2…} is identically distributed-x1 has the same distribution as any xt

2) Any joint distribution (ie: the joint distribution of [x1,x2]) remains the same over time (ie: same joint distribution of [xt,xt+1])

Basically, any collection of random variables has the same joint distribution now as in the future

11.1 Stationary Stochastic Process

It is hard to prove if a data was generated by a stochastic process

-Although certain sequences are obviously not stationary

-Often a weaker form of stationarity suffices-Some texts even call this weaker form stationarity:

11.1 Covariance Stationary Process

The stochastic process {xt: t=1,2…} with a finite second moment [E(xt2)<∞] is

covariance stationary if:i) E(xt) is constant

ii) Var(xt) is constant

iii)For any t, h≥1, Cov(xt,xt+h) depends on h and not on t

11.1 Covariance Stationary ProcessEssentially, covariance between variables can

only depend on the distance between themLikewise, correlation between two variables can

only depend on the distance between them

-This does however allow for different correlation between variables of different distances

-Note that Stationarity, often called “strict stationarity”, is stronger than and implies covariance stationarity

11.1 Stationarity ImportanceStationarity is important for two reasons:1) It simplifies the law of large numbers and

the central limit theorem-it makes it easier to statisticians to prove

theorems for economists

2) If the relationship between variables (y and x) are allowed to vary each period we cannot accurately estimate their relationship

-Note that we already assume some form of stationarity by assuming that Bj does not differ over time

11.1 Weakly Dependent Time Series

The stochastic process {xt: t=1,2…} is said to be WEAKLY DEPENDENT if xt and xt+h are “almost independent” as h increases without bound

If in the covariance stationary sequence Cor(xt,xt+h)->0 as h->∞, the sequence is ASYMPTOTICALLY UNCORRELATED

11.1 Weakly Dependent Time Series

-weak dependence cannot be formally defined since it varies across applications-essentially, the correlation between xt and xt+h must go to zero “sufficiently quickly” as the

distance between them approaches infinity

-Essentially, weak dependence replaces the random sampling assumption in the law of large numbers and the central limit theorem

11.1 MA(1)-an independent sequence is trivially weakly dependent-a more interesting example is:

(11.1) 1,2,..., t,11 ttt eex -were et is an iid (independent and identically distributed) sequence with mean zero and variance σe

2

-the process {xt} is called a MOVING AVERAGE PROCESS OF ORDER ONE [MA(1)]

-xt is a weighted average of et and et-1

11.1 MA(1)-an MA(1) process is weakly dependent because:

1) Adjacent terms are correlated-ie: both xt and xt+1 depend on et

-note that:

21

11 )1(),(

tt xxCorr

2) Variables more than two periods apart are uncorrelated

11.1 AR(1)-another key example is:

(11.2) 1,2,..., t,11 ttt eyy -were et is an iid (independent and identically distributed) sequence with mean zero and variance σe

2

-we also assume et is independent of y0 and E(y0)=0

-the process {yt} is called an AUTOREGRESSIVE PROCESS OF ORDER ONE [AR(1)]

-xt is a weighted average of et and et-1

11.1 AR(1)-The critical assumption for weak dependence of AR(1) is the stability condition: 1||

-if this condition holds, we have a STABLE AR(1) PROCESS

-we can show that this process is stationary and weakly dependent

11.1 Stationary Notes-Although a trending series is nonstationary, it CAN be weakly dependent

-If a series is stationary ABOUT ITS TIME TREND, and is also weakly dependent, it is often called a TREND-STATIONARY PROCESS

-if time trends are included in these model, OLS can be performed as in chapter 10

11.2 Asymptotic OLS Properties-We’ve already seen cases where our CLM assumptions are not satisfied for time series

-In these cases, stationarity and weak dependence lead to modified assumptions that allow us to use OLS

-in general, these modified assumptions deal with single periods instead of across time

Assumption TS.1’(Linear and Weak

Dependence)Same as TS.1, only add the

assumption that {(Xt,yt) t=1, 2,…} is stationary and weakly dependent. In particular, the law of large numbers and the central limit theorem can be applied to sample averages.

Assumption TS.2’(No Perfect Collinearity)

Same as TS.2

Don’t mess with a good thing!

Assumption TS.3’(Zero Conditional

Mean)For each t, the expected value of the

error ut, given the explanatory variables for ITS OWN time periods, is zero. Mathematically,

(10.10) n.1,2,..., t,0)|( tt XuE

Xt is CONTEMPORANEOUSLY EXOGENOUS

(Note: Due to stationarity, if contemporaneous exogeneity holds for one time period, it

holds for them all.)

Assumption TS.3’ Notes

Note that technically the only requirement for Theorem 11.1 is zero UNconditional mean on ut and zero covariance between u and x:

(11.6) k.1,2,...,j )u,Cov(x ,0)( ttj tuE

However assumption TS.3’ leads to a more straightforward analysis

Theorem 11.1(Consistency of OLS)

Under assumptions TS.1’ through TS.3’, the OLS estimators consistent:

k.1,..., 0,j ,ˆplim jj

11.2 Asymptotic OLS Properties-We’ve effectively:

1) weakened exogeneity to allow for contemporaneous exogeneity2) strengthened the variable assumption to be weakly dependentIn order to conclude consistency of OLS with unbiasedness is impossible.-To allow for tests, we will now impose less strict homoskedasticity and no serial correlation assumptions:

Assumption TS.4’(Homoskedasticity)

The errors are CONTEMPORANEOUSLY HOMOSKEDASTIC, that is,

2)|( tt XuVar

Assumption TS.5’(No Serial Correlation)

For all t≠s,

0),|,( stst XXuuE

11.2 Asymptotic OLS PropertiesNote the modifications in these assumptions:-TS.4’ now only conditions on explanatory variables in time period t-TS.5’ now only conditions on explanatory variables in the involved time periods t and s.

-Note that TS.5’ does hold in AR(1) models-if only one lag exists, errors are seareally uncorrelated

-these assumptions allow us to test OLS without the random distribution assumption:

Theorem 11.2(Asymptotic Normality of

OLS)Under assumptions TS.1’ through

TS.5’, the OLS estimators are asymptotically normally

distributed. Further, the usual OLS standard errors, t statistics and F statistics are asymptotically valid.

11.3 Random Walks-We’ve already seen consistent AR(1) with |ρ|<1, however some series are better explained with ρ=1, producing a RANDOM WALK process:

ttt eyy 1

-where et is iid with mean zero and constant variance σe2, and

the initial value y0 is independent of all et

-essentially, this period’s y is the sum as last period’s y and a zero mean random variable e

11.3 Random Walks-We can also calculate expected value and variance for a random walk:

)()(

)()(...)()()(

...

1

1

ot

otttt

otttt

yEyE

yEeEeEeEyE

yeeey

-therefore the expected value of a random walk does not depend on t, although the variance does:

(11.21) )(

)()(...)()()(2

1

tyVar

yVareVareVareVaryVar

et

otttt

11.3 Random Walks-A random walk is an example of a HIGHLY PERSISTENT or STRONGLY DEPENDENT time series without trending-an example of a highly persistent time series with trending is a RANDOM WALK WITH DRIFT:

(11.23) 10 ttt eyy -Note that if y0=0,

tyVar

tyE

et

t

2

0

)(

)(

11.5 Time Series Homoskedasticity

-Due to lagged terms, time series homoskedasticity from TS. 4’ can differ from cross-sectional homoskedasticity-a simple static model and its homoskedasiticity is of the form:

2

10

)|(

tt

ttt

zuVar

uzy

-The addition of lagged terms can change this homoskedasticity statement:

11.5 Time Series Homoskedasticity-For the AR(1) model:

211

110

)|()|(

tttt

ttt

yyVaryuVar

uyy

-Likewise for a more complicated model:

211,11,

132110

),|(),|(

tttttttt

ttttt

zyzyVarzyzuVar

uzzyy

-Essentially, y’s variance is constant given ALL explanatory variables, lagged or not-This may lead to dynamic homoskedasticity statements