Submitted by Geandra Rodrigues (geandrar@gmail.com) on 2018-10-24T12:50:06Z No. of bitstreams: 1 taisdemedeirospedreira.pdf: 1862768 bytes, checksum: 0c6ee31fd9be772b5b609051a207f61f (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2018-11-23T12:17:19Z (GMT) No. of bitstreams: 1 taisdemedeirospedreira.pdf: 1862768 bytes, checksum: 0c6ee31fd9be772b5b609051a207f61f (MD5) / Made available in DSpace on 2018-11-23T12:17:19Z (GMT). No. of bitstreams: 1 taisdemedeirospedreira.pdf: 1862768 bytes, checksum: 0c6ee31fd9be772b5b609051a207f61f (MD5) Previous issue date: 2018-09-05 / As previsões a curto prazo da carga elétrica (de algumas horas até alguns dias à frente) são essenciais para o planejamento, controle e operação dos sistemas de energia, tanto por por razões técnicas quanto financeiras. Como não é possível estocar grandes quantidades, torna-se indispensável um maneira eficaz de programar a produção da energia para que ela atenda a demanda. Por conta disso, uma grande literatura desenvolveu-se sobre o assunto. Devido à complexidade das séries de carga e à dependência não-linear destas carga em relação a diversas variáveis exógenas, os sistemas de previsão mais frequentemente propostos em trabalhos recentes são aqueles baseados em algoritmos complexos de inteligência computacional. No entanto, métodos lineares simples ainda são muito comumente usados, por si sós ou em combinação com técnicas não-lineares. Um desses métodos é o de Holt-Winters-Taylor, que é uma adaptação do conhecido método de amortecimento exponencial de Holt-Winters para que múltiplas sazonalidades possam ser modelados concomitantemente. Este trabalho implementa três variantes deste método HWT e analisa seus desempenhos em duas séries de dados reais de carga. Verificou-se que uma combinação linear dessas variantes nitidamente supera o método HWT original e fornece previsões precisas, com um baixo custo computacional. / Short-term load forecasts (forecasts for horizons ranging from a few hours to a few days ahead) are essential for the planning, controling and operation of energy systems, both for technical and financial reasons. Since it is not feasible to store energy in large quantities, an efficient way to forecast energy demand becomes indispensable. Because of this, a large literature has developed on the subject. Due to the complexity of load series and the nonlinear relationship of the load with exogenous variables, the most frequently proposed forecasting systems in recent papers are those based on complex algorithms of computational intelligence. However, simple linear methods are still very frequently used, either alone or in combination with non-linear techniques. One of these methods is Holt-Winters-Taylor (HWT), which is an adaptation of the well-known Holt-Winters exponential smoothing method, modified so that multiple seasonalities can be modeled at the same time. In this paper, we implement three variants of this HWT method and analyze their performances over two sets of actual load data. We found that a linear combination of these variants clearly outperforms the original HWT method, and provides accurate forecasts at a low computational cost.

D.Litt. et Phil. (Geography) / The aim of this study is to integrate LANDSAT multispectral digital data with agricultural statistics, to analyse, explain and forecast the spatial variation of crop production in the Vermaas development area (south of Lichtenburg, Western Transvaal). This aim answers the urgent need for a reliable agricultural data base that can be quickly and cheaply obtained and used for the timely planning of an environment's limited agricultural resources. With such a data base available, early decisions about imports and exports can be taken in connection with the expected agricultural commodities of an area: the year-to-year fluctuation in crop yields is still the main problem in relation to the overall planning of agricultural food production. The study has been conducted according to two main analytical phases, i.e. (i) the interpretation of the data, which in turn was subdivided into: - the cartographic-analytical evaluation of the agricultural information, and - the recognition of rural land-use patterns from LANDSAT digital data. (i i) the integration process. The LANDSAT land-use information was integrated with the observed agricultural statistics with the aid of two integration models: an empirical and an operational model. The data for the research consisted of the multispectral digital data of LANDSAT-l and available agricultural statistics. The LANDSAT data was acquired from the Satellite Remote Sensing Centre at Hartbeeshoek, while the agricultural data was obtained from the Department of Agriculture (Highveld Region) and other official soures. These analytical phases were conducted at the computer centres of the CSIR and RAU. Existing computer programme packages were used - the VICAR system for pattern recognition, and the BMD and SYMAP systems for the analytical evaluation of the agricultural information and for the implementation of the integration models. The following results were obtained: 3.1 The integration of the LANDSAT information with the agricultural statistics was reasonably successful. The success of any study of this nature can be ascertained from the accuracy with which the necessary information is derived from the LANDSAT multispectral digital data. 3.2 This analysis highl ighted the cultivated area as a major factor for consideration. The type of crop and the area covered by it are the two most important sets of information that can be obtained from the LANDSAT data and used in an integration model. 3.3 The results (predicted crop yields) that were obtained from the integration process could probably be improved, if the detrimental influence of collinearity, which existed between some of the agricultural variables, was el iminated. 3.4 The identification of different crops from the LANDSAT digital data was not possible - a fact which can be attributed to the lack of a crop calendar for this farming area. Besides the above-mentioned results, the following can also be listed: 4.1 The spatial variation In maize production was well analysed in terms of the integration results, In spite of the fact that the accuracy of the agricultural statistics was, in certain cases, questionable. 4.2 The important influence of time upon the spatial variation in crop production could not be implicated, because of the one point in time consideration of this study. 4.3 Only the agricultural variables that were directly related to farm area could be used as input data for this study. 4.4 The potential usefulness of the LANDSAT digital data as geographical information is mainly determined by its quality (cloudcover, resolution, etc.). 4.5 The application of multispectral digital data depends on certain specific techniques, with which the researcher must acquaint himself for a successful and useful interpretation of the digital data.

Luftverkehrsprognosen stellen ein wichtiges Instrument dar, die Luftverkehrsinfrastruktur zu beeinflussen. Hinter vielen der Projekte, die von Luftverkehrsprognosen begleitet werden, stehen Interessen. Dies gilt insbesondere für Ausbauvorhaben von Flughäfen, die von eindeutigen Zielen und Wünschen getragen werden. Die Gutachter, die im Rahmen solcher Ausbauvorhaben tätig werden, sind der Gefahr ausgesetzt, beeinflusste Prognosen zu erstellen. Es ist Ziel der folgenden Arbeit, die Qualität von Luftverkehrsprognosen in Deutschland empirisch zu überprüfen. Dabei stößt man auf das Problem, dass es für Luftverkehrsprognosen praktisch nur einen Gutachter gibt. Dies ist die Intraplan Consult GmbH. Prognosen anderer Gutachter liegen in so geringer Zahl vor, dass sie nicht empirisch auswertbar sind. Wir beschränken uns im Folgenden deshalb darauf, die Prognosen von Intraplan Consult GmbH zu analysieren. Wir glauben, dass die Untersuchungsergebnisse allgemeingültigen Charakter haben und auf andere Luftverkehrsprognosen übertragbar sind. Es werden drei Fragestellungen untersucht. • Überzeichnen die Luftverkehrsprognosen der Intraplan Consult GmbH die tatsächliche Verkehrsentwicklung? • Gibt es Hinweise darauf, dass Überzeichnungen bei bestimmten Prognosegrößen (z.B. PAX, Fracht, Flugbewegungen) besonders häufig vorkommen? • Gibt es Hinweise darauf, dass Überzeichnungen bei bestimmten Gutachtentypen bzw. Gutachtenanlässen (z.B. Erweiterungen, behördliche Genehmigungen) besonders häufig vorkommen? Die empirische Untersuchung zeigte, dass die Prognosen insgesamt betrachtet tendenziell die tatsächliche Verkehrsentwicklung überschätzen. Es konnte zudem ermittelt werden, dass die Qualität der Ergebnisse stark streut und sich nicht normalverteilt verhält. Weiterhin konnten Wendepunktefehler im Prognose-Realisierungs-Diagramm beobachtet werden. Das bedeutet, dass das Modell von Intraplan Wendepunkte von Entwicklungen nicht erkennen kann. Die Airlines haben in den von den Prognosen erfassten Zeiträumen Strategiewechsel vorgenommen, die von Intraplan nicht erkannt wurden. Im weiteren Verlauf wurden die drei Prognosegrößen (i) Passagiere, (ii) Fracht- und Postaufkommen sowie (iii) Flugbewegungen einzeln analysiert. Alle drei Größen zeigten systematisch überschätzende Prognosen. Besonders stark ist dies bei den Flugbewegungen zu erkennen. Die Prognosen wurden nach ihrem Zweck in die Kategorien „Planfälle bei Genehmigungsgutachten“, „Worst-Case-Fälle bei Genehmigungsgutachten“ sowie „Marktstudien“ getrennt. Alle drei Kategorien weisen systematische Fehlschätzungen auf. Bei den Marktstudien liegen systematische Überschätzungen vor. Sie sind tendenziell etwas geringer als bei Planfallprognosen, wo die Überschätzungen erhebliche Größenordnungen annehmen (20-50% zu hohe Wachstumsraten). Worst-Case-Prognosen unterschätzen die tatsächliche Entwicklung systematisch. Da die beiden letzteren Kategorien (also Planfälle vs. Worst-Case-Fälle) im Zusammenhang mit Ausbauvorhaben an Flughäfen stehen, liegt die Vermutung nahe, dass im Rahmen der Genehmigungsgutachten die Worst-Case-Prognosen gezielt zu niedrig angesetzt werden (Unterschätzung), während die Planfallprognosen zu hoch angesetzt werden (Überschätzung), um das jeweilige Ausbauvorhaben in einem bedeutenderen Licht erscheinen zu lassen. Die Wendepunktfehler zeigen, dass Intraplan Strategiewechsel der Luftverkehrsunternehmen nicht richtig prognostiziert. Dies kann im Zusammenhang mit dem von den Auftraggebern verfolgten Zweck der Projekte stehen: Andere als die von den Auftraggebern gewünschten Strategien werden nicht abgebildet. Dieses sind, wie die empirischen ex post Ergebnisse zeigen, weder alle denkbaren Strategien und nicht einmal die wahrscheinlichsten Strategien. In der Luftverkehrswirtschaft wird argumentiert, dass die Prognosen von Intraplan nur wegen der Finanzkrise 2008, die nicht vorhersehbar gewesen wäre, so schlecht seien. Unsere Analyse zeigt für einige Flughäfen tatsächlich deutliche Brüche der Entwicklung nach der Finanzkrise. Bei anderen Flughäfen ist demgegenüber eine Konstanz der Entwicklung vor und nach der Finanzkrise festzustellen. Auch bei diesen Flughäfen ist aber die Prognose schlecht. Darüber hinaus gibt es Flughäfen, bei denen Airlines Strategiewechsel schon vor der Finanzkrise vorgenommen haben. Auch bei diesen hat das Prognosemodell von Intraplan schlechte Resultate erzielt. Bei anderen Flughäfen dagegen, ist die Prognoseleistung trotz Finanzkrise gut. Alles in allem zeigt sich deshalb, dass es weniger die Finanzkrise von 2008 ist, die zu Fehlprognosen führt, als die fehlende Fähigkeit, die Strategien der Airlines richtig abzubilden. / Aviation traffic forecasts and airport analyses are important instruments which influence decisions on aviation related infrastructure. Behind many of such infrastructure projects, which are supported by forecast analyses, one finds political interests. This is especially the case for aviation projects, such as infrastructure enlargement projects of airports, which are motivated by distinct goals and desires. Referees who act within this framework are exposed to the risk of producing biased results. The quality of such aviation traffic forecasts is the subject of this working paper. To begin with, one major obstacle is that there is only one such referee available in Germany - Intraplan Consult GmbH. Other referees have only a minimal, insignificant share of output, which cannot be used for any further empirical investigation. This is the reason why the present working paper focuses on airport analyses produced by Intraplan Consult GmbH. We believe that these research results are of common character, and thus are transferable to other related airport studies. The following three research questions have been investigated: • Do recent aviation traffic forecasts performed by Intraplan Consult GmbH overestimate actual traffic developments? • Are there any indications that some specific forecast measures (such as PAX, freight and cargo, flight movement) might be overestimated exceptionally frequently? • Are there any indications that specific types and events of forecast analysis (such as airport expansion projects, official authorisation of a project by the public administration) overestimated exceptionally frequently? Empirical investigation has shown that forecast analyses tend to be indeed overestimated, compared to the actual traffic developments. It could be shown that the quality of the research results is widespread and not normally distributed. Furthermore, systematic errors with regard to the point of inflection in forecast-realisation-diagrams were identified. This means that models constructed by Intraplan could not identify points of inflection on the development path in the real world. For instance, airlines have undergone structure and strategic changes over the periods of investigation which could not be captured by Intraplan models. In addition, three forecast measures, namely the amount of (i) passenger, (ii) freight and post and the (iii) flight movements, have been analysed. All three of them have shown systematic overestimated forecast results. In particular, forecasts of flight movements have been largely overestimated. These forecast analyses have further been categorized according to their purpose, namely project cases for the issue of approval certificates, worst-case scenarios for the issue of approval certificates and market studies. All three categories show systematic misjudgements and miscalculations. Regarding the market studies, systematic overestimation was identified. It tends to be less severe than for the project cases, where extreme estimates of 20-50% higher growth rates were found. In contrast to this, worst-case scenarios systematically underestimated actual development trends. The last two of which suggest the assumption that, when analysing enlargement projects of airports, worst-case scenario forecast figures and project case forecast figures have been deliberately manipulated in order to put the expansion project in question into the right perspective. Misjudgement by Intraplan, regarding the points of inflection, shows that they cannot forecast any strategic development by the aviation industry flawlessly. However, it can be assumed that this may also be in the interest of their contractors. Others as those suggested strategies by their clients are not included in their models. To further strengthen this suggestion, all empirical ex post results show that only a small share of all possible strategies, and especially the most improbable strategies, had been used. The aviation industry argues that miscalculation by Intraplan only relates to the events around the financial crisis in 2008, which have been unpredictable. Our research results showed that few airports had indeed been hit, regarding their development, by the financial crisis in 2008 and after. Others, however, have shown no effect and continued their rather constant development before and after the financial crisis. Forecast analyses have also been inaccurate and false for these airports. In addition to this, some of these airports had already undergone a strategic change, airline strategic change, before the financial crisis. As well, with regard to these airports, the forecast model by Intraplan produced false results. On the other hand, some forecast results have been correct, despite the financial crisis. All in all, it can be argued that incorrect forecasts have not been caused as much by the financial crisis as by the missing capacities of the model to incorporate strategic change.

Les inondations sont un risque naturel majeur pour les biens et les personnes. Prévoir celles-ci, informer le grand public et les autorités sont de la responsabilité des services de prévision des crues. Pour ce faire ils disposent d'observations in situ et de modèles numériques. Néanmoins les modèles numériques sont une représentation simplifiée et donc entachée d'erreur de la réalité. Les observations quant à elle fournissent une information localisée et peuvent être également entachées d'erreur. Les méthodes d'assimilation de données consistent à combiner ces deux sources d'information et sont utilisées pour réduire l'incertitude sur la description de l'état hydraulique des cours d'eau et améliorer les prévisisons. Ces dernières décennies l'assimilation de données a été appliquée avec succès à l'hydraulique fluviale pour l'amélioration des modèles et pour la prévision des crues. Cependant le développement de méthodes d'assimilation pour la prévision en temps réel est contraint par le temps de calcul disponible et par la conception de la chaîne opérationnelle. Les méthodes en question doivent donc être performantes, simples à implémenter et peu coûteuses. Un autre défi réside dans la combinaison des modèles hydrauliques de dimensions différentes développés pour décrire les réseaux hydrauliques. Un modèle 1D est peu coûteux mais ne permet pas de décrire des écoulement complexes, contrairement à un modèle 2D. Le simple chainage des modèles 1D et 2D avec échange des conditions aux limites n'assure pas la continuité de l'état hydraulique. Il convient alors de coupler les modèles, tout en limitant le coût de calcul. Cette thèse a été financée par la région Midi-Pyrénées et le SCHAPI (Service Central d'Hydrométéorolgie et d'Appui à la Prévisions des Inondations) et a pour objectif d'étudier l'apport de l'assimilation de données et du couplage de modèles pour la prévision des crues. Elle se décompose en deux axes : Un axe sur l'assimilation de données. On s'intéresse à l'émulation du filtre de Kalman d'Ensemble (EnKF) sur le modèle d'onde de crue. On montre, sous certaines hypothèses, qu'on peut émuler l'EnKF avec un filtre de Kalman invariant pour un coût de calcul réduit. Dans un second temps nous nous intéressons à l'application de l'EnKF sur l'Adour maritime avec un modèle Saint-Venant. Nous en montrons les limitations dans sa version classique et montrons les avantages apportés par des méthodes complémentaires d'inflation et d'estimation des covariances d'erreur d'observation. L'apport de l'assimilation des données in situ de hauteurs d'eau sur des cas synthétiques et sur des crues réelles a été démontré et permet une correction spatialisée des hauteurs d'eau et des débits. En conséquence, on constate que les prévisions à court terme sont améliorées. Nous montrons enfin qu'un système de prévisions probabilistes sur l'Adour dépend de la connaissance que l'on a des forçages amonts ; un axe sur le couplage de modèles hydrauliques. Sur l'Adour 2 modèles co-existent : un modèle 1D et un modèle 2D au niveau de Bayonne. Deux méthodes de couplage ont été implémentées. Une première méthode, dite de "couplage à interfaces", combine le 1D décomposé en sous-modèles couplés au 2D au niveau frontières liquides de ce dernier. Une deuxième méthode superpose le 1D avec le 2D sur la zone de recouvrement ; le 1D force le 2D qui, quand il est en crue, calcule les termes d'apports latéraux pour le 1D, modélisant les échanges entre lit mineur et lit majeur. Le coût de calcul de la méthode par interfaces est significativement plus élevé que celui associé à la méthode de couplage par superposition, mais assure une meilleure continuité des variables. En revanche, la méthode de superposition est immédiatement compatible avec l'approche d'assimilation de données sur la zone 1D. / Floods represent a major threat for people and society. Flood forecasting agencies are in charge of floods forecasting, risk assessment and alert to governmental authorities and population. To do so, flood forecasting agencies rely on observations and numerical models. However numerical models and observations provide an incomplete and inexact description of reality as they suffer from various sources of uncertianties. Data assimilation methods consists in optimally combining observations with models in order to reduce both uncertainties in the models and in the observations, thus improving simulation and forecast. Over the last decades, the merits of data assimilation has been greatly demonstrated in the field of hydraulics and hydrology, partly in the context of model calibration or flood forecasting. Yet, the implementation of such methods for real application, under computational cost constraints as well as technical constraints remains a challenge. An other challenge arises when the combining multidimensional models developed over partial domains of catchment. For instance, 1D models describe the mono-dimensional flow in a river while 2D model locally describe more complex flows. Simply chaining 1D and 2D with boundary conditions exchange does not suffice to guarantee the coherence and the continuity of both water level and discharge variables between 1D and 2D domains. The solution lies in dynamical coupling of 1D and 2D models, yet an other challenge when computational cost must be limited. This PhD thesis was funded by Midi-Pyrénées region and the french national agency for flood forecasting SCHAPI. It aims at demonstrating the merits of data assimilation and coupling methods for floof forecasting in the framework of operational application. This thesis is composed of two parts : A first part dealing with data assimilation. It was shown that, under some simplifying assumptions, the Ensemble Kalman filter algorithm (EnKF) can be emulated with a cheaper algorithm : the invariant Kalman filter. The EnKF was then implemented ovr the "Adour maritime" hydraulic network on top of the MASCARET model describing the shallow water equations. It was found that a variance inflation algorithm can further improve data assimlation results with the EnKF. It was shown on synthetical and real cases experiments that data assimilation provides an hydraulic state that is in great agreement with water level observations. As a consequence of the sequential correction of the hydraulic state over time, the forecasts were also greatly improved by data assimilation over the entire hydraulic network for both assimilated and nonassimilated variables, especially for short term forecasts. It was also shown that a probabilistic prediction system relies on the knowledge on the upstream forcings ; A second part focusses on hydraulic models coupling. While the 1D model has a great spatial extension and describes the mono-dimensional flow, the 2D model gives a focus on the Adour-Nive confluence in the Bayonne area. Two coupling methods have been implemented in this study : a first one based on the exchange of the state variables at the liquid boundaries of the models and a second one where the models are superposed. While simple 1D or chained 1D-2D solutions provide an incomplete or discontinuous description of the hydraulic state, both coupling methods provide a full and dynamically coherent description of water level and discharge over the entire 1D-2D domain. On the one hand, the interface coupling method presents a much higher computational cost than the superposition methods but the continuity is better preserved. On the other hand, the superposition methods allows to combine data assimilation of the 1D model and 1D-2D coupling. The positive impact of water level in-situ observations in the 1D domain was illustrated over the 2D domain for a flood event in 2014.

Intégrée dans la chaîne décisionnelle de la Supply Chain à un niveau tactique, la Planification de Production est un process clé qui permet de répondre au mieux aux besoins selon les ressources de l’entreprise. Un des défis du domaine est la gestion des incertitudes prévisionnelles, ayant des conséquences importantes sur des indicateurs clés comme le taux de service ou les coûts. Pour y faire face, des méthodes améliorant la flexibilité des processus sont mais en place, comme le contexte de travail en Plan Glissant. Cependant, en actualisant fréquemment les données, la stabilité du système se retrouve dégradée. Ainsi, malgré les gains issus de la gestion des incertitudes, ce cadre crée une complexité dynamique à gérer. Ce travail traite de cette complexité issue de l’actualisation des prévisions pour la planification de production en plan glissant. Plus particulièrement, la question traitée ici concerne l’optimisation du plan de production, en considérant u n système mono-produit monoétage. Une modélisation mathématique générique est tout d’abord développée pour construire un modèle d’optimisation théorique du problème. Ensuite, une procédure de résolution optimale est développée en utilisant le cadre d’optimisation dynamique stochastique. Ce modèle est appliquée à des cas concrets pour lesquels l’optimalité des solutions calculées est prouvée analytiquement grâce à un raisonnement inductif basé sur des séquences de calcul d’espérances mathématiques. Des analyses numériques finalement conduites mettent en exergue les performances de la méthode développée, ses limites, et sa sensibilité vis-à-vis de l’environnement industriel. / Production Planning, as part of tactical operations integrated into the Supply Chain process, is a key procedure allowing decisioners to balance demand and production resources. One of its most challenging issues is to handle uncertainties, especially the ones coming from the Forecasted Demand. In order to manage indicators at stake, such as service level and costs, best practices increasing flexibility in the process are implemented, as Rolling-Plan Framework. However, it creates instability since the updates procedures make the data set on change constantly. Consequently, although the gain in terms of flexibility is non-negligible for the uncertainties management, it generates on the other hand dynamics complexity. We study in this work how to deal this dynamics complexity generated by updates of the Forecasted Demand made in a Rolling-Plan Framework of a Production Planning Process. In particular, the question to which it answers is how to optimize the Production Plan in such a context. This issue is tackled considering a single item single level production system. A general mathematical model in the context of our study is built to be exploitable for analytical optimization. A theoretical optimization framework is designed, and a specific solutions computation framework using stochastic dynamic programming is developed. We apply it in some precise study cases in order to compute optimal solutions and get some valuable analytical results thanks to a dynamic computation process. The optimality of the solutions is proven through an inductive reasoning based on expectations computation. Solutions are finally implemented and calculated numerically with simulations in some particular numerical examples. Analyses and sensitivity studies are performed, highlighting the performances of our optimization method.

No description available.

I explore whether participation on Estimize.com, a crowdsourced earnings-forecasting platform aimed primarily at novices, improves professional analysts’ forecast accuracy and career outcomes. Estimize provides its contributors with frequent and timely feedback on their forecast performance and offers them a new channel for disseminating their forecasts to a wider public, features that could help analysts improve their forecast accuracy and raise their online visibility. Using proprietary data obtained from Estimize and a difference-in-differences research design, I find that IBES analysts who are active on Estimize improve their EPS forecast accuracy by 13% relative to the sample-mean forecast error, as well as reduce forecast bias. These improvements in performance vary predictably in ways consistent with learning through feedback. Additionally, I find increased market reaction to the positive earnings-forecasts revisions issued by analysts who are active on Estimize. I also find that analysts active on Estimize enjoy incremental positive career outcomes after controlling for forecast accuracy. My results suggest that professional analysts can learn to become better forecasters through online feedback and consequently garner more attention from the market. My results also suggest analysts can improve their career outcomes by gaining additional online visibility. / Business Administration/Accounting

This thesis consists of three papers that makes independent contributions to the fields of forecast evaluation and financial econometrics. As such, the papers, chapter 1-3, can be read independently of each other. In Chapter 1, “Inferring an agent’s loss function based on a term structure of forecasts”, we provide conditions for identification, estimation and inference of an agent’s loss function based on an observed term structure of point forecasts. The loss function specification is flexible as we allow the preferences to be both asymmetric and to vary non-linearly across the forecast horizon. In addition, we introduce a novel forecast rationality test based on the estimated loss function. We employ the approach to analyse the U.S. Government’s preferences over budget surplus forecast errors. Interestingly, we find that it is relatively more costly for the government to underestimate the budget surplus and that this asymmetry is stronger at long forecast horizons. In Chapter 2, “Monitoring Systemic Risk”, we define systemic risk as the conditional probability of a systemic banking crisis. This conditional probability is modelled in a fixed effect binary response panel-model framework that allows for cross-sectional dependence (e.g. due to contagion effects). In the empirical application we identify several risk factors and it is shown that the level of systemic risk contains a predictable component which varies through time. Furthermore, we illustrate how the forecasts of systemic risk map into dynamic policy thresholds in this framework. Finally, by conducting a pseudo out-of-sample exercise we find that the systemic risk estimates provided reliable early-warning signals ahead of the recent financial crisis for several economies. Finally, in Chapter 3, “Equity Premium Predictability”, we reassess the evidence of out-of- sample equity premium predictability. The empirical finance literature has identified several financial variables that appear to predict the equity premium in-sample. However, Welch & Goyal (2008) find that none of these variables have any predictive power out-of-sample. We show that the equity premium is predictable out-of-sample once you impose certain shrinkage restrictions on the model parameters. The approach is motivated by the observation that many of the proposed financial variables can be characterised as ’weak predictors’ and this suggest that a James-Stein type estimator will provide a substantial risk reduction. The out-of-sample explanatory power is small, but we show that it is, in fact, economically meaningful to an investor with time-invariant risk aversion. Using a shrinkage decomposition we also show that standard combination forecast techniques tends to ’overshrink’ the model parameters leading to suboptimal model forecasts.

本研究探討Banker and Chen (2006)建構之CVCS模型與本研究建構之CVCS’模型之盈餘預測準確度與資訊內涵，並以ROE模型、OPINC模型、CASHFLOW模型與分析師盈餘預測作為判斷CVCS模型與CVCS’模型是否具有盈餘預測準確度與資訊內涵之比較基準模型。盈餘預測準確度之實證結果顯示：(1)CVCS模型之盈餘預測準確度低於ROE模型、OPINC模型與CASHFLOW模型之盈餘預測準確度；(2)CVCS’模型與ROE模型、OPINC模型、CASHFLOW模型之盈餘預測準確度並無差異；(3)CVCS模型之盈餘預測準確度低於分析師盈餘預測之盈餘預測準確度；(4)CVCS’模型之盈餘預測準確度低於分析師盈餘預測之盈餘預測準確度。資訊內涵之實證結果顯示：(1)CVCS模型之資訊內涵高於ROE模型、OPINC模型與CASHFLOW模型之資訊內涵；(2)CVCS’模型之資訊內涵低於ROE模型、OPINC模型與CASHFLOW模型之資訊內涵；(3)CVCS模型之資訊內涵低於分析師盈餘預測之資訊內涵；(4)CVCS’模型之資訊內涵低於分析師盈餘預測之資訊內涵。 / This study examines the forecast accuracy and the information content of CVCS model, proposed by Banker and Chen (2006), and CVCS’ model, constructed by this study. To evaluate the performances of these two models, this study uses ROE model, OPINC model, CASHFLOW model and analysts’ consensus forecasts as the benchmarks. The results of forecast accuracy show (1) the forecast accuracy of CVCS model is less than that of ROE model, OPINC model, and CASHFLOW model, (2) the forecast accuracy of CVCS’ model is not different from that of ROE model, OPINC model, and CASHFLOW model, (3) the forecast accuracy of CVCS model is less than that of analysts’ consensus forecasts and (4) the forecast accuracy of CVCS’ model is less than that of analysts’ consensus forecasts. The results of information content show (1) the information content of CVCS model is greater than that of ROE model, OPINC model, and CASHFLOW model, (2) the information content of CVCS’ model is less than that of ROE model, OPINC model, and CASHFLOW model, (3) the information content of CVCS model is less than that of analysts consensus forecasts, (4) the information content of CVCS’ model is less than that of analysts consensus forecasts.

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