This seminar paper aims to briefly introduce selected modelfree methods which can be used both to evaluate specific forecast series and to compare pairwise competing series of forecasts. Problems arising from parameter estimation uncertainty and nested forecast generating models are illuminated curtly. The model-free methods will be applied to three series of annual german economic forecasts from 1970 - 2015 provided by the joint forecast and the Council of Economic Advisors.
It turns out that the forecast accuracy matches the chronology of the forecasts within the annual forecast semester. Moreover, a simple Monte Carlo study aims to illustrate graphically empirical size and empirical power of the tests for pairwise comparison depending on certain properties of the underlying forecast error sequences.
Inhaltsverzeichnis (Table of Contents)
- 1 Introduction
- 2 Evaluating Single Forecast Series
- 3 Pairwise Accuracy Comparison
- 3.1 Selected model-free tests
- 3.2 Parameter uncertainty and nested models
- 4 Application: Comparing Economic Forecasts
- 5 Simplistic Monte Carlo Study
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This seminar paper introduces model-free methods for evaluating single and comparing competing forecast series. The focus is on applying these methods to German economic forecasts from 1970-2015, highlighting challenges related to parameter uncertainty and nested models. A Monte Carlo study explores the empirical properties of the comparison tests.
- Model-free forecast evaluation methods
- Pairwise comparison of forecast series
- Impact of parameter uncertainty and nested models
- Application to German economic forecasts
- Empirical properties of model-free tests (Monte Carlo study)
Zusammenfassung der Kapitel (Chapter Summaries)
1 Introduction: This introductory chapter sets the stage for the seminar paper, outlining its objective to present and apply model-free methods for evaluating forecast accuracy. It briefly introduces the three German economic forecast series (1970-2015) that will be analyzed, emphasizing the model-free nature of the chosen statistical tests. The chapter also highlights the potential issues arising from incorporating information about the underlying forecast generating model into the statistical inference process, promising to briefly address these issues later. Finally, it details the paper's structure, providing a roadmap for the reader.
2 Evaluating Single Forecast Series: This chapter focuses on the methods used to assess the performance of a single forecast series. It introduces the concept of forecast errors (the difference between forecasted and actual values) and various loss functions (e.g., squared error, absolute error) used to quantify forecast accuracy. The chapter then discusses descriptive measures like Mean Squared Error (MSE), Mean Absolute Error (MAE), and Theil's inequality measure, providing a framework for understanding and quantifying the accuracy of individual forecasts over time. The detailed explanation of these metrics forms the foundation for the subsequent comparative analyses.
3 Pairwise Accuracy Comparison: This chapter delves into the methods for comparing the accuracy of two competing forecast series. It introduces selected model-free statistical tests, providing a detailed analysis of their application and interpretation. A key focus is on the challenges introduced by parameter uncertainty and nested models (where one model is a simplified version of another), exploring how these complexities affect the accuracy of comparative assessments. The chapter provides a nuanced understanding of the statistical tools used to compare forecasts and the potential pitfalls to avoid during the analysis.
4 Application: Comparing Economic Forecasts: This chapter applies the previously discussed methods to the three German economic forecast series from 1970-2015. The analysis likely involves comparing the accuracy of these forecasts using the selected model-free tests and considering the implications of parameter uncertainty and nested models. This section provides concrete examples and results, showcasing the practical application of the theoretical framework outlined in the preceding chapters.
5 Simplistic Monte Carlo Study: This chapter describes a Monte Carlo simulation designed to visually demonstrate the empirical size and power of the pairwise comparison tests. The simulation allows for a graphical illustration of how the tests' performance varies with different properties of the underlying forecast error sequences, providing valuable insights into the reliability and robustness of the methods.
Schlüsselwörter (Keywords)
Forecast evaluation, model-free methods, pairwise forecast comparison, parameter uncertainty, nested models, German economic forecasts, Monte Carlo simulation, forecast accuracy, loss functions, Mean Squared Error (MSE), Mean Absolute Error (MAE), Theil's U.
Frequently Asked Questions: A Comprehensive Language Preview
What is the main topic of this seminar paper?
This seminar paper focuses on model-free methods for evaluating single and comparing competing forecast series. It applies these methods to German economic forecasts from 1970-2015, addressing challenges like parameter uncertainty and nested models. A Monte Carlo study explores the empirical properties of the comparison tests.
What methods are used to evaluate single forecast series?
The paper utilizes various loss functions (e.g., squared error, absolute error) to quantify forecast accuracy. Descriptive measures like Mean Squared Error (MSE), Mean Absolute Error (MAE), and Theil's inequality measure are used to understand and quantify the accuracy of individual forecasts over time.
How does the paper compare the accuracy of competing forecast series?
The paper employs selected model-free statistical tests for pairwise accuracy comparison. It addresses the complexities introduced by parameter uncertainty and nested models, analyzing how these factors influence the accuracy of comparative assessments.
What data is used in the application section?
The application section uses three German economic forecast series spanning from 1970-2015 to demonstrate the practical application of the model-free methods. The analysis compares the accuracy of these forecasts considering parameter uncertainty and nested models.
What is the purpose of the Monte Carlo study?
The Monte Carlo simulation visually demonstrates the empirical size and power of the pairwise comparison tests. This allows for a graphical illustration of how the tests' performance varies with different properties of the underlying forecast error sequences, providing insights into the reliability and robustness of the methods.
What are the key themes explored in this paper?
Key themes include model-free forecast evaluation methods, pairwise comparison of forecast series, the impact of parameter uncertainty and nested models, application to German economic forecasts, and the empirical properties of model-free tests (as explored through the Monte Carlo study).
What are the key chapters and their contents?
The paper includes an introduction setting the context and outlining the objectives; a chapter on evaluating single forecast series; a chapter on pairwise accuracy comparison, addressing parameter uncertainty and nested models; an application chapter comparing German economic forecasts; and finally, a chapter detailing the simplistic Monte Carlo study.
What are the keywords associated with this paper?
Keywords include Forecast evaluation, model-free methods, pairwise forecast comparison, parameter uncertainty, nested models, German economic forecasts, Monte Carlo simulation, forecast accuracy, loss functions, Mean Squared Error (MSE), Mean Absolute Error (MAE), and Theil's U.
What is the overall objective of this paper?
The main objective is to present and apply model-free methods for evaluating and comparing forecast accuracy, specifically within the context of German economic forecasts, while acknowledging and addressing the challenges posed by parameter uncertainty and nested models.
- Quote paper
- Frank Undorf (Author), 2016, Forecast Evaluation Methods, Munich, GRIN Verlag, https://www.grin.com/document/441425
