1,974 research outputs found
Anthropocentric Realism about Values
31 The choice of human goals cannot be completely subjective, because
32 there are some (even ones that motivate many humans) that are simply
33 unintelligible as ultimate goals. For example, wealth is rational as an
34 intermediate goal, a means to achieving some further end, but it is simply
35 unintelligible to suggest that wealth is an ultimate goal in itself. Second,
36 we have seen that some things are reasonable to pursue as aspects of
37 our ultimate goals (like prestige and pleasure), but they are conceptu-
38 ally dependent on some other goal to give them concrete form. In this
39 essay, I argue in favor of six candidates for ultimate goal
Review of Boston Confucianism: Portable Tradition in the Late-Modern World by Robert Cummings Neville
The dynamical U(n) quantum group
We study the dynamical analogue of the matrix algebra M(n), constructed from
a dynamical R-matrix given by Etingof and Varchenko. A left and a right
corepresentation of this algebra, which can be seen as analogues of the
exterior algebra representation, are defined and this defines dynamical quantum
minor determinants as the matrix elements of these corepresentations. These
elements are studied in more detail, especially the action of the
comultiplication and Laplace expansions. Using the Laplace expansions we can
prove that the dynamical quantum determinant is almost central, and adjoining
an inverse the antipode can be defined. This results in the dynamical GL(n)
quantum group associated to the dynamical R-matrix. We study a *-structure
leading to the dynamical U(n) quantum group, and we obtain results for the
canonical pairing arising from the R-matrix.Comment: 24 page
Calibration and Resolution Diagnostics for Bank of England Density Forecasts
This paper applies new diagnostics to the Bank of Englandâs 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 Ă©tude dĂ©veloppe et applique des nouvelles techniques pour diagnostiquer les prĂ©visions de densitĂ© de la Banque dâAngleterre (leur âfan chartsâ). Nous calculons leurs probabilitĂ©s implicites pour des taux dâinflation et de croissance du PIB qui dĂ©passent des seuils critiques (soit le taux dâinflation ciblĂ©, soit 2.5%.) En contraste avec des travaux antĂ©rieurs sur ces prĂ©visions, nous gaugeons leur calibration aussi bien que leur rĂ©solution, en donnant des tests formels et des interprĂ©tations graphiques. Les rĂ©sultats renforcent des conclusions dĂ©jĂ existant sur les limites de ces prĂ©visions et ils donnent de nouvelles Ă©vidences sur leurs valeurs ajoutĂ©es.calibration, density forecast, probability forecast, resolu, calibration, prĂ©visions de densitĂ©, probabilitĂ©s implicites, rĂ©solution.
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 suggÚre 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 prĂ©vision probabiliste reprĂ©sente la probabilitĂ© quâun Ă©vĂ©nement futur satisfasse une condition donnĂ©e. Un des aspects intĂ©ressants de ces prĂ©visions est leur calibration, câest-Ă -dire lâappariement entre les probabilitĂ©s prĂ©dites et les probabilitĂ©s rĂ©alisĂ©es. Dans le passĂ©, la calibration a Ă©tĂ© Ă©valuĂ©e en regroupant des probabilitĂ©s de prĂ©visions en catĂ©gories distinctes. Nous proposons dâutiliser des estimateurs Ă noyaux, qui sont plus efficaces et qui estiment une relation lisse entre les probabilitĂ©s prĂ©dites et rĂ©alisĂ©es. Nous nous servons de ces estimations pour Ă©valuer lâimportance empirique des erreurs de calibration dans plusieurs pratiques Ă©conomiques, telles que la prĂ©vision de rĂ©cessions et de lâinflation. Pour ce faire, nous utilisons des prĂ©visions historiques, ainsi que des pseudoprĂ©visions effectuĂ©es Ă lâaide de donnĂ©es telles quâelles Ă©taient au moment de la prĂ©vision. Nous analysons les rĂ©sultats en utilisant autant des estimations prĂ©liminaires que des estimations tardives, ces derniĂšres incorporant parfois des rĂ©visions importantes. Nous trouvons une forte Ă©vidence empirique dâune calibration erronĂ©e des prĂ©visions professionnelles de rĂ©cession et dâinflation. Nous prĂ©sentons aussi une Ă©vidence dâasymĂ©tries dans la performance des prĂ©visions dâinflation basĂ©es sur des estimations des Ă©carts de la production en temps rĂ©el.calibration, probability forecast, real-time data, inflation, recession, calibration, probabilitĂ©s de prĂ©visions, donnĂ©es « en temps rĂ©el », inflation, rĂ©cession
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 fiabilitĂ© de plusieurs mĂ©thodes qui sont utilisĂ©s pour rendre des sĂ©ries chronologiques stationnaires, en portant une attention particuliĂšre Ă la prĂ©cision des estimations en temps rĂ©el de l'Ă©cart de la production. Nous montrons que de la taille des rĂ©visions ex post de nos estimations de l'Ă©cart et celle des estimations faites en temps rĂ©els sont du mĂȘme ordre de grandeur et que ces rĂ©visions sont fortement persistantes. MĂȘme si elle est importante, la rĂ©vision des donnĂ©es n'est pas la source principale des rĂ©visions des estimations. La majoritĂ© de ce problĂšme est due Ă la forte imprĂ©cision des estimations des tendances actuelles de la production. Des techniques multivariĂ©s, qui exploitent aussi le taux d'inflation pour estimer l'Ă©cart de la production, ne sont pas plus prĂ©cises que leurs Ă©quivalents univariĂ©s.Real-time data, output gap, business cycle measurement, donnĂ©es en temps rĂ©els, l'Ă©cart de la production, l'estimation du cycle d'affaire
Elliptic U(2) quantum group and elliptic hypergeometric series
We investigate an elliptic quantum group introduced by Felder and Varchenko,
which is constructed from the -matrix of the Andrews-Baxter-Forrester model,
containing both spectral and dynamical parameter. We explicitly compute the
matrix elements of certain corepresentations and obtain orthogonality relations
for these elements. Using dynamical representations these orthogonality
relations give discrete bi-orthogonality relations for terminating
very-well-poised balanced elliptic hypergeometric series, previously obtained
by Frenkel and Turaev and by Spiridonov and Zhedanov in different contexts.Comment: 20 page
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