Calibration and Resolution Diagnostics for Bank of England Density Forecasts

This paper applies new diagnostics to the Bank of England’s pioneering density forecasts (fan charts). We compute their implicit probability forecast for annual rates of inflation and output growth that exceed a given threshold (in this case, the target inflation rate and 2.5% respectively.) Unlike earlier work on these forecasts, we measure both their calibration and their resolution, providing both formal tests and graphical interpretations of the results. These results both reinforce earlier evidence on some of the limitations of these forecasts and provide new evidence on their information content. Cet étude développe et applique des nouvelles techniques pour diagnostiquer les prévisions de densité de la Banque d’Angleterre (leur “fan charts”). Nous calculons leurs probabilités implicites pour des taux d’inflation et de croissance du PIB qui dépassent des seuils critiques (soit le taux d’inflation ciblé, soit 2.5%.) En contraste avec des travaux antérieurs sur ces prévisions, nous gaugeons leur calibration aussi bien que leur résolution, en donnant des tests formels et des interprétations graphiques. Les résultats renforcent des conclusions déjà existant sur les limites de ces prévisions et ils donnent de nouvelles évidences sur leurs valeurs ajoutées.calibration, density forecast, probability forecast, resolu, calibration, prévisions de densité, probabilités implicites, résolution.

The Reliability of Inflation Forecasts Based on Output Gap Estimates in Real Time

A stable predictive relationship between inflation and the output gap, often referred to as a Phillips curve, provides the basis for empirical formulations of countercyclical monetary policy in many models. In this paper, we provide an empirical evaluation of the usefulness of alternative univariate and multivariate estimates of the output gap for predicting inflation. In-sample analysis based on ex post output gap measures suggests that many of the alternative estimates we examine appear to be quite useful for predicting inflation. However, examination of out-of-sample forecasts using real-time estimates of the same measures suggests that this predictive ability is mostly illusory. We find that the usefulness of output gaps as predictors of inflation has been severely overstated and that real-time forecasts using the output gap are often less accurate than forecasts that abstract from the output gap concept altogether. Dans ce papier, on jauge l'utilité de plusieurs estimations (univariées autant que multivariées) de l'écart de production pour prévoir le taux d'inflation. Une analyse ex post suggère que plusieurs de ces estimations aident à prédire l'inflation. Néanmoins, les erreurs de prédictions hors de l'enchantillon qui se sont construites avec les écarts de production estimés en temps réel indiquent que cette amélioration de prédiction est illusoire. On trouve que l'utilité des écarts de production pour prédire l'inflation a été exagérée et que les prédictions faites avec l'écart de production sont souvent moins précises que celles qui ignorent le concept d'un écart de production.Phillips curve, output gap, inflation forecasts, real-time data, La courbe de Phillips, l'écart de production, des prévisions d'inflation, des données en temps réel

THE CALIBRATION OF PROBABILISTIC ECONOMIC FORECASTS

A probabilistic forecast is the estimated probability with which a future event will satisfy a particular criterion. One interesting feature of such forecasts is their calibration, or the match between predicted probabilities and actual outcome probabilities. Calibration has been evaluated in the past by gropuing probability forecasts into discrete categories. Here we show that we can do so without discrete groupings; the kernel estimators that we use produce efficiency gains and smooth estimated curves relating predicted and actual probabilities. We use such estimates to evaluate the empirical evidence on calibration error in a number of economic applications including recession and inflation prediction, using both forecasts made and stored in real time and pseudo-forecasts made using the data vintage available at the forecast date. We evaluate outcomes using both first-release outcome measures as well as later, thoroughly revised data. We find strong evidence of incorrect calibration in professional forecasts of recessions and inflation. We also present evidence of asymmetries in the performace of inflation forecasts based on real-time output gaps.

The Calibration of Probabilistic Economic Forecasts

A probabilistic forecast is the estimated probability with which a future event will satisfy a specified criterion. One interesting feature of such forecasts is their calibration, or the match between predicted probabilities and actual outcome probabilities. Calibration has been evaluated in the past by grouping probability forecasts into discrete categories. Here we show that we can do so without discrete groupings; the kernel estimators that we use produce efficiency gains and smooth estimated curves relating predicted and actual probabilities. We use such estimates to evaluate the empirical evidence on calibration error in a number of economic applications including recession and inflation prediction, using both forecasts made and stored in real time and pseudoforecasts made using the data vintage available at the forecast date. We evaluate outcomes using both first-release outcome measures as well as later, thoroughly-revised data. We find strong evidence of incorrect calibration in professional forecasts of recessions and inflation. We also present evidence of asymmetries in the performance of inflation forecasts based on real-time output gaps. Une prévision probabiliste représente la probabilité qu’un événement futur satisfasse une condition donnée. Un des aspects intéressants de ces prévisions est leur calibration, c’est-à-dire l’appariement entre les probabilités prédites et les probabilités réalisées. Dans le passé, la calibration a été évaluée en regroupant des probabilités de prévisions en catégories distinctes. Nous proposons d’utiliser des estimateurs à noyaux, qui sont plus efficaces et qui estiment une relation lisse entre les probabilités prédites et réalisées. Nous nous servons de ces estimations pour évaluer l’importance empirique des erreurs de calibration dans plusieurs pratiques économiques, telles que la prévision de récessions et de l’inflation. Pour ce faire, nous utilisons des prévisions historiques, ainsi que des pseudoprévisions effectuées à l’aide de données telles qu’elles étaient au moment de la prévision. Nous analysons les résultats en utilisant autant des estimations préliminaires que des estimations tardives, ces dernières incorporant parfois des révisions importantes. Nous trouvons une forte évidence empirique d’une calibration erronée des prévisions professionnelles de récession et d’inflation. Nous présentons aussi une évidence d’asymétries dans la performance des prévisions d’inflation basées sur des estimations des écarts de la production en temps réel.calibration, probability forecast, real-time data, inflation, recession, calibration, probabilités de prévisions, données « en temps réel », inflation, récession

The Unreliability of Output Gap Estimates in Real Time

We examine the reliability of alternative output detrending methods, with special attention to the accuracy of real-time estimates of the output gap. We show that ex post revisions of the estimated gap are of the same order of magnitude as the estimated gap itself and that these revisions are highly persistent. Although important, the revision of published data is not the primary source of revisions in measured output gaps; the bulk of the problem is due to the pervasive unreliability of end-of-sample estimates of the trend in output. Multivariate methods that incorporate information from inflation to estimate the output gap are not more reliable than their univariate counterparts. Nous examinons la fiabilité de plusieurs méthodes qui sont utilisés pour rendre des séries chronologiques stationnaires, en portant une attention particulière à la précision des estimations en temps réel de l'écart de la production. Nous montrons que de la taille des révisions ex post de nos estimations de l'écart et celle des estimations faites en temps réels sont du même ordre de grandeur et que ces révisions sont fortement persistantes. Même si elle est importante, la révision des données n'est pas la source principale des révisions des estimations. La majorité de ce problème est due à la forte imprécision des estimations des tendances actuelles de la production. Des techniques multivariés, qui exploitent aussi le taux d'inflation pour estimer l'écart de la production, ne sont pas plus précises que leurs équivalents univariés.Real-time data, output gap, business cycle measurement, données en temps réels, l'écart de la production, l'estimation du cycle d'affaire

Filtres pour l’analyse courante

L’auteur montre comment les techniques actuelles de filtrage passe-bande et leurs prolongements peuvent servir à estimer des tendances et des cycles courants. Ces techniques donnent des estimations jugées « optimales » compte tenu des données disponibles. Les erreurs types s’y rattachant représentent donc la borne inférieure de la marge d’erreur qui serait associée aux résultats produits par d’autres techniques univariées. Dans cette étude, l’auteur examine les applications de ce filtre aux problèmes que pose l’estimation de la croissance de la productivité, de l’inflation de base et de l’écart de production observés.This paper shows how existing band-pass filtering techniques and their extension can be applied to the common current-analysis problem of estimating current trends or cycles. These techniques give estimates that are “optimal” given the available data, so their standard errors represent a lower bound on what can be achieved with other univariate techniques. Applications to the problems of estimating current trend productivity growth, core inflation, and output gaps are considered

Lessons from the latest data on U.S. productivity

Productivity growth is carefully scrutinized by macroeconomists because it plays key roles in understanding private savings behaviour, the sources of macroeconomic shocks, the evolution of international competitiveness and the solvency of public pension systems, among other things. However, estimates of recent and expected productivity growth rates suffer from two potential problems: (i) recent estimates of growth trends are imprecise, and (ii) recently published data often undergo important revisions. This paper documents the statistical (un)reliability of several measures of aggregate productivity growth in the U.S. by examining the extent to which they are revised over time. The authors also examine the extent to which such revisions contribute to errors in forecasts of U.S. productivity growth. The authors find that data revisions typically cause appreciable changes in published estimates of productivity growth rates across a range of different productivity measures. Substantial revisions often occur years after the initial data release, which they argue contributes significantly to the overall uncertainty policymakers face. This emphasizes the need for means of reducing the uncertainty facing policymakers and policies robust to uncertainty about current economic conditions.>Productivity - United States ; Statistics ; Real-time data

Exchange Rates and Order Flow in the Long Run

Several recent papers have underlined the importance of the microstructure effects in understanding exchange rate behavior by documenting stable long-run relationships between cumulated order flows and spot exchange rates. This stands in contrast to the widely-studied failure of exchange rates to conform to the long-run behavior implied by “conventional” macroeconomic models and is consistent with the prediction of micro-structure models. We reexamine the evidence for stable long-run relationships. We find that such evidence exists only for a small number of the major currencies we examine and that is it statistically fragile. We conclude that this implication of microstructure models does not fit the data as well as previous studies suggest. Plusieurs études récentes ont souligné l’importance de la microstructure des marchés pour la compréhension des comportements des taux de change en documentant les relations stables à long terme entre les flux des commandes cumulées et les taux de change courants. Les résultats contrastent avec ceux de nombreuses études sur l’échec des taux de change de se conformer au comportement à long terme que supposent les modèles macroéconomiques « conventionnels » et sont conformes à la prédiction des modèles microstructurels. Nous réexaminons l’évidence de relations stables à long terme et constatons que celle-ci n’existe que dans un petit nombre des taux de change étudiés et qu’elle est fragile du point de vue statistique. Nous concluons que l’implication des modèles microstructurels ne correspond pas aux données aussi bien que des études précédentes laissent supposer.cointegration, foreign exchange rates, order flow, microstructure, cointégration, flux de commandes, microstructure, taux de change

The reliability of Canadian output gap estimates

Recent work on policy rules under uncertainty have highlighted the impact of output gap measurement errors on economic outcomes and their importance in the formulation of appropriate policy rules. This paper investigates the reliability of current estimates of the output gap in Canada. We begin by assembling a new data base of quarterly realtime output estimates which spans the post-WWII period and contains data vintages dating back to 1972. We use this with a broad range of univariate and multivariate output gap models to recreate ?contemporary? estimates of the output gap and then study how these estimates are revised over time. The nature and sources of these revisions are used to draw conclusions about the overall measurement errors associated with current estimates of the output gap. Relative to similar recent work with US realtime data, we find that revisions in Canadian output gaps are more important and that the role of data revision is less innocuous than previously indicated. We also show that using the change rather than the level of the output gap may only modestly reduce the measurement problem, and we investigate the relative importance of model uncertainty to overall measurement uncertainty. --output gap,business cycle,real-time data,policy rules,monetary policy,Canada

Testing Optimal Punishment Mechanisms under Price Regulation: the Case of the Retail Market for Gasoline

We analyse the effects of a price floor on price wars (or deep price cuts) in the retail market for gasoline. Bertrand supergame oligopoly models predict that price wars should last longer in the presence of price floors. In 1996, the introduction of a price floor in the Quebec retail market for gasoline serves as a natural experiment with which to test this prediction. We use a Markov Switching Model with two latent states to simultaneously identify the periods of price-collusion/price-war and estimate the parameters characterizing each state. Results support the prediction that price floors reduce the intensity of price wars but increase their expected duration.price regulation, oligopoly supergame, Markov switching model, gasoline