Application of distributed lag and autocorrelated error models to short-run demand analysis

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2017-06-21
Authors
Ladd, George
Martin, James
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Extension and Experiment Station Publications
It can be very challenging to locate information about individual ISU Extension publications via the library website. Quick Search will list the name of the series, but it will not list individual publications within each series. The Parks Library Reference Collection has a List of Current Series, Serial Publications (Series Publications of Agricultural Experiment Station and Cooperative Extension Service), published as of March 2004. It lists each publication from 1888-2004 (by title and publication number - and in some cases it will show an author name).
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Abstract

The objective of the research reported here was to investigate the usefulness of distributed lag economic models and autocorrelated error statistical models for analysis of monthly and quarterly food demand. Distributed lags are a way of incorporating dynamic considerations into econometric models of consumer demand. In the distributed lag model used here, current consumption is the dependent variable, and lagged consumption is one explanatory variable. Testing the significance of the coefficient of lagged consumption tests the hypothesis of a lag in consumer adjustment to conditions affecting demand.

The presence of autocorrelated errors can have serious effects on least squares (L.S.) estimates of coefficients. Autocorrelated errors may frequently occur in equations fitted to monthly and quarterly data. Therefore, equations were estimated by autoregressive least squares (A.L.S.) as well as by least squares. A.L.S.-1 assumes the errors ut to follow a first order autoregressive scheme, ut = β1ut-1 + et. It provides simultaneous estimates of β1 and of the coefficients in the demand equation. A.L.S.-2 assumes the errors to be generated by a second order autoregressive process, ut = β1ut-1 + β2ut-2 + et. It provides simultaneous estimates of β1, β2 and the coefficients in the demand equation.

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