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letztes Update: 03.01.2009
Besucher/-innen: Die ENCID ist unbekannt
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Statistik
UK Panel data Econometrics
Dynamic Panel Data methods for cross-section panels with an application on a winter tourism demand model.pdf
Presentation Slides.pdf
| The theoretical part deals with the importance of dynamic modelling, the bias of the LSDV estimator and then focuses on the description of consistent estimators. These are the Anderson/Hsia (1981), the Difference-GMM from Arellano and Bond (1991), the System-GMM from Blundell and Bond (1998) and the bias corrected LSDV estimator suggested by Bruno (2005). It follows an application which is taken from Eigner, Toeglhofer, Prettenthaler (2009) and which models winter tourism demand for 185 ski destinations in Austria, based on the number of overnight stays from 1973-2006. By using income and relative purchasing power of the tourists together with snow cover- age as determinants for tourism demand, both economic and climatologic aspects are combined in a single framework, based on an autoregressive distributed lag model. The study especially emphasizes the importance of climatologic variables in explaining winter tourism demand in Austria.
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Advanced Econometrics
Nonstationary Panel Data Methods.pdf
GAUSS-Code
| In this report, methods for nonstationary panel data are applied on a winter tourism demand model for Austrian ski destinations. Assuming cross‐section independence, cointegrating relationships are employed and estimated by OLS, fully modified OLS (FM‐OLS) and dynamic OLS (DOLS). Panel cointegration analyses are made with the statistical software GAUSS (Aptech Systems, 2001), using the packages Coint 2.0 by Ouliaris and Phillips, NPT 1.3 (Kao/Chiang, 2002) and CNPT by Hlouskova and Wagner.
- Winter Tourism Demand Model
- Panel Cointegration tests and estimations
- Panel unit root tests
- Cointegration tests
- Estimation table
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UK Non-linear Time Series Analysis
[url=http://web.student.tuwien.ac.at/~e0301345/texte/sv_heston.pdf]Stochastic Volatility - Option Pricing using Heston's SV model.pdf[/url]
[url=http://web.student.tuwien.ac.at/~e0301345/texte/sv_heston_presentation.pdf]Presentation Slides.pdf[/url]
| This article gives an introduction into the concept of stochastic volatility models in the field of option pricing using Heston‘s popular Stochastic Volatility (SV) model. The main idea of stochastic volatility models is to incorporate the empirical observation that volatility varies, at least in part, randomly. As opposed to basic versions of Black-Scholes-Merton and ARCH/GARCH models, stochastic volatility models are able to capture several important stylized facts in financial times series, e.g. volatility clustering, leptokurtic distribution of returns and the leverage effect. This article in particular emphasizes the differences in volatility modelling and its implications on option pricing between Heston’s SV, Black- Scholes-Merton and ARCH/GARCH models.
- Stylized Facts
- Volatility clustering
- Leverage Effect
- Volatility Smile/Skew
- Models for volatility modelling and option pricing
- Black-Scholes-Merton model
- ARCH/GARCH and Stochastic Volatility
- Heston's Stochastic Volatility Model
- Option pricing, computation and calibration
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Statistisches Programmieren:
Algorithmen zur Nullstellenbestimmung.pdf
R-Code
| Die 3 vorgestellten, adaptiven Algorithmen zur Nullstellenbestimmung werden mit dem Statistikprogramm R-Project programmiert und anschliessend anhand ihrer Konvergenzgeschwindigkeit verglichen.
- Bisektion (Halbierungsmethode)
- Sekantenmethode
- Newtonverfahren (Tangentenmethode)
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Zeitreihenanalyse:
Analyse der Exportdaten von den USA nach BRD.pdf
R-Code
| Die Analyse der Entwicklung der Exporte von den USA nach Deutschland, ausgedrueckt in Dollareinheiten, ist Gegenstand dieser Arbeit. Es handelt sich dabei um einen Monatsdatensatz, welcher den Zeitraum von 1974-01-01 bis 2007-10-01 umfasst und welcher noch nicht saisonbereinigt worden ist. Analysen erfolgen mit dem Statistikprogramm R-Project. Auf folgende Verfahren wird zurueckgegriffen:
Deterministische Modelle (Regressionsanalyse)
- Moving averages (MA, Gleitender Durchschnitt)
- Saisonale Komponente (Sinusoide)
Stochastische Modelle
- Spektralanalyse: Periodogramm (sample spectrum)
- Autokorrelationen – Abhaengigkeiten zwischen Zeitreihenwerten identifizieren
- AR-Modell
- ARIMA-Modell (Box-Jenkins-Modelle)
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Medical Statistics:
Survival of patients with breast cancer.pdf
R-Code
| 6 candidate markers were evaluated in its ability to predict survival of breast cancer patients. For simplicity, the expression values of these markers have been categorized (above median, below median). Univariable and multivariable Cox regressions are used to examine correlations between gene expression of pmp22 and the analyzed survival times, which were disease-free survival, overall survival and tumor specific survival. Analyzes showed that the significant effect of pmp22 in multivariable Cox regressions seemed to be due to interactions with pN and, at least in the overall survival case, with pT. The statistical package R Project was used for following analyses:
Correlations
- Spearman-coefficient
- chi^2 test
- Mann-Whitney Test
Kaplan-Meier Curves
Cox-Regressions
- univariable / multivariable
- Schoenfeld Residual,Stratification,Interaction,Terms,Cubic Splines
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