An evaluation of forecasting using leading indicators

Abstract

We consider the use of indices of leading indicators in forecasting and macro-economic modelling. The procedures used to select the components and construct the indices are examined, noting that the composition of indicator systems gets altered frequently. Cointegration within the indices, and between their components and macro-economic variables are considered as well as the role of co-breaking to mitigate regime shifts. Issues of model choice and data-based restrictions are investigated. A framework is proposed for index analysis and selecting indices, and applied to the UK longer-leading indicator. The effects of adding leading indicators to macro models are considered theoretically and for UK data.

A comparison of methods for the construction of composite coincident and leading indexes for the UK

“…As usual, omission of relevant variables yields biased estimators of the parameters of the included regressors, which can translate into biased and inefficient composite leading indicators. See Emerson and Hendry (1996) for additional details and generalizations and, e.g., Clements and Hendry (1999) for the consequences of omitting cointegrating relations when forecasting. As long as m + n is small enough with respect to the sample size, the number and composition of the cointegrating vectors can be readily tested, see e.g.…”

“…However, from an econometric point of view, NMB CLIs are also subject to several criticisms, see e.g. Emerson and Hendry (1996) and Marcellino …”

The ET Interview: Professor David F. Hendry

“…That enquiry triggered my interest in developing a viable theory of forecasting. Even after numerous papers-starting with [124], [125], [137], [138], [139], and [141]-that research program is still ongoing.…”

Economic forecasting: some lessons from recent research

“…Consequently, we regard surveys as a noncausal input to the forecasting processes. Such information could be entered as a regressor in forecasting systems, but that seems subject to the same problems as Emerson and Hendry (1996) found for leading indicators (see section 7.10). Alternatively, surveys might inform the estimate of the variables at the forecast origin (see section 7.6), perhaps guiding the choice of IC.…”

“…Nevertheless, 'early-warning' signals merit serious consideration, and we believe that high-frequency readings on the state of the economy must play a role in this area. Emerson and Hendry (1996) found that in theory and practice composite leading indicators (CLIs) by themselves were not likely to prove good at forecasting relative to robustified devices. Moreover, adding a leading indicator to a VAR, as in Artis, Bladen-Hovell, Osborn, Smith and Zhang (1995), might even jeopardize the latter's robustness for little gain (but see Marsland and Weale, 1992).…”