Consider a non-stationary time series that follows a random walk drift Yt = beta

Question

Consider a non-stationary time series that follows a random walk drift Yt = beta_o + beta_1 Yt-1 + ut| Then, The first difference of the time series will result in a stationary time series, The deterministic time deterministic time detruding methods will result in a stationary time series. Both the first difference and deterministic time detruding methods will result in a stationary time series. None of the above will result in a stationary time series. Two or more time-series that have a common stochastic trend are said to be Integrated Endogenous counteracted None of the above A problem where stochastic trends can lead two-time series to appear related when they are not called Robust regression Spurious regression Unit squareroot Autocorrelation To estimate an ARCH (Autoregressive Conditional Heteroskedasticity) model The Ordinary Least Squares(OLS) method. The Maximum Likelihood Estimation (MLE) method The generalized method of moments All of the above. The Autocorrelation function (ACF) or correlogram is very important because It serves as useful tools to identifying univariate time-series models. It helps us to identify if an economic time series is has a unit squareroot or not. Both A and B. None of the above The partial autocorrelation function (PACF) between Yt and Yt sis The direct autocorrelation between Y_t and Y_t-s The autocorrelation between Y_t and Y Always zero in an AR (1) model and when s > 1. Very similar to the ACF.

Explanation / Answer

1.Ans-(D) None of the above will result stationary time series.

2.Ans-(C) Co-integrated.

3.Ans-(C) Unit Root.

4.Ans-(a) The Ordinary Least Square Method.

5.Ans-(a) It serves as useful tools to identifying univariate time-series models.

6Ans-(d) Very similar to the ACF.

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