To predict future economic conditions.

To control economic outcomes.

To analyze historical economic data.

To develop economic policies.

Time series models.

Econometric models.

Input-output models.

Computable general equilibrium models.

Time series models use historical data to predict future values, while econometric models use economic theory to predict future values.

Time series models are more accurate than econometric models.

Time series models are easier to build than econometric models.

Time series models are used for short-term forecasting, while econometric models are used for long-term forecasting.

Mean absolute error.

Root mean squared error.

Mean absolute percentage error.

Theil's U statistic.

Deterministic models assume that all economic variables are known with certainty, while stochastic models allow for uncertainty.

Deterministic models are more accurate than stochastic models.

Deterministic models are easier to build than stochastic models.

Deterministic models are used for short-term forecasting, while stochastic models are used for long-term forecasting.

Structural models specify the relationships between economic variables, while reduced-form models do not.

Structural models are more accurate than reduced-form models.

Structural models are easier to build than reduced-form models.

Structural models are used for short-term forecasting, while reduced-form models are used for long-term forecasting.

Dynamic models allow for the effects of past values of economic variables to influence current values, while static models do not.

Dynamic models are more accurate than static models.

Dynamic models are easier to build than static models.

Dynamic models are used for short-term forecasting, while static models are used for long-term forecasting.

Linear models assume that the relationship between economic variables is linear, while nonlinear models allow for nonlinear relationships.

Linear models are more accurate than nonlinear models.

Linear models are easier to build than nonlinear models.

Linear models are used for short-term forecasting, while nonlinear models are used for long-term forecasting.

Discrete models assume that economic variables can take on only a finite number of values, while continuous models allow for economic variables to take on any value within a range.

Discrete models are more accurate than continuous models.

Discrete models are easier to build than continuous models.

Discrete models are used for short-term forecasting, while continuous models are used for long-term forecasting.

Deterministic models assume that all economic variables are known with certainty, while stochastic models allow for uncertainty.

Deterministic models are more accurate than stochastic models.

Deterministic models are easier to build than stochastic models.

Deterministic models are used for short-term forecasting, while stochastic models are used for long-term forecasting.

Structural models specify the relationships between economic variables, while reduced-form models do not.

Structural models are more accurate than reduced-form models.

Structural models are easier to build than reduced-form models.

Structural models are used for short-term forecasting, while reduced-form models are used for long-term forecasting.

Dynamic models allow for the effects of past values of economic variables to influence current values, while static models do not.

Dynamic models are more accurate than static models.

Dynamic models are easier to build than static models.

Dynamic models are used for short-term forecasting, while static models are used for long-term forecasting.

Linear models assume that the relationship between economic variables is linear, while nonlinear models allow for nonlinear relationships.

Linear models are more accurate than nonlinear models.

Linear models are easier to build than nonlinear models.

Linear models are used for short-term forecasting, while nonlinear models are used for long-term forecasting.

Discrete models assume that economic variables can take on only a finite number of values, while continuous models allow for economic variables to take on any value within a range.

Discrete models are more accurate than continuous models.

Discrete models are easier to build than continuous models.

Discrete models are used for short-term forecasting, while continuous models are used for long-term forecasting.