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Time Series Analysis
The objective of the time series analysis is to describe the under
lying stochastic process that produces the original price series. These
results can then be used to test hypotheses regarding the series of
interest or forecast future values. A distinction regarding the result
ing model is that the parameters determined are referred to in the
literature as being "mechanically" derived, often considered devoid of
theoretical economic content (Zellner, 1979). However, recent studies
have supported the contention that time series models, in fact, are
consistent with structural economic models (Anderson et al., 1983). In
addition, the dynamic adjustment properties of price series data as
revealed by time series analysis will allow testing of hypotheses orig
inally motivated by the theory.
There exists two principal time series approaches: time domain
(time series) analysis and frequency domain (spectral) analysis. The
two are theoretically equivalent (Granger and Newbold, 1977). As Ngenge
(1982) states, a result in one domain always has its equivalent result
in the other domain. The spectral approach is particularly useful if
the price series is suspected of being characterized by significant
periodicity and if the nature of these periodic components are unknown.
Price data for shrimp in the U.S. have empirically been found to not
contain an identifiable cyclical component (Thompson and Roberts, 1983).
Rather, periodicity is restricted to seasonal influences. Thus, the
spectral approach would be inappropriate. This study primarily uses the
more appropriate Box-Jenkins time domain approach, due to the nature of
the data, access to and familiarity with established software and the
relative ease of Box-Jenkins estimation (Box and Jenkins, 1976).