In: Statistics and Probability
One of my recent papers examine important and timely research questions using a field experiment approach in eBay auctions: (i) Can merchandise return policy (MRP; liberalness in the MRP) increase consumers’ willingness to pay? and (ii) is the marginal impact of MRP diminishing? In this study we created three brand new eBay seller profiles, all with zip-codes located within five miles of each other in a college town in the U.S. The eBay stores received exactly the same product description, pictures, outbound shipping policies, etc. The only difference among the three sellers was the extent of liberalness in the MRP and we chose to operationalize MRP liberalness in terms of the time window during which the customer is allowed to return the purchased product. The most conservative MRP (Storefront 1 and 1a) involved a 15-day return window. According to trade publications, this return condition is more conservative than retail-industry averages. Storefront2 and 2a received a 30-day return window, which corresponds closely with retail-industry averages. Finally, Storefront3 and 3a received a 60-day return window, which is more liberal than many retailers offer at this point. The other elements of the return remained constant across the three storefronts. Therefore, in terms of overall return-policy liberalness, it could be argued that Storefront1/1a < Storefront 2/2a < Storefront 3/3a . It is important to note that it is very common in my data that we observe a customer’s bidding behavior in several auctions. [Question] During the revision stage of the journal publication process, one of the reviewer’s comment was that the I may use a fixed effects model to control for unobserved individual fixed effects. Do you agree or disagree with the above statement? Please explain with details.
solution :
Prior the scientist was useing the relapse model to foresee coupon reclamation, in light of buyer pay level ,while controlling for variable shopping bushel size and socioeconomics with respect to having youngsters in the family. The relapse model can be spoken to as
y=\beta _{0}+\beta _{1}x1+\beta _{2}x2+\beta _{3}x3+\varepsilon
x1= purchaser pay
x2=shopping bushel measure (contol variable).
x3=demographics with respect to having children(control variable).
However,entering settled impact factor would give better gauge of the model as it would improve the expectation more dependent on settled impact proposed by the reviewers.In irregular impact the individual impact are not controlled and are caught up in blunder ter,The settled factor controls for individual-level impacts using sham factors and consequently more precise forecast is conceivable.
The model would be better spoken to as
y=\beta {0}+\beta _{1}x1+\beta _{2}x2+\beta _{3}x3+\varepsilon +v{i}
vi is the settled impact of all the factor.
Subsequently the best relapse mode should produce the consolidated results of both settled and irregular impact.
In short I can state that I concur with the above explanation.