Stationarity means that the: a. error terms are not correlated. b. time series does not exhibit severe fluctuations. c. probability distribution of the time series variable does not change over time. d. forecasts remain within 1.96 standard deviation outside the sample period.

What does the term "stationarity" refer to in time series analysis?Review LaterA time series that does not change over timeA time series with a constant mean and varianceA time series with a linear trendA time series with no seasonal patterns

Departures from stationarity: a. occur often in cross-sectional data. b. jeopardize forecasts and inference based on time series regression. c. cannot be fixed. d. can be made to have less severe consequences by using log-log specifications.

Trend-stationarity implies that while a time series may have time-varying mean, its variance and covariances remain constant over time. True or False

Why is stationarity important in time series analysis?Review LaterStationarity simplifies the modeling process.Stationarity reduces the computational complexity.Stationarity guarantees accurate forecasting.Stationarity allows for the inclusion of external factors.

Why is stationarity important in time series analysis?Answer choicesSelect only one optionREVISITIt allows us to make accurate predictions about future valuesIt ensures that the mean, variance, and covariance of the data are constant over timeIt simplifies the process of data cleaning and preprocessingIt allows us to use linear regression models to analyze the data