宏观经济学 模型

Baseline forecast from one series: lags, differencing, and residual checks.

ARIMA with seasonal structure for recurring monthly or quarterly patterns.

Forecasts built from error, trend, seasonality, and smoothing states.

Questions where the object is not the next forecast but the split between trend, cycle, and transitory movement in one series.

Estimating latent trend and cycle paths when a direct observable is noisy or mixes several frequency components.

A small macro system where variables forecast each other from their lags.

VAR dynamics with priors that shrink noisy coefficients toward stable behavior.

VAR forecasts plus restrictions that identify structural shocks.

Cointegrated variables with short-run adjustment back to long-run equilibrium.

Testing whether a long-run equilibrium relation is plausible without requiring every variable to have the same integration order.

Shock-transmission questions where recession, inflation, leverage, or slack regimes plausibly change the coefficients.

Evidence questions where the same policy shock may not propagate the same way in the 1980s, 2000s, and post-pandemic data.

Business-cycle questions where observed series appear to alternate between persistent regimes rather than move around one fixed law.

Nonlinear macro relationships where the move between regimes is gradual rather than an abrupt cutoff.

Monthly indicators mapped into a quarterly target before the official release.

Many indicators compressed into latent factors for nowcasting.

Forecast changes attributed to the releases that moved the estimate.

Mixed-frequency regressions without first aggregating every predictor.

Real-time tracking problems where the state is unobserved and the information set updates observation by observation.

Estimated output, inflation, policy, and labor equations tied by identities.

A larger equation system with identities, lags, and policy blocks.

Auxiliary equations for sectors, labor, or prices around a core forecast.

Country or regional panels with fixed effects for persistent differences.

Dynamic feedback across countries, regions, or sectors.

Many predictors with shrinkage that keeps coefficients stable.

Variable selection and shrinkage when the predictor set is wide.

Model forecasts combined and weighted by observed performance.

Wide macro predictor sets where correlated groups matter and pure LASSO would choose one variable too arbitrarily.

Forecasting with many predictors when interactions and nonlinear thresholds matter more than a single coefficient table.

High-dimensional macro forecasting when the gain comes from nonlinear interactions but stopping discipline matters.

Sequence-forecasting problems where lag structure is long, nonlinear, and hard to summarize with fixed AR lags.

Sector accounts showing how demand shocks move through suppliers.

Cross-sector or cross-country links used to trace shock propagation.