Estimating Zero Order Models Given Variable Number of Purchase Records Per House
Estimating Zero Order Models Given Variable Number of Purchase Records Per House
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The maximum likelihood estimation method presented in this paper on the other hand, would utilize all purchase records of all the 2496 households in the panel. Does this full utilization of purchase information translate into -6- increased efficiency for the estimation method based on variable number of purchase records per houshold? If yes, how much is the gain in efficiency?
Recall that under rather general regularity conditions (see [6, pp. 295-302]), maximum likelihood estimates are best as...ymptotically normal (BAN). That is, they are consistent, asyptotically normal, and asymptotically efficient. It is shown in [6] that BAN estimators obey the "inverse square root of n relationship"; that if, for large sample sizes, standard error of a BAN estimator is reduced by l//n with an n-fold increase in sample size. Assuming that the maximum likelihood functions presented in this paper satisfy the regularity conditions discussed in [6], the standard errors of maximum likelihood estimates using the_estimation method based on variable number of purchase records would be^ — times the standard errors of maximum likelihood estimates based on fixed number of purchase records per household where n^ and n, denote the corresponding sample sizes.
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To quickly assess the difficulty of the text, read a short excerpt:
The maximum likelihood estimation method presented in this paper on the other hand, would utilize all purchase records of all the 2496 households in the panel. Does this full utilization of purchase information translate into -6- increased efficiency for the estimation method based on variable number of purchase records per houshold? If yes, how much is the gain in efficiency?
Recall that under rather general regularity conditions (see [6, pp. 295-302]), maximum likelihood estimates are best as...ymptotically normal (BAN). That is, they are consistent, asyptotically normal, and asymptotically efficient. It is shown in [6] that BAN estimators obey the "inverse square root of n relationship"; that if, for large sample sizes, standard error of a BAN estimator is reduced by l//n with an n-fold increase in sample size. Assuming that the maximum likelihood functions presented in this paper satisfy the regularity conditions discussed in [6], the standard errors of maximum likelihood estimates using the_estimation method based on variable number of purchase records would be^ — times the standard errors of maximum likelihood estimates based on fixed number of purchase records per household where n^ and n, denote the corresponding sample sizes.
What to read after Estimating Zero Order Models Given Variable Number of Purchase Records Per House?
You can find similar books in the "Read Also" column, or choose other free books by Manohar U Kalwani to read onlineMoreLess
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