Guide-linked quiz · Medium Level
Vector Autoregression Quiz
Review VAR lagged equations, parameter growth, Granger predictability and assumptions for multivariate time-series analysis.
Question 1 of 8
In a VAR(p), what predictors appear in each equation?
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- In a VAR(p), what predictors appear in each equation?
- Lagged values of every included endogenous variable
- Only current values of other variables, with no lags
- Only each variable’s own lagged values, excluding cross-series lags
- Only exogenous predictors, with no lagged endogenous terms
- In a VAR(2) with three variables, how many lag coefficients appear in each equation before intercepts?
- 3
- 6
- 2
- 9
- What does a Granger-causality finding support?
- The variables are contemporaneously independent
- An intervention has a proven causal effect
- Lagged values add predictive information conditional on the model
- The model has no omitted variables
- Why assess cointegration before differencing several nonstationary series?
- A standard VAR requires all variables to be independent
- Cointegration forces every VAR coefficient to zero
- Differencing creates a seasonal period
- Differencing may remove a meaningful long-run equilibrium relation
- Why can a high-dimensional VAR overfit a short series?
- Lagged predictors use no parameters
- Each added variable and lag adds many coefficients
- The residuals become deterministic
- The model estimates only one mean
- What does residual autocorrelation after fitting suggest?
- The variables are cointegrated
- All lag terms are causal
- Some temporal dependence may remain unexplained
- The series is guaranteed nonstationary
- What assumptions affect structural impulse-response interpretations?
- A zero-valued mean for all observations
- Only the reported lag order
- The alphabetical order of series names
- Identification restrictions on correlated system shocks
- Which model may be considered for cointegrated nonstationary variables?
- A vector error-correction model
- A standard stationary VAR without checking
- A k-means clustering model
- A univariate moving average of residuals only
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