A UNIFYING VIEW ON MULTI‐STEP FORECASTING USING AN AUTOREGRESSION

Abstract This paper unifies two methodologies for multi‐step forecasting from autoregressive time series models. The first is covered in most of the traditional time series literature and it uses short‐horizon forecasts to compute longer‐horizon forecasts, while the estimation method minimizes one‐step‐ahead forecast errors. The second methodology considers direct multi‐step estimation and forecasting. In this paper, we show that both approaches are special (boundary) cases of a technique called partial least squares (PLS) when this technique is applied to an autoregression. We outline this methodology and show how it unifies the other two. We also illustrate the practical relevance of the resultant PLS autoregression for 17 quarterly, seasonally adjusted, industrial production series. Our main findings are that both boundary models can be improved by including factors indicated from the PLS technique.     

Statistical institutes and economic prosperity

The quality of economic institutions can impact economic growth and it can mediate the relation between economic growth and its drivers. We examine the relevance of one such institution, which is the establishment of a national statistical institute for, amongst others, national accounts. We collect data for 106 countries, and we estimate that there are four separate clusters of countries with similar establishment dates. For these clusters we fit regression models to explain economic growth, and we obtain significant differences across these clusters with respect to relevant explanatory variables and effect sizes, suggesting that a national statistics institute indeed is an important institution for the macro-economy.     

EVALUATING MACROECONOMIC FORECASTS: A CONCISE REVIEW OF SOME RECENT DEVELOPMENTS

Macroeconomic forecasts are frequently produced, widely published, intensively discussed, and comprehensively used. The formal evaluation of such forecasts has a long research history. Recently, a new angle to the evaluation of forecasts has been addressed, and in this review we analyze some recent developments from that perspective. The literature on forecast evaluation predominantly assumes that macroeconomic forecasts are generated from econometric models. In practice, however, most macroeconomic forecasts, such as those from the IMF, World Bank, OECD, Federal Reserve Board, Federal Open Market Committee (FOMC), and the ECB, are typically based on econometric model forecasts jointly with human intuition. This seemingly inevitable combination renders most of these forecasts biased and, as such, their evaluation becomes nonstandard. In this review, we consider the evaluation of two forecasts in which: (i) the two forecasts are generated from two distinct econometric models; (ii) one forecast is generated from an econometric model and the other is obtained as a combination of a model and intuition; and (iii) the two forecasts are generated from two distinct (but unknown) combinations of different models and intuition. It is shown that alternative tools are needed to compare and evaluate the forecasts in each of these three situations. These alternative techniques are illustrated by comparing the forecasts from the (econometric) Staff of the Federal Reserve Board and the FOMC on inflation, unemployment, and real GDP growth. It is shown that the FOMC does not forecast significantly better than the Staff, and that the intuition of the FOMC does not add significantly in forecasting the actual values of the economic fundamentals. This would seem to belie the purported expertise of the FOMC.     

What makes public-private partnerships work? Survey research into the outcomes and the quality of cooperation in PPPs

Public–private partnerships (PPPs) are often regarded as the solution for time and budget overruns in large infrastructural projects, but not all are successful. This raises the question of what really makes PPPs work. Focusing on the role of relational aspects, this article examines the degree to which trust and managerial activities correlate to the perceived performance and cooperation process in PPP projects. A multilevel analysis of survey data from 144 respondents involved in Dutch PPP projects shows that both trust and management correlate significantly to the perceived performance of these projects. Moreover, trust is associated with a good cooperation process.     

Expert opinion versus expertise in forecasting

Expert opinion is an opinion given by an expert, and it can have significant value in forecasting key policy variables in economics and finance. Expert forecasts can either be expert opinions, or forecasts based on an econometric model. An expert forecast that is based on an econometric model is replicable, and can be defined as a replicable expert forecast (REF), whereas an expert opinion that is not based on an econometric model can be defined as a non-replicable expert forecast (Non-REF). Both REF and Non-REF may be made available by an expert regarding a policy variable of interest. In this paper, we develop a model to generate REF, and compare REF with Non-REF. A method is presented to compare REF and Non-REF using efficient estimation methods, and a direct test of expertise on expert opinion is given. The latter serves the purpose of investigating whether expert adjustment improves the model-based forecasts. Illustrations for forecasting pharmaceutical stock keeping unit (SKUs), where the econometric model is of (variations of) the autoregressive integrated moving average model (ARIMA) type, show the relevance of the new methodology proposed in the paper. In particular, experts possess significant expertise, and expert forecasts are significant in explaining actual sales.     

Estimating loss functions of experts

We propose a new and simple methodology to estimate the loss function associated with experts’ forecasts. Under the assumption of conditional normality of the data and the forecast distribution, the asymmetry parameter of the lin–lin and linex loss function can easily be estimated using a linear regression. This regression also provides an estimate for potential systematic bias in the forecasts of the experts. The residuals of the regression are the input for a test for the validity of the normality assumption. We apply our approach to a large data set of SKU-level sales forecasts made by experts, and we compare the outcomes with those for statistical model-based forecasts of the same sales data. We find substantial evidence for asymmetry in the loss functions of the experts, with underprediction penalized more than overprediction.