Frank Undorf

Forecast evaluation methods: A Monte Carlo investigation and an application to the predictability of interest rates

• Broschiertes Buch

Produktbeschreibung

Master's Thesis from the year 2017 in the subject Economics - Statistics and Methods, grade: 1,0, University of Cologne (Institut für Ökonometrie und Statistik), language: English, abstract: This thesis overviews selected forecast evaluation tests and attempts to link the concept of testing equal mean squared error and forecast encompassing within a common simple regression framework. A Monte Carlo analysis provides size and power properties for both a model-free and model-based environment. In particular, the encompassing regression based test assessing the null hypothesis of equal mean squared error offers beneficial size and power properties compared to the Diebold-Mariano test, at least in a conditional homoskedastic small sample framework without autocorrelation. A simple application of several tests is provided by comparing different interest rate prediction models like a time series model, a linear model with macroeconomic indicators and a dynamic yield curve model. It turns out that simple time series specifications are hard to outperform for most of the comparisons. However, indicators like the German stock market index or the ifo expectation indicator provide useful information for future German government bond yields.

Produktdetails

• Verlag: GRIN Verlag
• 1. Auflage
• Seitenzahl: 68
• Erscheinungstermin: 17. Oktober 2018
• Englisch
• Abmessung: 210mm x 148mm x 6mm
• Gewicht: 112g
• ISBN-13: 9783668792487
• ISBN-10: 3668792488
• Artikelnr.: 54352491

Inhaltsangabe

1 Introduction 2 Forecast evaluation methods 2.1 Selected tests in a model-free environment 2.1.1 Forecast encompassing 2.1.2 Equal mean squared error 2.2 Selected tests in a model-based environment 2.2.1 Exemplary forecasting framework 2.2.2 Non-nested model structure 2.2.3 Nested model structure 2.2.4 Inference à la Giacomini and White (2006) 3 Monte Carlo evidence 3.1 Model-free framework 3.1.1 Size properties 3.1.2 Power properties 3.2 Model-based framework 3.2.1 Non-nested models 3.2.1.1 Size investigation 3.2.1.2 Power investigation 3.2.2 Nested models 3.2.2.1 Size investigation 3.2.2.2 Power investigation 4 Application: Predicting interest rates 4.1 Competing prediction models 4.1.1 Macro-indicator model 4.1.2 Pure time series model 4.1.3 Dynamic Svensson (1994) model 4.2 Results of pairwise comparison 4.3 Out-of-sample Granger causality 5 Conclusion