Unit Roots for Time-Series Analysis

Unit Roots for Time-Series Analysis

The Unit Root Problem

An AR(1) series is said to be covariance stationary if the absolute value of the lag coefficient \(\text{b}_{1}\) is less than 1. If the absolute value of \(\text{b}_{1}=1\), the time series is said to have a unit root. All random walks have a unit root since they have \(\text{b}_{1}=1\). This implies that they are not covariance stationary; hence we cannot apply the standard linear regression to test for \(\text{b}_{1}=1\).

A Dickey-Fuller test can be used to establish if the time series has a unit root. A time series with unit roots should be transformed by first-differencing it to a covariance stationary time series, which can be effectively analyzed using regression analysis.

First-differencing is a technique that involves subtracting the dependent variable in the immediately preceding period from the current value of the time series to define a new dependent variable, \(y\). Thus, we model the change in the value of the dependent variable.


The first-differenced time series can then be modeled as an autoregressive time series. A properly differenced random walk time series is covariance stationary with a mean reversion level of 0.


Which of the following is the most appropriate approach to transforming a time series with a unit root problem?

  1. Performing a Dickey-Fuller test.
  2. Modeling the first differences of the time series.
  3. Performing a log-linear transformation.


The correct answer is B.

A time series with a unit root can be first differenced to transform it into one that is covariance stationary. This is followed by estimating an autoregressive model for the first-differenced series.

First differencing involves subtracting the value of the time series in the immediately preceding period from the current value of the series to define a new dependent variable, \(y\).

A is incorrect. The Dickey fuller test is used to determine if the time series has a unit root.

C is incorrect. A log-linear transformation is appropriate when data grows at a constant rate.

Reading 5: Time Series Analysis

LOS 5 (j) Describe implications of unit roots for time-series analysis, explain when unit-roots are likely to occur and how to test for them, and demonstrate how a time series with a unit root can be transformed so it can be analyzed with an AR model.

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