9 articles
Autoregressive distributed lag model
The autoregressive distributed lag (ARDL) model is an econometric time-series regression explaining a variable by its own lags and current and lagged explanatory variables, used with the Pesaran bounds test for cointegration.
Distributed lag model
A distributed lag model is a time series regression that estimates a predictor's effect on an outcome as spread over several past values, with coefficients called lag weights.
Distributed lag non-linear model
The distributed lag non-linear model (DLNM) is a regression framework estimating non-linear exposure–outcome relationships and how they unfold over time lags, developed for environmental epidemiology.
Joinpoint analysis
Joinpoint analysis is a statistical method that fits segmented linear regression to time-trend data such as disease rates, identifying where the trend changes; it is widely used in cancer surveillance.
Mixed data sampling regression
Mixed data sampling (MIDAS) regression is an econometric method that regresses a low-frequency variable, such as quarterly GDP growth, on more frequently sampled predictors, like monthly indicators, using parsimonious distributed lag weights.
Nonlinear autoregressive distributed lag model
The nonlinear autoregressive distributed lag (NARDL) model is a single-equation error correction model, introduced in 2014, that estimates separate effects of positive and negative changes in each explanatory variable.
Temporally weighted regression
Temporally weighted regression (TWR) is a local regression method that weights observations by their distance in time, addressing temporal nonstationarity, where relationships change over the study period.
Time series regression
Time series regression is a statistical method for modeling relationships between variables observed over time, with errors that are dependent across periods, requiring corrections to ordinary least squares.
Transfer function model
The transfer function model is a time-series regression method that estimates how an input series dynamically influences an output, compressing a distributed lag into a few parameters.