Comparing the accuracy of density forecasts from competing models

A rapidly growing literature emphasizes the importance of evaluating the forecast accuracy of empirical models on the basis of density (as opposed to point) forecasting performance. We propose a test statistic for the null hypothesis that two competing models have equal density forecast accuracy. Monte Carlo simulations suggest that the test, which has a known limiting distribution, displays satisfactory size and power properties. The use of the test is illustrated with an application to exchange rate forecasting. Copyright © 2004 John Wiley & Sons, Ltd.     

Medium‐term forecasts of potential GDP and inflation using age structure information

Economic behaviour as well as economic resources of individuals vary with age. Swedish time series show that the age structure contains information correlated to medium‐term trends in growth and inflation. GDP gaps estimated by age structure regressions are closely related to conventional measures. Monetary policy is believed to affect inflation with a lag of 1 or 2 years. Projections of the population's age structure are comparatively reliable several years ahead and provide additional information to improve on 3–5 years‐ahead forecasts of potential GDP and inflation. Thus there is a potential scope for using age structure based forecasts as an aid to monetary policy formation. Copyright © 2004 John Wiley & Sons, Ltd.     

Estimating private information usage amongst analysts: evidence from UK earnings forecasts

This paper introduces a methodology for estimating the likelihood of private information usage amongst earnings analysts. This is achieved by assuming that one group of analysts generate forecasts based on the underlying dynamics of earnings, while all other analysts are assumed to issue forecasts based on the prevailing consensus forecast. Given this behavioural dichotomy, we are able to derive (and estimate) a structural econometric model of forecast behaviour, which has implications regarding the determinants of analysts' private information endowments and forecast accuracy over the forecast horizon. Copyright © 2010 John Wiley & Sons, Ltd.     

Forecasts of inflation from var models

Why are forecasts of inflation from VAR models so much worse than their forecasts of real variables? This paper documents that relatively poor performance, and finds that the price equation of a VAR model fitted to US post-war data is poorly specified. Statistical work by other authors has found that coefficients in such price equations may not be constant. Based on specific monetary actions, two changes in monetary policy regimes are proposed. Accounting for those two shifts yields significantly more accurate forecasts and lessens the evidence of misspecification.     

A comparative evaluation of objective and subjective weather forecasts in the united states

This paper is concerned primarily with the evaluation and comparison of objective and subjective weather forecasts. Operational forecasts of three weather elements are considered: (1) probability forecasts of precipitation occurrence, (2) categorical (i.e. non-probabilistic) forecasts of maximum and minimum temperatures and (3) categorical forecasts of cloud amount. The objective forecasts are prepared by numerical-statistical procedures, whereas the subjective forecasts are based on the judgements of individual forecasters. In formulating the latter, the forecasters consult information from a variety of sources, including the objective forecasts themselves. The precipitation probability forecasts are found to be both reliable and skilful, and evaluation of the temperature/cloud amount forecasts reveals that they are quite accurate/skilful. Comparison of the objective and subjective forecasts of precipitation occurrence indicates that the latter are generally more skilful than the former for shorter lead times (e.g. 12–24 hours), whereas the two types of forecasts are of approximately equal skill for longer lead times (e.g. 36–48 hours). Similar results are obtained for the maximum and minimum temperature forecasts. Objective cloud amount forecasts are more skilful than subjective cloud amount forecasts for all lead times. Examination of trends in performance over the last decade reveals that both types of forecasts for all three elements increased in skill (or accuracy) over the period, with improvements in objective forecasts equalling or exceeding improvements in subjective forecasts. The role and impact of the objective forecasts in the subjective weather forecasting process are discussed in some detail. The need to conduct controlled experiments and other studies of this process, with particular reference to the assimilation of information from different sources, is emphasized. Important characteristics of the forecasting system in meteorology are identified, and they are used to describe similarities and differences between weather forecasting and forecasting in other fields. Acquisition of some of these characteristics may be beneficial to other forecasting systems.     

Unbiasedness,efficiency and the combination of economic forecasts

This paper considers the problem of determining whether forecasts are unbiased and examines the implications this has for combining different forecasts. The practical issues of how economic forecasts might be combined are discussed. There is an empirical illustration of the procedures in which the properties of UK forecasts from the London Business School, the National Institute, the Henley Centre for Forecasting, Phillips and Drew and the OECD are examined.     

Revisions in analysts' earnings forecasts: evidence of non‐linear adaptive expectations

Prior studies use a linear adaptive expectations model to describe how analysts revise their forecasts of future earnings in response to current forecast errors. However, research shows that extreme forecast errors are less likely than small forecast errors to persist in future years. If analysts recognize this property, their marginal forecast revisions should decrease with the forecast error's magnitude. Therefore, a linear model is likely to be unsatisfactory at describing analysts' forecast revisions. We find that a non‐linear model better describes the relation between analysts' forecast revisions and their forecast errors, and provides a richer theoretical framework for explaining analysts' forecasting behaviour. Our results are consistent with analysts' recognizing the permanent and temporary nature of forecast errors of differing magnitudes. Copyright © 2000 John Wiley & Sons, Ltd.     

Constructing and evaluating core inflation measures from component‐level inflation data

This paper undertakes a comprehensive examination of 10 measures of core inflation and evaluates which measure produces the best forecast of headline inflation out‐of‐sample. We use the Personal Consumption Expenditure Price Index as our measure of inflation. We use two sets of components (17 and 50) of the Personal Consumption Expenditure Price Index to construct these core inflation measures and evaluate these measures at the three time horizons (6, 12 and 24 months) most relevant for monetary policy decisions. The best measure of core inflation for both sets of components and over all time horizons uses weights based on the first principal component of the disaggregated (component‐level) prices. Interestingly, the results vary by the number of components used; when more components are used the weights based on the persistence of each component is statistically equivalent to the weights generated by the first principal component. However, those forecasts using the persistence of 50 components are statistically worse than those generated using the first principal component of 17 components. The statistical superiority of the principal component method is due to the fact that it extracts (in the first principal component) the common source of variation in the component level prices that accurately describes trend inflation over the next 6–24 months.     

Forecasting US inflation by Bayesian model averaging

Recent empirical work has considered the prediction of inflation by combining the information in a large number of time series. One such method that has been found to give consistently good results consists of simple equal‐weighted averaging of the forecasts from a large number of different models, each of which is a linear regression relating inflation to a single predictor and a lagged dependent variable. In this paper, I consider using Bayesian model averaging for pseudo out‐of‐sample prediction of US inflation, and find that it generally gives more accurate forecasts than simple equal‐weighted averaging. This superior performance is consistent across subsamples and a number of inflation measures. Copyright © 2008 John Wiley & Sons, Ltd.     

Bayesian consensus forecasts of macroeconomic variables

Economists, like other forecasters, share knowledge, data and theories in common. Consequently, their forecast errors are likely to be highly dependent. This paper reports on an empirical study of 16 macroeconomic forecasters. Composite forecasts are computed using a sequential weighting scheme that takes dependence into account; these are compared to a simple average and median forecasts. A within-sample composite is also calculated. Both these methods perform significantly better than the average or median of the forecasts. This improvement in accuracy is apparently because the dependence between the forecasters' errors is so high that the optimal composite forecasts sometimes lie outside the range of the individual forecasts.     

Forecasting euro area inflation using dynamic factor measures of underlying inflation

Standard measures of prices are often contaminated by transitory shocks. This has prompted economists to suggest the use of measures of underlying inflation to formulate monetary policy and assist in forecasting observed inflation. Recent work has concentrated on modelling large data sets using factor models. In this paper we estimate factors from data sets of disaggregated price indices for European countries. We then assess the forecasting ability of these factor estimates against other measures of underlying inflation built from more traditional methods. The power to forecast headline inflation over horizons of 12 to 18 months is adopted as a valid criterion to assess forecasting. Empirical results for the five largest euro area countries, as well as for the euro area itself, are presented. Copyright © 2005 John Wiley & Sons, Ltd.     

Real‐time inflation forecast combination for time‐varying coefficient models

We use real‐time macroeconomic variables and combination forecasts with both time‐varying weights and equal weights to forecast inflation in the USA. The combination forecasts compare three sets of commonly used time‐varying coefficient autoregressive models: Gaussian distributed errors, errors with stochastic volatility, and errors with moving average stochastic volatility. Both point forecasts and density forecasts suggest that models combined by equal weights do not produce worse forecasts than those with time‐varying weights. We also find that variable selection, the allowance of time‐varying lag length choice, and the stochastic volatility specification significantly improve forecast performance over standard benchmarks. Finally, when compared with the Survey of Professional Forecasters, the results of the best combination model are found to be highly competitive during the 2007/08 financial crisis.     

Linear constraints and the efficiency of combined forecasts

This note extends some recent results, achieved by Clemen, on constraining the weights of a combined forecast. There is a great potential for improving the ordinary least squares forecast by imposing linear restrictions, and it will be shown how this potential can be exhausted by using an F-test. The corresponding decision procedure leads to a pre-test forecast with good statistical properties.     

A test of the joint efficiency of macroeconomic forecasts using multivariate random forests

We contribute to recent research on the joint evaluation of the properties of macroeconomic forecasts in a multivariate setting. The specific property of forecasts that we are interested in is their joint efficiency. We study the joint efficiency of forecasts by means of multivariate random forests, which we use to model the links between forecast errors and predictor variables in a forecaster's information set. We then use permutation tests to study whether the Mahalanobis distance between the predicted forecast errors for the growth and inflation forecasts of four leading German economic research institutes and actual forecast errors is significantly smaller than under the null hypothesis of forecast efficiency. We reject joint efficiency in several cases, but also document heterogeneity across research institutes with regard to the joint efficiency of their forecasts.     

Forecast accuracy and effort: The case of US inflation rates

This paper investigates the relationship between forecast accuracy and effort, where effort is defined as the number of times the model used to generate forecasts is recursively estimated over the full sample period. More specifically, within a framework of costly effort, optimal effort strategies are derived under the assumption that the dynamics of the variable of interest follow an autoregressive‐type process. Results indicate that the strategies are fairly robust over a wide range of linear and nonlinear processes (including structural break processes), and deliver forecasts of transitory, core and total inflation that require less effort to generate and are as accurate as (that is, are insignificantly different from) those produced with maximum effort. Copyright © 2010 John Wiley & Sons, Ltd.     

Political manoeuvrings as sources of measurement errors in forecasts

We test the extent to which political manoeuvrings can be the sources of measurement errors in forecasts. Our objective is to examine the forecast error based on a simple model in which we attempt to explain deviations between the March budget forecast and the November forecast, and deviations between the outcome and the March budget forecast in the UK. The analysis is based on forecasts made by the general government. We use the forecasts of the variables as alternatives to the outcomes. We also test for political spins in the GDP forecast updates and the GDP forecast errors. We find evidence of partisan and electoral effects in forecast updates and forecast errors. Copyright © 2005 John Wiley & Sons, Ltd.     

To combine or not to combine? issues of combining forecasts

This paper addresses issues such as: Does it always pay to combine individual forecasts of a variable? Should one combine an unbiased forecast with one that is heavily biased? Should one use optimal weights as suggested by Bates and Granger over twenty years ago? A simple model which accounts for the main features of individual forecasts is put forward. Bayesian analysis of the model using noninformative and informative prior probability densities is provided which extends and generalizes results obtained by Winkler (1981) and compared with non-Bayesian methods of combining forecasts relying explicitly on a statistical model for the individual forecasts. It is shown that in some instances it is sensible to use a simple average of individual forecasts instead of using Bates and Granger type weights. Finally, model uncertainty is considered and the issue of combining different models for individual forecasts is addressed.     

Combination of forecasts using self‐organizing algorithms

Based on the theories and methods of self‐organizing data mining, a new forecasting method, called self‐organizing combining forecasting method, is proposed. Compared with optimal linear combining forecasting methods and neural networks combining forecasting methods, the new method can improve the forecasting capability of the model. The superiority of the new method is justified and demonstrated by real applications. Copyright © 2005 John Wiley & Sons, Ltd.     

Nonparametric density forecast based on time‐ and state‐domain

We propose a new nonparametric density forecast based on time‐ and state‐domain smoothing. We analyze some of its asymptotic properties and provide an empirical illustration. Copyright © 2010 John Wiley & Sons, Ltd.