Dear List members, I wonder if anyone could help me with the following question. Is it possible to calculate confidence intervals for missing data itself? So not for the underlying parameters, but for a single missing value? I have a dataset with stock prices of some 20 equities over a period of 65 days. For one equity the prices are missing in a consecutive period of 11 days. I would like to 'reconstruct' the missing prices using Multiple imputation (or the EM algorithm). I'm using the fact that the daily returns (log of relative price changes) are distributed in a multivariate normal way. Using the EM algorithm I can create a sequence of 11 returns, which can be considered as the 'most likely' values, or point estimates, but I do not get a confidence interval for these values. Is it justifyable to use Multiple Imputation, regarding each missing value as a parameter and simply calculate the point estimate and variance using the well-known combining rules? Or is this approach too simple?
Chiel Bakkeren --------------------------------------------------------------------------- This message (including any attachments) is confidential and may be privileged. If you have received it by mistake please notify the sender by return e-mail and delete this message from your system. Any unauthorised use or dissemination of this message in whole or in part is strictly prohibited. Please note that e-mails are susceptible to change. ABN AMRO Bank N.V. (including its group companies) shall not be liable for the improper or incomplete transmission of the information contained in this communication nor for any delay in its receipt or damage to your system. ABN AMRO Bank N.V. (or its group companies) does not guarantee that the integrity of this communication has been maintained nor that this communication is free of viruses, interceptions or interference. ---------------------------------------------------------------------------
