explain why when you choose your answer Consider a non-stationary time series th
ID: 1209643 • Letter: E
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explain why when you choose your answer
Consider a non-stationary time series that follows a random walk drift Yt=Betao+Beta_1Y_t-1+ut Then, The first difference of the time series will result in a stationary time series, The deterministic time deterministic time detrending methods will result in a stationary time series. Both the first difference and deterministic time detrending methods will result in a stationary time series, d- 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 cointegrated 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 a- 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 Y_t and Y_t-s is___The direct autocorrelation between Y_t and Y_t-s The autocorrelation between Y_t and Y_t-s+1 Always zero in an AR (1) model and when s > 1. Very similar to the ACF.Explanation / Answer
1.(a) construct yt = yt yt1. First differencing the trend stationary series will yield a stationary series, but it won’t correspond exactly to ut . Hence, if you think the series is a random walk with drift, difference it. If you think the series is trend stationary, remove a deterministic time trend
2.(c)If two or more series are individually integrated (in the time series sense) but some linear combination of them has a lower order of integration, then the series are said to be cointegrated. A common example is where the individual series are first-order integrated but some (cointegrating) vector of coefficients exists to form a stationary linear combination of them.
3.(b).
4.(b).
5.(a).The sample autocorrelation function (ACF) for a series gives correlations between the series xtand lagged values of the series for lags of 1, 2, 3, and so on. The lagged values can be written as xt-1, xt-2, xt-3,and so on. The ACF gives correlations between xtand xt-1, xt and xt-2, and so on.
The ACF can be used to identify the possible structure of time series data. That can be tricky going as there often isn’t a single clear-cut interpretation of a sample autocorrelation function.
6.(a).Correlation between two variables can result from a mutual linear dependence on other variables (confounding). Partial autocorrelation is the autocorrelation between ytand yt–h after removing any linear dependence on y1, y2, ..., yt–h+1. The partial lag-h autocorrelation is denoted h,h.
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