Linear predictive regression framework

Table of Contents

1. Introduction

2. Linear Predictive Regression Model

3. Econometric Methodology and Hypothesis of Interest

4. Empirical Results

5. Economic Interpretation

6. Conclusions

References

Appendix

Table I, II, III

Table IV, V, VI

Table VII, VIII

Table IX, X

Figure I

Research Objectives and Core Themes

This dissertation examines the predictive power of dividend yields in forecasting future excess stock returns within a linear predictive regression framework, while explicitly addressing the issue of structural instability over time.

• Application of linear predictive regression to financial time series.
• Econometric challenges posed by persistent explanatory variables (endogeneity and near-unit-root processes).
• Detection of structural breaks using the Andrews (SupWald) test.
• Assessment of parameter stability across distinct economic regimes.
• Economic interpretation of findings in relation to historical stock market shocks.

Excerpt from the Book

Summary of Chapters

1. Introduction: Outlines the concept of predictive regressions, identifies statistical challenges like persistence and endogeneity, and states the primary research goal of assessing predictive ability and stability.

2. Linear Predictive Regression Model: Defines the mathematical framework of the regression model and discusses the econometric issues arising from persistent variables and endogenous innovations.

3. Econometric Methodology and Hypothesis of Interest: Details the testing procedure for structural breaks, specifically discussing the Andrews (SupWald) test as a remedy for unknown break dates.

4. Empirical Results: Presents the findings of applying the model to US stock market data (1927-2007), testing for unit roots and evaluating coefficient stability across sub-periods.

5. Economic Interpretation: Provides context for the statistical findings by correlating identified potential break-points with major historical economic events and shifts in stock market dynamics.

6. Conclusions: Summarizes the dissertation's contributions, acknowledging the complexity of predictive regression models and the necessity of testing for structural instability.

Keywords

Predictive regression, Dividend yields, Excess stock returns, Structural break, Andrews test, SupWald statistic, Parameter instability, Financial econometrics, Endogeneity, Persistence, Unit root, Forecasting, Market efficiency, Economic regime, Asset pricing.

Frequently Asked Questions

What is the core focus of this dissertation?

This work explores whether dividend yields can predict excess stock returns and whether this predictive relationship remains stable over time or is subject to structural breaks.

What are the central thematic fields covered?

The research sits at the intersection of financial econometrics and asset pricing, specifically focusing on the statistical validity of predictive models for stock returns.

What is the primary research question?

The study asks if excess stock returns are predictable using dividend yields and if such predictability holds stable across different economic regimes.

Which scientific method is applied?

The author employs a linear predictive regression framework and utilizes the Andrews (SupWald) test to detect potential structural instability in model coefficients.

What is covered in the main section of the work?

The work covers the theoretical formulation of the predictive model, the identification of statistical pitfalls (like endogeneity), the empirical application to long-term financial data, and the economic interpretation of found instabilities.

Which keywords best characterize the work?

Key terms include Predictive regression, Dividend yields, Structural break, SupWald statistic, and Asset pricing.

Why is the Andrews test used instead of a standard Chow test?

The Andrews test is used because the exact timing of potential structural breaks in the financial data is unknown, whereas the Chow test requires an a priori known break-point.

How do economic events influence the statistical findings?

The author notes that statistical break-points, such as those found in 1932 and 1995, often coincide with significant historical market events like the Great Depression and the emergence of new technologies in the 90s.