Simulating Survey-Based Consensus Forecasts With Econometrics

Surveying economists for their projections on a variety of economic and financial indicators and aggregating the results has wide appeal, and for good reason. The wisdom of the crowd, such as it is, tends to be more reliable through time compared with any one forecaster. But there are challenges with standard consensus forecasts—challenges that can minimized if not solved with econometric-based applications.

The challenges include the time and expense in surveying and aggregating the data. The Wall Street Journal, for instance, surveys 60-plus economists each month for their expectations on ten key economic indicators. The question is whether there’s an easier, quicker and low-cost methodology to approximate the task with econometric modeling? As we’ll see, there’s a case for answering with a cautious “yes,” or at least a confident “maybe.”

As an example, consider The Capital Spectator’s current estimate for US retail spending in May, which is due to be released later this morning by the Census Bureau.’s consensus forecast sees sales rising by a strong 0.7% vs. the previous month. The survey estimates range from 0.4%-to-0.9%.

For comparison, let’s run the numbers with The Capital Spectator’s combination-forecasting model, which uses projections based on aggregated results from nine econometric models. The focus here is on generating estimates for the one-year trend, as summarized in the following chart. [See below for actual results.]

By comparison, one-month projections tend to suffer a higher degree of noise. Although all forecasts tend to miss the mark, the one-year trend is somewhat more reliable as a prediction target as well as more relevant for business-cycle analysis. But if there’s a need to generate a one-month estimate that approximates a conventional consensus forecast, it’s a straightforward task to back-out this data out from a one-year projection.

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