Question

Stationarity implies that:

A. error terms does not correlate with each other.

B. probability distribution of the time series variable does not change over time.

C. time series has a random-walk.

D. forecasts remain within 1.96 standard deviation outside the sample period.

E. probability distribution of the time series variable does not change over time for a given part
of the data (such as the beginning periods) but can be different for the other part (such as the last periods).

2. Problems caused by stochastic trends include all of the following except:

A. the estimator of an AR(1) is biased.

B. the model can no longer be estimated by OLS.

C. t-statistics on regression coefficients can have a non-normal distribution, even in large samples.

D. the presence of spurious regression.

E. none of the above

Answer 1

1.)

option b is corrct.i.e probability distribution of the time series variable does not change over time.

Because stationarity means that the statistical properties of a process generating a time series do not change over time.

2)

option e is correct .ie none of the above

OLS estimation of the coefficients on regressors that have a stochastic trend is problematic because the distribution of the estimator and its t-statistic is non-normal, even asymptotically. It creates problems of Downward bias of autoregressive coefficients , Non-normally distributed t-statistics and Spurious Regression

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