Challenges from bias when estimating change of renal allograft function

Unobserved heterogeneity bias when estimating the economic model of crime

Using unique and unpublished panel data from selected US cities, the paper investigates the consequences of ignoring unobserved heterogeneity in the unit of observationwhenestimating the economicmodel of crime. Results con® rmthat neglecting to control for unobserved heterogeneity overstates the ability of sanctions to deter criminal activity. Further, thisupwardbias is found tovary signi® cant...

Estimating Renal Function in Paraplegia

Bias and Sensitivity Analysis When Estimating Treatment Effects from the Cox Model with Omitted Covariates

Omission of relevant covariates can lead to bias when estimating treatment or exposure effects from survival data in both randomized controlled trials and observational studies. This paper presents a general approach to assessing bias when covariates are omitted from the Cox model. The proposed method is applicable to both randomized and non-randomized studies. We distinguish between the effect...

Estimating bias and variance from data

The bias-variance decomposition of error provides useful insights into the error performance of a classifier as it is applied to different types of learning task. Most notably, it has been used to explain the extraordinary effectiveness of ensemble learning techniques. It is important that the research community have effective tools for assessing such explanations. To this end, techniques have ...

Estimating Production Functions When Productivity Change Is Endogenous∗

Production function estimation on micro data suffers from persistent unobserved shocks that vary within firms and cause bias. This paper presents an estimation model where the firm chooses capital investment and productivityrelevant intangible assets in response to market conditions under partly fixed adjustment costs. Estimation on Brazilian manufacturing firm data suggests that (i) firms’ uno...