Economic Forecasting Techniques Flashcards
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Read the first 7 Economic Forecasting Techniques flashcards as text
Which forecasting method uses a system of simultaneous equations to model the interdependencies among multiple economic variables?
Answer: Simultaneous Equation Model (SEM)
Simultaneous Equation Models capture bidirectional relationships among variables using a system of equations estimated jointly.
In the context of economic forecasting, what does 'nowcasting' refer to?
Answer: Estimating the current state of the economy using real-time data
Nowcasting uses high-frequency real-time data (e.g., weekly jobless claims) to estimate economic conditions for the current or very recent period.
The Kalman filter is primarily used in economic forecasting to:
Answer: Estimate unobserved state variables from noisy observations
The Kalman filter recursively updates estimates of hidden state variables (e.g., potential output) as new data arrives.
A forecast that minimizes the sum of squared errors is said to be optimal under which loss function?
Answer: Quadratic (squared error) loss
Quadratic loss penalizes large errors heavily and its minimization yields the conditional mean as the optimal point forecast.
When combining forecasts from multiple models, research consistently shows that:
Answer: Simple averages of forecasts often outperform individual models
Forecast combination exploits model diversity and reduces variance, frequently beating any single model including the best ex-post model.
Which technique decomposes a time series into trend, seasonal, and irregular components using a multiplicative or additive framework?
Answer: Census X-13ARIMA-SEATS
X-13ARIMA-SEATS, developed by the US Census Bureau, is the standard seasonal adjustment and decomposition tool used by statistical agencies.
An economic forecaster uses a rolling window of 60 months to re-estimate a model as each new month of data arrives. This approach is called:
Answer: Rolling window estimation
Rolling window estimation keeps the sample size fixed, allowing model parameters to evolve over time and accommodating structural change.