Economic Forecasting Techniques Flashcards

Bayesian Model Averaging (BMA) addresses model uncertainty in forecasting by:

Answer: Weighting forecasts from all candidate models by their posterior model probabilities

BMA integrates over model uncertainty by combining forecasts weighted by each model's posterior probability, accounting for the fact that the true model is unknown.

In long-horizon economic forecasting, which method is generally preferred over ARIMA models?

Answer: Structural economic models anchored by long-run theory

At long horizons, theoretical anchors (e.g., purchasing power parity, potential output) matter more than statistical time-series properties, favoring structural models.

The concept of 'cointegration' is important in economic forecasting because cointegrated variables:

Answer: Share a long-run equilibrium relationship that constrains their joint dynamics

Cointegrated series are individually non-stationary but have a stationary linear combination, which is exploited in Error Correction Models (ECMs) to improve forecasts.

Which of the following best describes 'judgmental adjustment' in economic forecasting?

Answer: Expert modification of model-based forecasts using information not captured by the model

Judgmental adjustment incorporates soft information (e.g., anticipated policy changes, geopolitical events) that statistical models cannot capture, improving real-world forecast accuracy.

In the context of real-time forecasting, 'data vintage' refers to:

Answer: The specific release of a data series available at a given point in time

Different vintages of GDP, for example, reflect successive revisions; using the correct vintage is essential for realistic out-of-sample forecast evaluation.

A forecaster observes that their model systematically underpredicts inflation during supply shocks. This systematic error is best described as:

Answer: Forecast bias

Systematic directional errors indicate forecast bias; an unbiased forecast should have errors that are zero on average with no predictable pattern.

Which approach is most effective for forecasting rare economic events such as financial crises?

Answer: Tail-risk models, extreme value theory, or early warning systems using nonlinear indicators

Rare events lie in the tails of distributions; extreme value theory and early warning system models trained on pre-crisis indicators are better suited than standard linear models.