Podcast
Questions and Answers
Which type of time series model attempts to model and predict financial variables using only information contained in their own past values and possibly current and past values of an error term?
Which type of time series model attempts to model and predict financial variables using only information contained in their own past values and possibly current and past values of an error term?
- Multivariate time series models
- AutoRegressive Moving Average (ARMA) models
- Univariate time series models (correct)
- Structural models
What is the defining characteristic of a weakly stationary process?
What is the defining characteristic of a weakly stationary process?
- $E(y_t) = E(y_{t+m}), \text{ for some } m$
- $E(y_t) = \mu, \text{ for some } t$
- All variances are the same and all covariances depend on the difference between $t_1$ and $t_2$ (correct)
- $E(y_t) = \mu, \text{ for } t = 1,2,\ldots,\infty$
Which class of time series models is usually associated with Box and Jenkins (1970)?
Which class of time series models is usually associated with Box and Jenkins (1970)?
- Univariate time series models
- AutoRegressive Moving Average (ARMA) models (correct)
- Multivariate time series models
- Structural models
What is the defining characteristic of a strictly stationary process?
What is the defining characteristic of a strictly stationary process?
When might time series models be useful?
When might time series models be useful?
Flashcards
Univariate Time Series Models
Univariate Time Series Models
Time series models that predict financial variables using only their own past values and possibly current and past values of an error term.
Weakly Stationary Process
Weakly Stationary Process
A process where all variances are constant and covariances depend solely on the time difference between observations.
ARMA Models
ARMA Models
A class of models often associated with Box and Jenkins (1970), characterized by combining autoregressive (AR) and moving average (MA) components.
Strictly Stationary Process
Strictly Stationary Process
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Time Series Model: When Structural Models Fail
Time Series Model: When Structural Models Fail
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