29 Questions
Why is testing for non-stationarity important in regression analysis?
Non-stationary variables can lead to spurious regressions with high R2 values, even if the variables are unrelated.
Which model characterizes non-stationarity as a random walk with drift?
$y_t = \mu + y_{t-1} + u_t$
What happens to the standard assumptions for asymptotic analysis if the variables in a regression model are non-stationary?
The standard assumptions for asymptotic analysis will not be valid.
What is the general form of an explosive process in the given context?
$y_t = \mu + \phi y_{t-1} + u_t$ where $\phi > 1$
What does a value of $\phi > 1$ indicate in the context of non-stationarity?
Shocks to the system are not only persistent through time, but also propagated to have an increasingly large influence.
What is required to induce stationarity in the case of deterministic non-stationarity?
Detrending
What is the characteristic of an I(2) series?
It contains two unit roots and would require differencing twice to induce stationarity.
What is the basic objective of testing for a unit root in time series?
$\phi = 1$
What does an I(0) series indicate?
It is a stationary series.
What does an I(1) series indicate?
It contains one unit root and would require differencing once to induce stationarity.
What does an I(2) series indicate?
It contains two unit roots and would require differencing twice to induce stationarity.
What are the characteristics of I(0), I(1), and I(2) series?
I(2) series contains two unit roots, I(1) series contains one unit root, and I(0) series is stationary.
How do we generalize the concept of a non-stationary series?
By considering the case where the series contains more than one 'unit root', requiring more than one application of the first difference operator, $\Delta$, to induce stationarity.
Non-stationary series can lead to spurious regressions, where a regression of one variable on another could have a high R2 even if the two are totally unrelated.
True
The random walk model with drift is given by the equation $y_t = \mu + y_{t-1} + u_t$ where $u_t$ is an iid process.
True
If the variables in a regression model are not stationary, the standard assumptions for asymptotic analysis will not be valid, and the usual t-ratios will not follow a t-distribution.
True
There are two models frequently used to characterize non-stationarity: the random walk model with drift and the deterministic trend process.
True
An I(2) series contains two unit roots and so would require differencing twice to induce stationarity.
True
Detrending a stochastic non-stationary series involves differencing the series to induce stationarity.
True
The model $y_t = \mu + \phi y_{t-1} + u_t$ characterizes the non-stationarity as an explosive process when $\phi > 1$.
True
A series that is integrated of order $d$ is said to be $I(d)$.
True
If a non-stationary series $y_t$ must be differenced $d$ times before it becomes stationary, then it is said to be integrated of order $d$.
True
The basic objective of testing for a unit root in time series is to test the null hypothesis that $\phi = 1$ in the model $y_t = \phi y_{t-1} + u_t$.
True
An I(0) series is a non-stationary series that contains no unit roots and is already stationary.
False
If $\phi > 1$, shocks to the system are not only persistent through time, they are also propagated, leading to an increasingly large influence.
True
Consumer prices have been argued to have 2 unit roots, indicating a high degree of non-stationarity.
True
The majority of economic and financial series contain a single unit root, making them $I(1)$ series.
False
The second case, $yt = \mu + yt-1 + u_t$, is known as deterministic non-stationarity and requires detrending to induce stationarity.
False
The first difference operator, $\Delta$, is used to induce stationarity in a non-stationary series.
True
Learn about the importance of testing for non-stationarity in finance, and how it can influence the behavior and properties of time series data. Understand the impact of spurious regressions and the need to determine stationarity and unit root testing.
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