Estimating the Dynamic Effects of Marketing Communications Expenditures
Estimating the Dynamic Effects of Marketing Communications Expenditures
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00) . 026 LJA(t-3) . 091 (3. 15) . 085 (3. 12) . 079 (2. 97) . 079 (2. 85) . 068 (2. 61) . 077 LJA(t-4) . 040 (1. 59) . 024 (. 94) . 020 (. 70) . 027 LJA(t-5) . 054 (2. 22) . 054 (1. 97) . 009 LJA(t-6) . 005 (. 18) . 003 LMS(t-l) . 348 (3. 66) r2 . 8862 . 9185 . 9326 . 9333 . 9137 r' . 860 . 892 . 904 . 896 . 891 ■36- TABLE I (CON'T) Variable I, J, K=3 , 2 Direct Es, 1 I, J, K-4 tima1, 3, 2 te2 I , J, K=5 , 4 , 3 I , J, K=6 5 Koyck Estimates^, 4 Raw Adjusted d" 1. 32 1. 52 1. 54 1. 60 -. 308 s5... . 34 . 24 . 23 . 20 -. 03 7, 6 p . 42 . 32 . 33 . 31 -37- Footnotes to Table 1 The sample size in all cases is 54 monthly observations. The number in parentheses below each estimate is the corresponding t ratio hav- ing 54-k degrees of freedom where k = number of independent vari- ables in that equation including the intercept.
I, J, K represent the number of lagged terms included in the model for journal advertising, samples and literature, and direct mail, respectively. See (1).
Adjusted Koyck coefficients beyond the values for the last variable included in the Koyck regression were computed using the geometric decline with A = 0.
What to read after Estimating the Dynamic Effects of Marketing Communications Expenditures?
You can find similar books in the "Read Also" column, or choose other free books by David Bruce Montgomery to read onlineMoreLess
To quickly assess the difficulty of the text, read a short excerpt:
00) . 026 LJA(t-3) . 091 (3. 15) . 085 (3. 12) . 079 (2. 97) . 079 (2. 85) . 068 (2. 61) . 077 LJA(t-4) . 040 (1. 59) . 024 (. 94) . 020 (. 70) . 027 LJA(t-5) . 054 (2. 22) . 054 (1. 97) . 009 LJA(t-6) . 005 (. 18) . 003 LMS(t-l) . 348 (3. 66) r2 . 8862 . 9185 . 9326 . 9333 . 9137 r' . 860 . 892 . 904 . 896 . 891 ■36- TABLE I (CON'T) Variable I, J, K=3 , 2 Direct Es, 1 I, J, K-4 tima1, 3, 2 te2 I , J, K=5 , 4 , 3 I , J, K=6 5 Koyck Estimates^, 4 Raw Adjusted d" 1. 32 1. 52 1. 54 1. 60 -. 308 s5... . 34 . 24 . 23 . 20 -. 03 7, 6 p . 42 . 32 . 33 . 31 -37- Footnotes to Table 1 The sample size in all cases is 54 monthly observations. The number in parentheses below each estimate is the corresponding t ratio hav- ing 54-k degrees of freedom where k = number of independent vari- ables in that equation including the intercept.
I, J, K represent the number of lagged terms included in the model for journal advertising, samples and literature, and direct mail, respectively. See (1).
Adjusted Koyck coefficients beyond the values for the last variable included in the Koyck regression were computed using the geometric decline with A = 0.
What to read after Estimating the Dynamic Effects of Marketing Communications Expenditures?
You can find similar books in the "Read Also" column, or choose other free books by David Bruce Montgomery to read onlineMoreLess
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