Macroeconomics Models
Browse compact macro models for mechanism-first explanation, then move through a single overview, explore, and compare shell. Open proofs only when you need derivation detail.
Browse by model family
Empirical Forecasting Models
Use forecasting models when the observed path, data vintage, error behavior, and model comparison matter more than a single theoretical channel.
Reading lens
Frame
Univariate time-series
One macro series, its own history, and the forecast errors that remain.
ARIMA
Baseline forecast from one series: lags, differencing, and residual checks.
SARIMA
ARIMA with seasonal structure for recurring monthly or quarterly patterns.
ETS (exponential smoothing)
Forecasts built from error, trend, seasonality, and smoothing states.
HP filter and Beveridge-Nelson decomposition
Questions where the object is not the next forecast but the split between trend, cycle, and transitory movement in one series.
Unobserved components model
Estimating latent trend and cycle paths when a direct observable is noisy or mixes several frequency components.
Multivariate time-series
Small systems where output, prices, rates, and labor move together.
VAR
A small macro system where variables forecast each other from their lags.
Bayesian VAR (BVAR)
VAR dynamics with priors that shrink noisy coefficients toward stable behavior.
Structural VAR (SVAR)
VAR forecasts plus restrictions that identify structural shocks.
VECM
Cointegrated variables with short-run adjustment back to long-run equilibrium.
ARDL / bounds testing
Testing whether a long-run equilibrium relation is plausible without requiring every variable to have the same integration order.
Threshold VAR
Shock-transmission questions where recession, inflation, leverage, or slack regimes plausibly change the coefficients.
TVP-VAR
Evidence questions where the same policy shock may not propagate the same way in the 1980s, 2000s, and post-pandemic data.
Markov-switching VAR
Business-cycle questions where observed series appear to alternate between persistent regimes rather than move around one fixed law.
Smooth transition regression
Nonlinear macro relationships where the move between regimes is gradual rather than an abrupt cutoff.
Nowcasting and indicator models
Current-quarter reads from mixed-frequency releases and incoming news.
Bridge equations
Monthly indicators mapped into a quarterly target before the official release.
Dynamic factor model
Many indicators compressed into latent factors for nowcasting.
Nowcasting with news
Forecast changes attributed to the releases that moved the estimate.
MIDAS
Mixed-frequency regressions without first aggregating every predictor.
Kalman filter
Real-time tracking problems where the state is unobserved and the information set updates observation by observation.
Semi-structural / macroeconometric forecasting
Estimated macro blocks tied together by identities and policy rules.
Panel / cross-country empirical macro
Country, regional, or sector panels where cross-section variation matters.
Machine learning / AI forecasting
High-dimensional predictors, shrinkage, selection, and forecast combination.
Ridge regression
Many predictors with shrinkage that keeps coefficients stable.
LASSO
Variable selection and shrinkage when the predictor set is wide.
Forecast ensemble
Model forecasts combined and weighted by observed performance.
Elastic Net
Wide macro predictor sets where correlated groups matter and pure LASSO would choose one variable too arbitrarily.
Random forest
Forecasting with many predictors when interactions and nonlinear thresholds matter more than a single coefficient table.
Gradient boosting
High-dimensional macro forecasting when the gain comes from nonlinear interactions but stopping discipline matters.
LSTM
Sequence-forecasting problems where lag structure is long, nonlinear, and hard to summarize with fixed AR lags.
Network and input-output empirical models
Input-output links, sector exposure, and spillovers across networks.