Enhancing survey‐based investment forecasts

• Author: Ciaran Driver,Nigel Meade
• Published date: 01 April 2019
• DOI: http://doi.org/10.1002/for.2567
• Date: 01 April 2019

RESEARCH ARTICLE

Enhancing survey‐based investment forecasts

Ciaran Driver

1

| Nigel Meade

2

1

School of Finance and Management,

SOAS, University of London, London, UK

2

The Business School, Imperial College

London, London, UK

Correspondence

Nigel Meade, The Business School,

Imperial College London, London SW7

2AZ, UK.

Email: n.meade@imperial.ac.uk

Funding information

British Academy, Grant/Award Number:

SG150307

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 predictorshave value but are biased and inefficient. For shorterhori-

zons 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.

KEYWORDS

business surveys, directional forecasting, forecasting accuracy, forecasting evaluation, time series

1|INTRODUCTION

Business surveys, if timely and accurate, enable a first

view of emerging trends that may not be apparent from

a time series analysis of official statistics or econometric

models. We examine the accuracy of various predictors

contained in a national business survey and investigate

whether the variations in accuracy of directional and

point forecasts can be explained by information available

at the time the forecasts were made. We further study

how that accuracy changes over a range of horizons, as

different business decisions necessitate different response

times so that forecast horizons useful, say, for pricing or

inventory decisions will be shorter than those relevant

to longer term capacity decisions.

Our data source is a long‐established, reputable busi-

ness survey of South Africa carried out by the Bureau of

Economic Research (BER) at the University of Stellen-

bosch. The survey contains a variety of business informa-

tion, but we consider only indicators of capital

investment intentions. Assessing and improving the accu-

racy of investment forecasts is important because the

investment spend affects both the demand side and the

supply side of the economy. However, investment is also

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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the

original work is properly cited.

© 2018 The Authors Journal of Forecasting Published by John Wiley & Sons, Ltd.

Received: 27 April 2018 Revised: 31 October 2018 Accepted: 4 December 2018

DOI: 10.1002/for.2567

236 Journal of Forecasting. 2019;38:236–255.wileyonlinelibrary.com/journal/for

one of the most difficult series to predict accurately.

1

Our

paper assesses the added value of the BER survey series

in respect of directional and point forecasts of

fixed investment. In addition, we enhance the survey

predictions with a probability of directional accuracy

and a predicted magnitude of the error in a point fore-

cast, both based on contemporaneous information.

There are at least four relevant predictors of

manufacturing capital investment in the BER quarterly

survey: (a) intentions for the current quarter (the Nowcast)

(b) intentions for the next quarter; (c) intentions for a year

ahead in respect of a narrow investment category; and (d)

a general indicator of business conditions for a year ahead.

The definitive values of total manufacturing capital invest-

ment are published after some delay and revision by the

South African Reserve Bank. Throughout our study, to

replicate forecasting in real time, we pay particular atten-

tion to the timing of the publication of the survey and

the official data. We address both directional accuracy

and point accuracy of the survey‐based forecasts; in both

cases a time series forecast is used as a benchmark. Having

assessed the directional accuracy of the survey forecasts

using a battery of tests, we model the probability of a cor-

rect directional prediction using data available at the time

of prediction. This gives an objective measure of confi-

dence in the survey prediction, which is an important

issue as turning points are notoriously difficult to predict.

Addressing the accuracy of point forecasts, we consider

several ways in which survey forecasts might be improved:

rescaling the predictions; including the predictions in time

series regressions; and by combining time series and sur-

vey results to produce hybrid forecasts. In parallel with

providing a confidence measure for directional accuracy,

we model the magnitude of survey forecast errors using

data available at the time of prediction.

The structure of the paper is as follows. In Section 2, we

review the context of the business survey—provided by the

South African Bureau of Economic Research—and report

on related literature. Methodologies and measures for

assessing forecast accuracy are assessed in Section 3. We

describe our data set in Section 4. In Section 5, we assess

the directional accuracy of the BER survey predictions;

we investigate the consistency of model accuracy before,

during, and after the financial crisis; we also propose sev-

eral variables likely to affect the stability of the survey pre-

dictors using data available at the time of the survey and,

using these stability variables, we model the probability

of directional accuracy. In Section 6, we investigate the

accuracy of survey‐based point forecasts. We also examine

the effect of the financial crisis on our results and we model

the magnitude of survey forecast errors using the stability

variables proposed earlier. We summarize our findings

and offer our conclusions in Section 7.

2|CONTEXTUAL BACKGROUND

AND RELATED WORK

The context of our study is the South African economy,

whose trajectory is in some respects a puzzle. Its recent

growth rate has not been above 4% for any sustained

period, except for the 4 years before the financial crisis,

when it averaged about 5%. The economy is widely

perceived as being held back by a variety of poorly under-

stood “soft”factors inhibiting faster investment and

growth. Evidence suggests a positive relationship between

investment and the growth of GDP per worker in non‐

OECD countries (Bond, Leblebicioğlu, & Schiantarelli,

2010). However, a particular concern for South Africa is

that capital investment in the decade since independence

only contributed about a quarter of GDP growth com-

pared with almost a third in a panel of 10 similar coun-

tries selected on the basis of income and population

(Eyraud, 2009); such a pattern has been shown to have

been detrimental to the country's productivity growth

(Arora & Bhundia, 2003).

Investment did accelerate somewhat in the decade

before the financial crisis (Fedderke, 2009) but there has

been concern that this was concentrated in the

nontradable sectors (Frankel, Smit, & Sturzenegger,

2008). Comparatively, the overall investment‐to‐GDP

ratio for the decade from 2000 was far lower than for sim-

ilar countries (Viegi, 2014). The financial crisis adversely

affected South Africa from the first quarter of 2009 and

resulted in lower growth for a considerable period of time

(OECD, 2017). The manufacturing investment rate

declined sharply and, despite a considerable devaluation,

the sector's employment fell by nearly 15%. The main

constraints on corporate investment appear to be nonfi-

nancial factors, reflecting fears over matters such as

corruption, crime, and infrastructure, and also the chal-

lenges posed by the dual economy structure and extreme

inequality that characterize the country (World Bank,

2010, 2018). These constraints may also affect foreign

direct investment (FDI): In recent years FDI has averaged

less than 5% of overall investment, and the manufactur-

ing sector tends to receive only about a fifth of these

funds (South African Reserve Bank, 2018).

Some improvement is now expected. Reflecting recent

changes in the administration and political reforms, the

OECD currently estimates that real GDP growth will

1

Investment reflects jumps in expectations regarding events far in the

future and is thus more autonomous (and more variable) than other

aggregates. The average 1‐year‐ahead root mean square error (RMSE)

for fixed investment taken over a number of forecasting models has been

shown to be four times larger than for gross domestic product (GDP)

growth (Granger, 1994).

DRIVER AND MEADE 237