Forecast combinations

The combination of individual forecasts is often a useful tool to improve forecast accuracy. This paper considers factor models to produce a single forecast from several individual forecasts provided by the Survey of Professional Forecasters for the main US macroeconomic aggregates.

Contemporary Economics, 2011

The latest global economic crisis has shed some dark light on the possibilities of making valuable and reliable short-and medium-term forecasts with the use of the most commonly applied econometric models in the structural or autoregressive form (SVAR, VAR) as well as general equilibrium models (CGE, DSGE). All these models failed, especially on the verge of the crisis when information on the upcoming peak in the business cycle would have been of the highest value.

SSRN Electronic Journal, 2000

This paper reinterprets Maganelli's (2009) idea of "Forecasting with Judgment" to obtain a dynamic algorithm for combining survey data and time series models for macroeconomic forecasting. Unlike existing combination approaches which typically assign weights to alternative forecasts, the algorithm uses survey forecasts in estimating the parameter vector of a time series model. The methodology is applied to mid-term forecasts of the three-month Euribor.

Journal of Economic Surveys, 2014

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 analyse 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 non-standard. 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.

1994

The National Bureau of Economic Research, in co-operation with the American Statistical Association, conducted a regular quarterly survey of professional macroeconomic forecasters for 22 years beginning in 1968. The survey produced a mass of information about characteristics and results of the forecasting process. Many studies have already used some of this material, but this is the first comprehensive examination of all of it.

SSRN Electronic Journal, 2012

We conduct a systematic comparison of the short-term forecasting abilities of eleven statistical models and professional analysts in a pseudo-real time setting, using a large set of monthly indicators. Our analysis covers the euro area and its five largest countries over the years 1996-2011. We find that summarizing the available monthly information in a few factors is a more promising forecasting strategy than averaging a large number of indicatorbased forecasts. The dynamic and static factor model outperform other models, especially during the crisis period. Judgmental forecasts by professional analysts often embody valuable information that could be used to enhance forecasts derived from purely mechanical procedures.

In this paper, we explore the potential gains from alternative combinations of the surveyed forecasts in the ECB Survey of Professional Forecasters. Our analysis encompasses a variety of methods including statistical combinations based on principal components analysis and trimmed means, performance-based weighting, least squares estimates of optimal weights as well as Bayesian shrinkage. We provide a pseudo real–time out-of-sample performance evaluation of these alternative combinations and check the sensitivity of the results to possible data-snooping bias. The latter robustness check is also informed using a novel real time meta selection procedure which is not subject to the data-snooping critique. For GDP growth and the unemployment rate, only few of the forecast combination schemes are able to outperform the simple equal-weighted average forecast. Conversely, for the inflation rate there is stronger evidence that more refined combinations can lead to improvement over this bench...

2012

Abstract This paper presents a new approach to evaluating multiple economic forecasts. In the past, evaluations have focused on the forecasts of individual variables. However, many macroeconomic variables are forecast at the same time and are used together to describe the state of the economy. It is, therefore, appropriate to examine those forecasts jointly. This specific approach is based on the Sinclair and Stekler (forthcoming) analysis of data revisions.

Economics: The Open-Access, Open-Assessment E-Journal, 2015

The article compares forecast quality from two atheoretical models. Neither method assumed a priori causality and forecasts were generated without additional assumptions about regressors. Tendency survey data was used within the Bayesian averaging of classical estimates (BACE) framework and dynamic factor models (DFM). Two methods for regressor selection were applied within the BACE framework: frequentist averaging (BA) and frequentist (BF) with a collinearity-corrected version of the latter (BFC). Since models yielded multiple forecasts for each period, an approach to combine them was implemented. Results were assessed using in- and out-of-sample prediction errors. Although results did not vary significantly, best performance was observed from Bayesian models adopting the frequentist approach. Forecast of the unemployment rate were generated with the highest precision, followed by rate of GDP growth and CPI. It can be concluded that although these methods are atheoretical, they pro...

2010

Surveys improve forecasting performance by adding explanatory power to a model which is based on only past values of manufacturing growth. The issue addressed in this paper is whether surveys of production expectations, when added to equations that contain lagged values of a headline index pertaining to the real economy, improve forecasting performance. If so, it may be better for researchers to use not just the headline index, but production expectations or the Economic Sentiment Indicator if they wish to better predict manufacturing growth.