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


Master's Thesis, 2017
60 Pages, Grade: 1,0

Abstract or Introduction

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.

Details

Title
Forecast evaluation methods: A Monte Carlo investigation and an application to the predictability of interest rates
College
University of Cologne  (Institut für Ökonometrie und Statistik)
Grade
1,0
Author
Year
2017
Pages
60
Catalog Number
V439428
ISBN (eBook)
9783668792470
ISBN (Book)
9783668792487
Language
English
Tags
Forecast Evaluation, Monte Carlo, Interest Rates, Forecast Encompassing, Zinsprognose, Diebold-Mariano, Prognosegüte, Econometrics, Economics
Quote paper
Frank Undorf (Author), 2017, Forecast evaluation methods: A Monte Carlo investigation and an application to the predictability of interest rates, Munich, GRIN Verlag, https://www.grin.com/document/439428

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