In regressions in which the dependent variable is assumed to be affected by both current and lagged ( past ) values of the independent variable, a distributed lag function is estimated, this function being a weighted average of the current and various lagged independent variable values.
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In an infinite distributed lag model, an infinite number of lag weights need to be estimated; clearly this can be done only if some structure is assumed for the relation between the various lag weights, with the entire infinitude of them expressible in terms of a finite number of assumed underlying parameters.
13.
In the alternative, second, equation, there are only a finite number of lag weights, indicating an assumption that there is a maximum lag beyond which values of the independent variable do not affect the dependent variable; a model based on this assumption is called a " finite distributed lag model ".
14.
In econometrics methodology, Maddala s key areas of research and exposition included distributed lags, generalized least squares, panel data, simultaneous equations, errors in variables, income distribution, switching regressions, disequilibrium models, qualitative and limited dependent variable models, self-selection models, outliers and bootstrap methods, unit roots and cointegration methods, and Bayesian econometrics.
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In a finite distributed lag model, the parameters could be directly estimated by ordinary least squares ( assuming the number of data points sufficiently exceeds the number of lag weights ); nevertheless, such estimation may give very imprecise results due to extreme multicollinearity among the various lagged values of the independent variable, so again it may be necessary to assume some structure for the relation between the various lag weights.
