Four Essays on Probabilistic Forecasting in Econometrics
Four Essays on Probabilistic Forecasting in Econometrics
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2013
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Development and application of new methodolgoies of combining time series and expert survey data for economic forecasting
Analyse, Modellierung und Vorhersage
Analyse, Modellierung und Vorhersage
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Abstract
This dissertation is concerned with probabilistic forecasting, which has been a vivid research area within econometrics during the past few years. Probabilistic forecasts indicate a predictive probability distribution for a variable of interest. Thus, they contain forecasts of all possible features of the predictand, like its mean, quantiles, and variance, and can serve as a basis for decision making in very general settings. This is an important aspect, since decision making is arguably the key motivation behind constructing forecasts in the first place. In particular, probabilistic forecasts are useful in situations in which the forecaster and the forecast user(s) are not the same person. By issuing a probabilistic
prediction, a forecaster fully communicates her uncertainty about the future, and thus allows potential users to put the forecast in perspective. Perhaps the most popular economic examples of this situation are so-called "fan charts" of inflation issued by several central banks around the world.
The demand for probabilistic forecasts raises two basic questions: First, what is a good probabilistic forecast? Second, how can it be constructed? Broadly, each of the four chapters of this thesis deals with one or both of these questions. The chapters are stand-alone research papers which I have written, three of them jointly with coauthors as mentioned below, during my PhD studies at the University of Konstanz.
Chapters 1 and 2 deal with the case of a binary predictand. In this case, a probabilistic forecast is simply a number between zero and one. This simplifies many conceptual and technical issues about forecast evaluation. At the same time, the binary case is relevant in practice – for example, assessments of the probability of a recession, or the probability of a sovereign defaulting on its debt, are routinely reported in the financial press. Chapters 3 and 4 deal with more complicated (continuous or mixed discrete-continuous) predictands, which also arise in many applied settings.
prediction, a forecaster fully communicates her uncertainty about the future, and thus allows potential users to put the forecast in perspective. Perhaps the most popular economic examples of this situation are so-called "fan charts" of inflation issued by several central banks around the world.
The demand for probabilistic forecasts raises two basic questions: First, what is a good probabilistic forecast? Second, how can it be constructed? Broadly, each of the four chapters of this thesis deals with one or both of these questions. The chapters are stand-alone research papers which I have written, three of them jointly with coauthors as mentioned below, during my PhD studies at the University of Konstanz.
Chapters 1 and 2 deal with the case of a binary predictand. In this case, a probabilistic forecast is simply a number between zero and one. This simplifies many conceptual and technical issues about forecast evaluation. At the same time, the binary case is relevant in practice – for example, assessments of the probability of a recession, or the probability of a sovereign defaulting on its debt, are routinely reported in the financial press. Chapters 3 and 4 deal with more complicated (continuous or mixed discrete-continuous) predictands, which also arise in many applied settings.
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Die vorliegende Dissertation befasst sich mit probabilistischen Prognosen, die seit einigen Jahren ein aktives ökonometrisches Forschungsgebiet darstellen. Da solche Prognosen eine vollständige Verteilung für die interessierende Zufallsvariable angeben, beinhalten sie Information über sämtliche ihrer Eigenschaften – wie etwa Erwartungswert, Quantile, und Varianz – und können als Grundlage für ökonomische Entscheidungen in verschiedenen Situationen dienen. Dieser Aspekt ist von zentraler Bedeutung, da Prognosen üblicherweise mit dem Ziel konstruiert werden, "gute" Entscheidungen herbeizuführen. Die Erstellung probabilistischer Prognosen ist insbesondere dann nützlich, wenn die Prognose an mehrere Nutzer kommuniziert wird. Durch Angabe einer vollständigen Zufallsverteilung legt der Prognostiker seine Unsicherheit über die Zukunft gegenüber den Nutzern offen. Das
vielleicht wichtigste ökonomische Beispiel für solch eine Situation sind so genannte "Fan Charts" für Inflation, wie sie von einigen Zentralbanken veröffentlicht werden.
Die Analyse probabilistischer Prognosen wirft zwei grundlegende Fragen auf: Erstens, was zeichnet eine gute probabilistische Prognose aus? Zweitens, wie kann eine solche Prognose erstellt werden? Grob gesprochen dreht sich jedes Kapitel der vorliegenden Dissertation um eine oder beide dieser Fragen. Die Kapitel sind eigenständige Forschungspapiere, die ich während meines Promotionsstudiums an der Universität Konstanz verfasst habe. Wie unten beschrieben, sind drei der Kapitel gemeinsam mit Koautoren entstanden.
Kapitel 1 und 2 behandeln den Fall einer binären Zielgröße; die probabilistische Prognoseverteilung ist hier eine Zahl zwischen null und eins. Dies vereinfacht viele konzeptionelle und technische Aspekte der Evaluation dieser Prognose. Gleichzeitig ist der binäre Fall praxisrelevant; beispielsweise berichtet die Wirtschaftspresse regelmäßig über die Wahrscheinlichkeit einer Rezession oder eines Staatsbankrotts. Kapitel 3 und 4 behandeln kompliziertere (stetige bzw. gemischt stetig-diskrete) Zielgrößen, die ebenfalls anwendungsrelevant sind.
vielleicht wichtigste ökonomische Beispiel für solch eine Situation sind so genannte "Fan Charts" für Inflation, wie sie von einigen Zentralbanken veröffentlicht werden.
Die Analyse probabilistischer Prognosen wirft zwei grundlegende Fragen auf: Erstens, was zeichnet eine gute probabilistische Prognose aus? Zweitens, wie kann eine solche Prognose erstellt werden? Grob gesprochen dreht sich jedes Kapitel der vorliegenden Dissertation um eine oder beide dieser Fragen. Die Kapitel sind eigenständige Forschungspapiere, die ich während meines Promotionsstudiums an der Universität Konstanz verfasst habe. Wie unten beschrieben, sind drei der Kapitel gemeinsam mit Koautoren entstanden.
Kapitel 1 und 2 behandeln den Fall einer binären Zielgröße; die probabilistische Prognoseverteilung ist hier eine Zahl zwischen null und eins. Dies vereinfacht viele konzeptionelle und technische Aspekte der Evaluation dieser Prognose. Gleichzeitig ist der binäre Fall praxisrelevant; beispielsweise berichtet die Wirtschaftspresse regelmäßig über die Wahrscheinlichkeit einer Rezession oder eines Staatsbankrotts. Kapitel 3 und 4 behandeln kompliziertere (stetige bzw. gemischt stetig-diskrete) Zielgrößen, die ebenfalls anwendungsrelevant sind.
Subject (DDC)
330 Economics
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Conference
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Cite This
ISO 690
KRÜGER, Fabian, 2013. Four Essays on Probabilistic Forecasting in Econometrics [Dissertation]. Konstanz: University of KonstanzBibTex
@phdthesis{Kruger2013Essay-25352,
year={2013},
title={Four Essays on Probabilistic Forecasting in Econometrics},
author={Krüger, Fabian},
address={Konstanz},
school={Universität Konstanz}
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Examination date of dissertation
October 15, 2013