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While the deterministic predictors are asymptotically biased, there are some Monte Carlo experiments that suggest the magnitude of this bias is rather insignificant. Here, we offer a possible explanation of the smallness of the deterministic bias. It is shown that when the error terms have small standard deviation, the deterministic predictor turns out to be asymptotically unbiased. The results are based on deriving asymptotic expansions for alternative predictors. The asymptotic expansions carried out here are similar to the large-sample asymptotic expansions; however, the expansions here are in terms of the standard deviation of the disturbance terms. The results are then used to obtain the asymptotic bias and asymptotic mean squared prediction errors of the deterministic and stochastic predictors of a model containing the Box-Cox transformation.<\/jats:p>","DOI":"10.1017\/s0266466600005855","type":"journal-article","created":{"date-parts":[[2009,3,10]],"date-time":"2009-03-10T15:36:14Z","timestamp":1236699374000},"page":"368-391","source":"Crossref","is-referenced-by-count":0,"title":["Comparison of Deterministic and Stochastic Predictors in Nonlinear Systems When the Disturbances Are Small"],"prefix":"10.1017","volume":"13","author":[{"given":"Hassan","family":"Arvin-Rad","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2009,2,11]]},"reference":[{"key":"S0266466600005855_ref028","unstructured":"Mariano R.S. & Brown B.W. (1985) Finite sample properties of predictors in nonlinear systems: Some initial results. 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