A Monte Carlo Study of Aggregation Effects On Regression Parameter Estimates
A Monte Carlo Study of Aggregation Effects On Regression Parameter Estimates
Edwin Kuh
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551 TOTAL: 41 22 19 23 20 40 NOTE: Based on cumulative distribution of sales for the four Compustat-based industries (see text for notation and fuller explanation) and on the Monte Carlo series for which r =0 and R =. 90. (The A run had a total of 20 firms, and the B run 40 firms. ) TABLE 2 Average Micro Intercorrelations MACHINE TOOLS PETROLEUM REFINERIES RETAIL STEEL Mo. Of firms Sales Capital Stock . 75 No. Of Capital Firms Sales Stock 4 . 68 . 80 No. Of Firms 4 Sales . 54 Capital Stock . 63... No. Of Firms Sales . 67 Capital Stock 5 . 61 5 . 54 11 . 50 . 62 7 . 58 . 75 7 . 44 . 63 8 . 65 . 54 17 . 48 . 51 10 . 47 . 63 10 . 40 . 53 11 . 55 . 44 23 . 43 . 48 13 . 43 . 56 13 . 37 . 47 14 . 49 . 40 29 . 40 . 42 16 . 38 . 50 16 . 34 . 41 17 . 43 . 33 36 . 37 . 39 19 . 35 . 46 19 . 30 . 36 20 . 40 . 30 41 . 33 . 34 22 . 32 . 40 23 . 36 . 26 Source: Ccrnpustat-based firms and industries: see text. TABLE 3 Estimated macro parameters and their standard errors for r. =0. JO (True ^=. 3, 6 2 =0) \..
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