The degree to which a model explains (statistically speaking) variations in the calibration data. Fit is likely to be misleading as a criterion for selecting and developing forecasting models, because it typically has only a weak relationship to ex ante forecast accuracy (Armstrong 2001d). Fit tends to favor complex models, and these models often do not hold up in forecasting, especially when using time-series data. Nevertheless, Pant and Starbuck (1990) found a modest relationship between fit (when using MAPE) and short-term forecasts for 13 extrapolation methods. It is more relevant when working with cross-sectional data.