Question 1

A long-term movement in the data (a systematic tendency for a series to either increase or decrease) is called a?&#10;A) Trend&#10;B) Cycle&#10;C) Seasonality&#10;D) Window

Accepted Answer

A trend is a long-term movement in the data, a systematic tendency for a series to either increase or decrease.

Question 2

What is the definition of autocorrelation?&#10;A) The data are hierarchically structured&#10;B) There is absence of multicollinearity&#10;C) The variance of the residuals is unequal over a range of measured values&#10;D) The correlation of a series with its own lagged or future values

Accepted Answer

Autocorrelation refers to the correlation of a time series with its own past and future values, indicating how the values of the series are related to each other over time.

Question 3

Which statistical test is commonly employed to detect autocorrelation?&#10;A) Cook's D&#10;B) Durbin-Watson&#10;C) Hausman&#10;D) Huber-White

Accepted Answer

The Durbin-Watson test is commonly employed to detect autocorrelation. It is a statistical test that measures the correlation between the residuals of a regression model and the residuals of the previous period. A low Durbin-Watson statistic indicates that there is positive autocorrelation, while a high Durbin-Watson statistic indicates that there is negative autocorrelation.

Question 4

What is a stationary time series?&#10;A) A series where the mean, variance and covariance does not change over time&#10;B) A series where the mean, variance and covariance does change over time&#10;C) A series where the probability distribution does not change over time&#10;D) A series where the probability distribution does change over time

Accepted Answer

A stationary time series is one where the statistical properties such as mean, variance, and covariance remain constant over time.

Question 5

Which model can be used to include multiple time series?&#10;A) Arima model&#10;B) Vector autoregression&#10;C) Unit root&#10;D) Correlogram

Accepted Answer

Vector autoregression (VAR) is a statistical model that captures the linear interdependencies among multiple time series.

Deck 12: Time-Series Analysis