Tackling the omitted variables problem without the strong assumptions of proxies

Omitted variables that interact with included independent variables change the vertical placement of observations. Thus, by projecting the data to an output oriented VRS DEA frontier, the influence of omitted variables can be eliminated. After this is done once, the efficient observations can be eliminated and the process repeated. Each subsequent iteration shows the relationship between the dependant and known independent variable for progressively less favorable omitted variables. Building on these ideas, we introduce a new analytical technique named “Reiterative Truncated Projected Least Squares” (RTPLS). We provide both a theoretical argument and simulation evidence that RTPLS produces less bias than ordinary least squares (OLS) when there are omitted variables that interact with the included variables. By way of example, we show how omitted variables have affected the relationship between the monetary base (MB) and the money supply (M2 + CDs) for Japan using monthly data from January 1970 to April 2003.