Using Econometric Forecasting To Correct for Missing Data: Homicide and the Early Registration Area (From Trends, Risks, and Interventions in Lethal Violence: Proceedings of the Third Annual Spring Symposium of the Homicide Research Working Group, P 51-63, 1995, Carolyn Block and Richard Block, eds.

• NCJ Number: 159893
• Author(s): D L Eckberg
• Date Published: 1995
• Length: 13 pages
• Sponsoring Agency: Office of Justice Programs (OJP)
• Sale Source: National Institute of Justice/NCJRS
• Publication Type: Research (Applied/Empirical)
• Language: English
• Country: United States of America
• Note: *This document is currently unavailable from NCJRS. NIJ Research Report, See NCJ-154254 for complete document.

Annotation

Regression-based econometric forecasting methodology can be used to estimate rates of phenomena like homicide when data are missing in time-series data sets.

Abstract

Missing data lead to unreliability of counts and rates, thereby reducing the effectiveness of statistical techniques used in causal analysis. Because data are unlikely to be missing randomly, it is possible that time-series trends will be portrayed incorrectly and that coefficients in linear models will be biased. This paper outlines U.S. crude homicide trends that emerge from direct use of published death registration notices, and discusses specific missing data problems with earlier data sets. Regression- based econometric forecasting is used to derive year-by-year forecasts of U.S. crude homicide rates; these are compared with previous estimates. Finally, limitations of this methodology are described. 1 table, 3 figures, and 25 references