A forecast is one estimate. Its benchmarks, competing models and historical errors help us assess how much weight to place on it.
Angavu evaluates different forecasting approaches. Which models appear in a published comparison depends on the indicator, available data and evaluation evidence.
01 / Benchmarks
Random walk, integrated moving average (IMA(1,1)) and direct autoregression provide benchmark forecasts. More elaborate models are evaluated against their forecasting performance.
02 / Time series
Autoregressive integrated moving average (ARIMA) models are evaluated using fixed, recursive and rolling estimation windows. Their forecasting performance is compared with the benchmarks.
03 / Inflation
Inflation-specific models examine the relationship between inflation, its past values and economic slack. Structural Phillips-curve models are evaluated only when the necessary expectations and slack information would have been available at the forecast origin.
NMSFE compares a model's mean squared forecast error with the corresponding benchmark. Values below 1 indicate lower forecast error than the benchmark.
Evaluation / 01
Angavu compares models using their forecasting errors over a historical validation period. A model with a low error may still require stability checks or review before being used in a published forecast. Where sufficient later data are available, separate tests examine whether the selected model continues to perform well and whether forecast adjustments improve its accuracy.
Release / 02
Each forecast identifies the period being predicted, the model used, its forecast origin and the information cutoff. Forecasts reconstructed later are distinguished from forecasts actually produced and frozen at the time.