Enhancing survey‐based investment forecasts

Abstract

We investigate the accuracy of capital investment predictors from a national business survey of South African manufacturing. Based on data available to correspondents at the time of survey completion, we propose variables that might inform the confidence that can be attached to their predictions. Having calibrated the survey predictors' directional accuracy, we model the probability of a correct directional prediction using logistic regression with the proposed variables. For point forecasting, we compare the accuracy of rescaled survey forecasts with time series benchmarks and some survey/time series hybrid models. In addition, using the same set of variables, we model the magnitude of survey prediction errors. Directional forecast tests showed that three out of four survey predictors have value but are biased and inefficient. For shorter horizons we found that survey forecasts, enhanced by time series data, significantly improved point forecasting accuracy. For longer horizons the survey predictors were at least as accurate as alternatives. The usefulness of the more accurate of the predictors examined is enhanced by auxiliary information, namely the probability of directional accuracy and the estimated error magnitude.

Assessing the usefulness of survey‐based data in forecasting firms' capital formation: Evidence from Italy

“…As found more generally by Driver and Meade (2019), these findings confirm the value added of exploiting survey data in macroeconomic analyses and forecasting exercises, whenever possible, especially in the case of extreme shocks. The advantage in the case of Italy is the regular availability and easy access of the surveys employed in this paper.…”

Forecasting Corporate Capital Accumulation in Italy: The Role of Survey-Based Information

“…This result holds when a credit-constraint variable is employed to measure fi-nancial factors; moreover, when the in-sample estimation window includes the two crisis episodes, the cointegrated VECM specification with a survey-based uncertainty indicator presents the highest forecasting power. As found more generally by Driver and Meade (2019), these findings point to the usefulness of employing, whenever possible, microfounded measures in macroeconomic forecast exercises also in the Italian case, as well as confirming the exceptional nature of the two investigated crisis episodes.…”

A Genetic Programming Approach for Economic Forecasting with Survey Expectations

“…Using qualitative survey responses from the ifo's World Economic Survey (WES), in [63], it was found that the respondents provided statistically significant directional forecasts. In [64], the authors used survey data from South Africa to investigate the accuracy of directional and point forecasts of investment, and found that for shorter horizons, survey forecasts enhanced by time-series data significantly improved point forecasting accuracy.…”