Expert opinion versus expertise in forecasting

Expert opinion is an opinion given by an expert, and it can have significant value in forecasting key policy variables in economics and finance. Expert forecasts can either be expert opinions, or forecasts based on an econometric model. An expert forecast that is based on an econometric model is replicable, and can be defined as a replicable expert forecast (REF), whereas an expert opinion that is not based on an econometric model can be defined as a non-replicable expert forecast (Non-REF). Both replicable and non-replicable expert forecasts may be made available by an expert regarding a policy variable of interest. In this paper we develop a model to generate replicable expert forecasts, and compare REF with Non-REF. A method is presented to compare REF and Non-REF using efficient estimation methods, and a direct test of expertise on expert opinion is given. The latter serves the purpose of investigating whether expert adjustment improves the model-based forecasts. Illustrations for forecasting pharmaceutical SKUs, where the econometric model is of (variations of) the ARIMA type, show the relevance of the new methodology proposed in the paper. In particular, experts possess significant expertise, and expert forecasts are significant in explaining actual sales.

[1]  M. McAleer,et al.  WHEN ARE TWO STEP ESTIMATORS EFFICIENT , 1991 .

[2]  Robert C. Blattberg,et al.  Database Models And Managerial Intuition: 50% Model + 50% Manager , 1990 .

[3]  Michael McAleer,et al.  Intellectual Property Litigation Activity in the USA , 2005 .

[4]  Jeremy P. Smith,et al.  Newey–West covariance matrix estimates for models with generated regressors , 1994 .

[5]  Andrew J. Patton,et al.  Testing Forecast Optimality Under Unknown Loss , 2007 .

[6]  Michael McAleer,et al.  Properties of ordinary least squares estimators in regression models with nonspherical disturbances , 1992 .

[7]  Philip Hans Franses,et al.  Properties of expert adjustments on model-based SKU-level forecasts , 2009 .

[8]  On the optimality of expert-adjusted forecasts , 2007 .

[9]  M. McAleer.  Efficient Estimation: The Rao-Zyskind Condition, Kruskal's Theorem and Ordinary Least Squares , 1992 .

[10]  Philip Hans Franses,et al.  Merging models and experts , 2008 .

[11]  C. BlattbergRobert,et al.  Database Models and Managerial Intuition , 1990 .

[12]  M. McAleer,et al.  Econometric Issues in Macroeconomic Models with Generated Regressors , 1993 .

[13]  P. Goodwin.  Improving the voluntary integration of statistical forecasts and judgment , 2000 .

[14]  Michael McAleer,et al.  The significance of testing empirical non-nested models , 1995 .

[15]  D. Romer,et al.  The FOMC Versus the Staff: Where Can Monetary Policymakers Add Value? , 2008 .

[16]  Adrian Pagan,et al.  Econometric Issues in the Analysis of Regressions with Generated Regressors. , 1984 .

[17]  Ahti Salo,et al.  Adjustment of forecasts with model consistent expectations , 1996 .

[18]  Andrew J. Patton,et al.  Properties of Optimal Forecasts under Asymmetric Loss and Nonlinearity , 2007 .