Ensembles are today routinely applied to estimate uncertainty in numerical predictions of complex systems such as the weather. Instead of initializing a single numerical forecast, using only the best guess of the present state as initial conditions, a collection (an ensemble) of forecasts whose members start from slightly different initial conditions is calculated. By varying the initial conditions within their error bars, the sensitivity of the resulting forecasts to these measurement errors can be accounted for. The ensemble approach can also be applied to estimate forecast errors that are due to insufficiently known model parameters by varying these parameters between ensemble members. An important (and difficult) question in ensemble weather forecasting is how well does an ensemble of forecasts reproduce the actual forecast uncertainty. A widely used criterion to assess the quality of forecast ensembles is statistical consistency which demands that the ensemble members and the corresponding measurement (the ``verification\'\') behave like random independent draws from the same underlying probability distribution. Since this forecast distribution is generally unknown, such an analysis is nontrivial. An established criterion to assess statistical consistency of a historical archive of scalar ensembles and verifications is uniformity of the verification rank: If the verification falls between the (k-1)-st and k-th largest ensemble member it is said to have rank k. Statistical consistency implies that the average frequency of occurrence should be the same for each rank. A central result of the present thesis is that, in a statistically consistent K-member ensemble, the (K+1)-dimensional vector of rank probabilities is a random vector that is uniformly distributed on the K-dimensional probability simplex. This behavior is universal for all possible forecast distributions. It thus provides a way to describe forecast ensembles in a nonparametric way, without making any assumptions about the statistical behavior of the ensemble data. The physical details of the forecast model are eliminated, and the notion of statistical consistency is captured in an elementary way. Two applications of this result to ensemble analysis are presented. Ensemble stratification, the partitioning of an archive of ensemble forecasts into subsets using a discriminating criterion, is considered in the light of the above result. It is shown that certain stratification criteria can make the individual subsets of ensembles appear statistically inconsistent, even though the unstratified ensemble is statistically consistent. This effect is explained by considering statistical fluctuations of rank probabilities. A new hypothesis test is developed to assess statistical consistency of stratified ensembles while taking these potentially misleading stratification effects into account. The distribution of rank probabilities is further used to study the predictability of outliers, which are defined as events where the verification falls outside the range of the ensemble, being either smaller than the smallest, or larger than the largest ensemble member. It is shown that these events are better predictable than by a naive benchmark prediction, which unconditionally issues the average outlier frequency of 2/(K+1) as a forecast. Predictability of outlier events, quantified in terms of probabilistic skill scores and receiver operating characteristics (ROC), is shown to be universal in a hypothetical forecast ensemble. An empirical study shows that in an operational temperature forecast ensemble, outliers are likewise predictable, and that the corresponding predictability measures agree with the analytically calculated ones.

No description available.

Ensembles are today routinely applied to estimate uncertainty in numerical predictions of complex systems such as the weather. Instead of initializing a single numerical forecast, using only the best guess of the present state as initial conditions, a collection (an ensemble) of forecasts whose members start from slightly different initial conditions is calculated. By varying the initial conditions within their error bars, the sensitivity of the resulting forecasts to these measurement errors can be accounted for. The ensemble approach can also be applied to estimate forecast errors that are due to insufficiently known model parameters by varying these parameters between ensemble members. An important (and difficult) question in ensemble weather forecasting is how well does an ensemble of forecasts reproduce the actual forecast uncertainty. A widely used criterion to assess the quality of forecast ensembles is statistical consistency which demands that the ensemble members and the corresponding measurement (the ``verification\'\') behave like random independent draws from the same underlying probability distribution. Since this forecast distribution is generally unknown, such an analysis is nontrivial. An established criterion to assess statistical consistency of a historical archive of scalar ensembles and verifications is uniformity of the verification rank: If the verification falls between the (k-1)-st and k-th largest ensemble member it is said to have rank k. Statistical consistency implies that the average frequency of occurrence should be the same for each rank. A central result of the present thesis is that, in a statistically consistent K-member ensemble, the (K+1)-dimensional vector of rank probabilities is a random vector that is uniformly distributed on the K-dimensional probability simplex. This behavior is universal for all possible forecast distributions. It thus provides a way to describe forecast ensembles in a nonparametric way, without making any assumptions about the statistical behavior of the ensemble data. The physical details of the forecast model are eliminated, and the notion of statistical consistency is captured in an elementary way. Two applications of this result to ensemble analysis are presented. Ensemble stratification, the partitioning of an archive of ensemble forecasts into subsets using a discriminating criterion, is considered in the light of the above result. It is shown that certain stratification criteria can make the individual subsets of ensembles appear statistically inconsistent, even though the unstratified ensemble is statistically consistent. This effect is explained by considering statistical fluctuations of rank probabilities. A new hypothesis test is developed to assess statistical consistency of stratified ensembles while taking these potentially misleading stratification effects into account. The distribution of rank probabilities is further used to study the predictability of outliers, which are defined as events where the verification falls outside the range of the ensemble, being either smaller than the smallest, or larger than the largest ensemble member. It is shown that these events are better predictable than by a naive benchmark prediction, which unconditionally issues the average outlier frequency of 2/(K+1) as a forecast. Predictability of outlier events, quantified in terms of probabilistic skill scores and receiver operating characteristics (ROC), is shown to be universal in a hypothetical forecast ensemble. An empirical study shows that in an operational temperature forecast ensemble, outliers are likewise predictable, and that the corresponding predictability measures agree with the analytically calculated ones.

[pt] O presente trabalho investiga as potenciais explicações para o fenômeno do Missing Disinflation Puzzle. Nós montamos uma base de dados contendo apenas variáveis associadas com o fenômeno, e utilizamos métodos de Machine Learning para calcular estimativas para a inflação do Consumer Price Index durante o período de interesse. Esses métodos podem lidar com bases de dados extensas, e realizar seleção de variáveis. Um exercício de seleção de melhores modelos utilizando a técnica de Model Confidence Set sobre previsões pseudo out-of-sample é proposto. Nós analisamos o padrão de seleção de variáveis entre os melhores modelos selecionados e encontramos evidência a favor das explicações associadas ao uso de diferentes métricas de expectativas de inflação - em especial aquelas ligadas a pesquisas feitas com consumidores. / [en] This paper examines the potential explanations for the Missing Disinflation Puzzle (MDP). We construct a data set containing only variables associated with the puzzle, and use of Machine Learning (ML) methods to compute estimates for U.S. Consumer Price Index inflation over the period of interest. These methods can handle large data sets, and perform variable selection. A model selection exercise using Model Confidence Set over pseudo-out-of-sample forecasts is proposed to assess forecasting performance and to analyze the variable selection pattern of these models. We analyze the variable selection performed by the best models and find evidence for explanations associated with different metrics for inflation expectations - in particular those linked to consumers surveys.

No description available.

There are many different types of non-alcoholic beverages (NAB) available in the United States today compared to a decade ago. Additionally, the needs of beverage consumers have evolved over the years centering attention on functionality and health dimensions. These trends in volume of consumption are a testament to the growth in the NAB industry. Our study pertains to ten NAB categories. We developed and employed a unique cross-sectional and time-series data set based on Nielsen Homescan data associated with household purchases of NAB from 1998 through 2003. First, we considered demographic and economic profiling of the consumption of NAB in a two-stage model. Race, region, age and presence of children and gender of household head were the most important factors affecting the choice and level of consumption. Second, we used expectation-prediction success tables, calibration, resolution, the Brier score and the Yates partition of the Brier score to measure the accuracy of predictions generated from qualitative choice models used to model the purchase decision of NAB by U.S. households. The Yates partition of the Brier score outperformed all other measures. Third, we modeled demand interrelationships, dynamics and habits of NAB consumption estimating own-price, cross-price and expenditure elasticities. The Quadratic Almost Ideal Demand System, the synthetic Barten model and the State Adjustment Model were used. Soft drinks were substitutes and fruit juices were complements for most of non-alcoholic beverages. Investigation of a proposed tax on sugar-sweetened beverages revealed the importance of centering attention not only to direct effects but also to indirect effects of taxes on beverage consumption. Finally, we investigated factors affecting nutritional contributions derived from consumption of NAB. Also, we ascertained the impact of the USDA year 2000 Dietary Guidelines for Americans associated with the consumption of NAB. Significant factors affecting caloric and nutrient intake from NAB were price, employment status of household head, region, race, presence of children and the gender of household food manager. Furthermore, we found that USDA nutrition intervention program was successful in reducing caloric and caffeine intake from consumption of NAB. The away-from-home intake of beverages and potential impacts of NAB advertising are not captured in our work. In future work, we plan to address these limitations.

This dissertation contains three essays in international macroeconomics and financial time series forecasting. In the first essay, I show, numerically, that a two-country New-Keynesian Sticky Prices model, driven by monetary and productivity shocks, is capable of explaining the highly positive correlation across the industrialized countries' inflation even though their cross-country correlation in money growth rate is negligible. The structure of this model generates cross-country correlations of inflation, output and consumption that appear to closely correspond to the data. Additionally, this model can explain the internal correlation between inflation and output observed in the data. The second essay presents two important results. First, gains from monetary policy cooperation are different from zero when the elasticity of substitution between domestic and imported goods consumption is different from one. Second, when monetary policy is endogenous in a two-country model, the only Nash equilibria supported by this model are those that are symmetrical. That is, all exporting firms in both countries choose to price in their own currency, or all exporting firms in both countries choose to price in the importer's currency. The last essay provides both conditional and unconditional predictive ability evaluations of the aluminum futures contracts prices, by using five different econometric models, in forecasting the aluminum spot price monthly return 3, 15, and 27-months ahead for the sample period 1989.01-2010.10. From these evaluations, the best model in forecasting the aluminum spot price monthly return 3 and 15 months ahead is followed by a (VAR) model whose variables are aluminum futures contracts price, aluminum spot price and risk free interest rate, whereas for the aluminum spot price monthly return 27 months ahead is a single equation model in which the aluminum spot price today is explained by the aluminum futures price 27 months earlier. Finally, it shows that iterated multiperiod-ahead time series forecasts have a better conditional out-of-sample forecasting performance of the aluminum spot price monthly return when an estimated (VAR) model is used as a forecasting tool.

Wirtschaftsprognosen sollen die Unsicherheit bezüglich der zukünftigen wirtschaftlichen Entwicklung mindern und Planungsprozesse von Regierungen und Unternehmen unterstützen. Empirische Studien bescheinigen ihnen jedoch in aller Regel ein unbefriedigendes Qualitätsniveau. Auf der Suche nach den Ursachen hat sich in Form der rationalen Erwartungsbildung eine zentrale Grundforderung an  die Prognostiker herausgebildet. So müssten offensichtliche und systematische Fehler, wie bspw. regelmäßige Überschätzungen, mit der Zeit erkannt und abgestellt werden. Die erste Studie der Dissertation übt Kritik am vorherrschenden Verständnis der Rationalität. Dieses ist zu weitreichend, weshalb den Prognostikern die Rationalität voreilig abgesprochen wird. Anhand einer neuen empirischen Herangehensweise wird deutlich, dass die Prognosen aus einem anderen Blickwinkel heraus durchaus als rational angesehen werden können. Der zweite Aufsatz zeigt auf, dass in Form von Befragungsergebnissen öffentlich verfügbare Informationen bestehen, die bei geeigneter Verwendung zu einer Verbesserung der Qualität von Konjunkturprognosen beitragen würden. Die Rationalität dieser Prognosen ist daher stark eingeschränkt. Im dritten Papier erfolgt eine Analyse von Prognoserevisionen und deren Ursachen. Dabei zeigt sich, dass es keinen Zusammenhang zwischen der Rationalität und der Qualität der untersuchten Prognosezeitreihen gibt. Die vierte Studie dient der Präsentation der Ergebnisse eines Prognoseplanspiels, welches den Vergleich der Prognosen von Amateuren und Experten zum Ziel hatte. Es stellt sich heraus, dass die Prognosefehler erhebliche Übereinstimmungen aufweisen.