Abstract

In this paper we develop a general framework to analyze state space models with time-varying system matrices where time variation is driven by the score of the conditional likelihood. We derive a new filter that allows for the simultaneous estimation of the state vector and of the time-varying parameters. We use this method to study the time-varying relationship between the price dividend ratio, expected stock returns and expected dividend growth in the US since 1880. We find a significant increase in the long-run equilibrium value of the price dividend ratio over time, associated with a fall in the long-run expected rate of return on stocks. The latter can be attributed mainly to a decrease in the natural rate of interest, as the long-run risk premium has only slightly fallen.

Highlights

  • A decade after the Great Recession the global economy is mired in an environment of low real interest rates, low growth and high stock valuations

  • We derive the analytical expressions for a new set of recursions that, running in parallel with the Kalman Filter (KF), update at each point in time both the vector of time-varying parameters (TVP) and the latent states

  • The likelihood of any Gaussian state space model with TVP is available in closed form and the model can be estimated by maximum likelihood (ML)

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Summary

Introduction

A decade after the Great Recession the global economy is mired in an environment of low real interest rates, low growth and high stock valuations. In this paper we contribute to this debate by developing a general method to analyze state space models where parameters change over time and by applying this method to study the evolving relationship between stock valuations, stock returns and dividend growth. We use the methodology developed in the first part of the paper to revisit the relationship between the price dividend ratio, the return on stocks and dividend growth in present value models. This decline accelerated in the 1960s and in the 1990s, prior to the stock market crashes of the early 1970s and 2000s, and is reflected in an upward trend of long-run price dividend ratio, which so far the literature failed to explain An intuition for this result is sketched by Fama and French (2002) in their seminal paper on the equity risk premium.

Score driven state space models
Score driven system matrices
Result
The Jacobian counterpart of the KF leads to the following set of expressions
Non-linearity in the system matrices
Stock return and time-varying steady states
Preliminary evidence on parameter instability
A score driven present value model with drifting steady states
Slow-moving trends and time-varying risk
Results
Expected excess returns and the equilibrium real rate
Conclusions
A Proofs
Gradient and information matrix
Jacobians of the Kalman filter
State space model in forward form
B Examples
Local level model
Autoregressive models
C Monte Carlo exercise
Specification of the DGPs
Calibration
D Shrinking the vector of parameters by the L2 penalty
E Mixed frequencies and missing observations
F Correlated disturbances
Identification of the model
Modelling the correlation matrix by partial correlations
H Term structure of expected returns and dividend growth in recessions
Additional Results
Full Text
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