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Hedging the Return on Equity and Firm Profit: Evidence from Canadian Oil and Gas CompaniesZhu, Jiachi 22 August 2012 (has links)
In this thesis, we analyse the relationship between the hedging activities and return on equity, and the relationship between profit on hedging and other factors. Fully conditional specification is used to impute the missing values. Instrumental variable estimation and finite mixture of regression models are then used to predict the return on equity and hedging gain. We find the instrumental variable estimation is better than the OLS estimation to deal with the hedging data since it eliminates the endogeneity. By finite mixture of regression models, we show that different firms have different hedging strategies, which cause different profits in hedging. We also find the companies with large total assets prefer to hedge.
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Three-Level Multiple Imputation: A Fully Conditional Specication ApproachJanuary 2015 (has links)
abstract: Currently, there is a clear gap in the missing data literature for three-level models.
To date, the literature has only focused on the theoretical and algorithmic work
required to implement three-level imputation using the joint model (JM) method of
imputation, leaving relatively no work done on fully conditional specication (FCS)
method. Moreover, the literature lacks any methodological evaluation of three-level
imputation. Thus, this thesis serves two purposes: (1) to develop an algorithm in
order to implement FCS in the context of a three-level model and (2) to evaluate
both imputation methods. The simulation investigated a random intercept model
under both 20% and 40% missing data rates. The ndings of this thesis suggest
that the estimates for both JM and FCS were largely unbiased, gave good coverage,
and produced similar results. The sole exception for both methods was the slope for
the level-3 variable, which was modestly biased. The bias exhibited by the methods
could be due to the small number of clusters used. This nding suggests that future
research ought to investigate and establish clear recommendations for the number of
clusters required by these imputation methods. To conclude, this thesis serves as a
preliminary start in tackling a much larger issue and gap in the current missing data
literature. / Dissertation/Thesis / Masters Thesis Psychology 2015
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Analysis of survey data in the presence of non-ignorable missing-data and selection mechanismsHammon, Angelina 04 July 2023 (has links)
Diese Dissertation beschäftigt sich mit Methoden zur Behandlung von nicht-ignorierbaren
fehlenden Daten und Stichprobenverzerrungen – zwei häufig auftretenden Problemen bei
der Analyse von Umfragedaten. Beide Datenprobleme können die Qualität der Analyseergebnisse erheblich beeinträchtigen und zu irreführenden Inferenzen über die Population führen. Daher behandle ich innerhalb von drei verschiedenen Forschungsartikeln,
Methoden, die eine Durchführung von sogenannten Sensitivitätsanalysen in Bezug auf
Missing- und Selektionsmechanismen ermöglichen und dabei auf typische Survey-Daten
angewandt werden können. Im Rahmen des ersten und zweiten Artikels entwickele ich Verfahren zur multiplen Imputation von binären und ordinal Mehrebenen-Daten, welche es zulassen, einen potenziellen Missing Not at Random (MNAR) Mechanismus zu berücksichtigen. In unterschiedlichen Simulationsstudien konnte bestätigt werden, dass die neuen Imputationsmethoden in der Lage sind, in allen betrachteten Szenarien unverzerrte sowie effiziente Schätzungen zuliefern. Zudem konnte ihre Anwendbarkeit auf empirische Daten aufgezeigt werden.
Im dritten Artikel untersuche ich ein Maß zur Quantifizierung und Adjustierung von nicht ignorierbaren Stichprobenverzerrungen in Anteilswerten, die auf der Basis von nicht-probabilistischen Daten geschätzt wurden. Es handelt sich hierbei um die erste Anwendung des Index auf eine echte nicht-probabilistische Stichprobe abseits der Forschergruppe, die das Maß entwickelt hat. Zudem leite ich einen allgemeinen Leitfaden für die
Verwendung des Index in der Praxis ab und validiere die Fähigkeit des Maßes vorhandene
Stichprobenverzerrungen korrekt zu erkennen.
Die drei vorgestellten Artikel zeigen, wie wichtig es ist, vorhandene Schätzer auf ihre Robustheit hinsichtlich unterschiedlicher Annahmen über den Missing- und Selektionsmechanismus zu untersuchen, wenn es Hinweise darauf gibt, dass die Ignorierbarkeitsannahme verletzt sein könnte und stellen erste Lösungen zur Umsetzung bereit. / This thesis deals with methods for the appropriate handling of non-ignorable missing
data and sample selection, which are two common challenges of survey data analysis.
Both issues can dramatically affect the quality of analysis results and lead to misleading
inferences about the population. Therefore, in three different research articles, I treat
methods for the performance of so-called sensitivity analyses with regards to the missing data and selection mechanism that are usable with typical survey data.
In the first and second article, I provide novel procedures for the multiple imputation
of binary and ordinal multilevel data that are supposed to be Missing not At Random
(MNAR). The methods’ suitability to produce unbiased and efficient estimates could be
demonstrated in various simulation studies considering different data scenarios. Moreover,
I could show their applicability to empirical data.
In the third article, I investigate a measure to quantify and adjust non-ignorable selection
bias in proportions estimated based on non-probabilistic data. In doing so, I provide
the first application of the suggested index to a real non-probability sample outside its
original research group. In addition, I derive general guidelines for its usage in practice,
and validate the measure’s performance in properly detecting selection bias.
The three presented articles highlight the necessity to assess the sensitivity of estimates
towards different assumptions about the missing-data and selection mechanism if it seems
realistic that the ignorability assumption might be violated, and provide first solutions to
enable such robustness checks for specific data situations.
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