Making Forecasts

David has made economic forecasts throughout his professional career. His early experiences in forecast failure motivated him to examine the roles of forecasts in economics, and thence to understand forecasts qua forecasts and seek out how to improve them.

David first became interested in forecasting in 1964 as an undergraduate at the University of Aberdeen. He was very much influenced by the empirical economic models of Klein (1950) and Tinbergen (1951), who had suggested the feasibility of forecasting future outcomes. In his undergraduate thesis, David estimated a regression model for annual UK consumers' expenditure given current income and lagged expenditure—painstakingly worked out on a mechanical calculator. Using the whole-sample parameter estimates, he cal­culated a “forecast” of the last observation to see how close it was to the out­come—in effect, evaluating the last residual of his estimation period. The forecast and the outcome were reasonably close, but unsurprisingly so because the observation that was forecast was in the estimation sample, and hence the corresponding forecast error was included in the sum of squared residuals that least-square estimation minimised.

A few years later, when writing his PhD thesis under Denis Sargan at LSE, David developed a small macro-model of the UK economy that included an equation for consumers' expenditure. David's forecasts from that model did not fare well. In late 1967, he calculated ex ante forecasts of consumers' expenditure for the next two quarters: 1968Q1 and 1968Q2. When actual expenditure was later reported by the Central Statistical Office, David's model had massive forecast failure and, in his own words, it took him years to under­stand why such forecast failure is commonplace.

That particular forecast failure arose from a change in economic policy. During 1968Q1, the Chancellor of the Exchequer (i.e.

Other economists were also evaluating forecasts from macro-models, and their contributions stimulated David's own thinking on the topic. Charles Nelson in particular wrote two influential papers on ex ante forecasts: Nelson (1972) and Cooper and Nelson (1975). Using methods proposed by Box and Jenkins (1970), Nelson and Cooper showed that forecasts from univariate time-series models could beat forecasts from large empirical economic models such as the FRB—MIT—PENN model. From an LSE perspective, such large models treated dynamics inadequately, often simply as autocorrelated errors in static equations. David suspected that, in a trade-off between misspecified dynamics and omitted economics, models that included only dynamics could forecast better. Empirically, David found that simple dynamic models did indeed forecast better than static economic models, even though the latter embedded economic theory whereas the former did not.

This debate on forecast performance motivated David to investigate the nature of predictive failure. Why did models built from even the best available economics using the latest econometrics and fairly good data not produce useful forecasts? In Hendry (1979), David attributed ex post predictive failure to model misspecification. Chong and Hendry (1986) then developed forecast-encompassing statistics, a technique for comparing different models' forecasts. This approach is feasible even if the models themselves are unavail­able, as is common with proprietary models and for judgmentally based fore­casts. Hendry (1986d) looked at forecasting from dynamic systems, mainly to improve the power to test models.

The forecast failures documented in Hendry (1974, 1979) and elsewhere actually signalled a different source of forecasting problems with econometric models: unanticipated changes in the DGP Those forecast failures also sug­gested that it was possible to develop a general theory of economic forecasting in which the forecasting model was misspecified for a DGP that itself was nonconstant over time. These realisations came after a long hiatus, and they lead to the next section.

4.2