Abstract
In this chapter, a vector subset autoregressive process is fitted using a block modified Choleski decomposition method and a leaps-and-bounds algorithm to attain the best subset autoregression for each size (number of non-zero coefficient matrices). Model selection criteria are then employed to select the optimum subset AR. (See Penm and Terrell, 1982.) In this chapter the above approach is extended to select the optimum multivariate subset autoregression with constraints, putting the final optimal model in ideal form for detecting Granger causality patterns. The vector system comprising the variables included in the 1981 analysis by Fels and Tran Van Hoa (i.e. Average Weekly Earnings, Consumer Price Index. Index of Minimum Wage Rates, demand for labor, and a strikes variable) is re-examined for the period 1953-76 by using the proposed algorithm, and direct and indirect causal relationships are established.
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