Volume 20, Issue 3

I consider complicated patterns of structural breaks in postwar quarterly US inflation rates based on the CPI and the GDP deflator over the period from 1953:Q1 to 2013:Q4. Bayesian model selection procedures suggest that the two inflation measures had distinct structural changes in different parameters as well as at different dates. CPI inflation experienced a dramatic drop in persistence around the early 1980s, but GDP deflator inflation remains persistent throughout the postwar sample period. The residual variance for both inflation measures switched from a low volatility regime to a high volatility regime in the early 1970s, but returned to another low volatility regime at different dates: the early 1980s for GDP deflator inflation and the early 1990s for CPI inflation. The residual variance for CPI inflation has increased again since the early 2000s, while GDP deflator inflation has remained less volatile. I do not find evidence of a structural shift in the unconditional mean of either measure of inflation. When reviewing the recent literature, considerable controversy exists over the structural break in inflation persistence around the early 1980s but this appears to be dependent on the measures of inflation, as highlighted by the empirical findings in this paper.

We examine price discovery in the Credit Default Swap and corporate bond market. Using a Markov switching framework enables us to analyze the dynamic behavior of the information shares during tranquil and crisis periods. The results show that price discovery takes place mostly on the CDS market. The importance of the CDS market even increases during the more volatile crisis periods. According to a cross sectional analysis liquidity is the main determinant of a market’s contribution to price discovery. During the crisis period, however, we also find a positive link between leverage and CDS market information shares. Overall the results indicate that price discovery measures and their determinants change during tranquil and crisis periods, which emphasizes the importance of more flexible frameworks, such as Markov switching models.

This paper analyzes global dynamics in an overlapping generations general equilibrium model with housing-wealth effects. It demonstrates that monetary policy cannot burst rational bubbles in the housing market. Under monetary policy rules of the Taylor-type, there exist global self-fulfilling paths of house prices along a heteroclinic orbit connecting multiple equilibria. From bifurcation analysis, the orbit features a boom (bust) in house prices when monetary policy is more (less) active. The paper also proves that booms or busts cannot be ruled out by interest-rate feedback rules responding to both inflation and house prices.

This paper extends the basic stochastic volatility (SV) model in order to incorporate the realized variance (RV) as an additional measure for the latent daily volatility. The particular model we use explicitly accounts for the dependency between daily returns and measurement errors of the realized volatility estimate. Within a simulation study we investigate the form of the dependency. In order to capture the long memory property of asset volatility, we explore different autoregressive dynamics for the latent volatility process, including heterogeneous autoregressive (HAR) dynamics and a two-component approach. We estimate the model using simulated maximum likelihood based on efficient importance sampling (EIS), producing numerically accurate parameter estimates and filtered state sequences. The model is applied to daily asset returns and realized variances of New York Stock Exchange (NYSE) traded stocks. Estimation results indicate that accounting for the dependency of returns and realized measures significantly affects the estimation results and improves the model fit for all autoregressive dynamics.

Using post-war data on advanced economies, we find that a higher public debt ratio predicts marginally slower GDP growth under the assumption of a linear relationship. This result is robust to strong persistence in debt ratio, which may cause finite sample bias in estimation and inference. In the nonlinear framework, we find only weak support for piece-wise linear models that explicitly incorporate the idea of a debt tipping point. The threshold estimates from such models are subject to a high level of uncertainty and are sensitive to assumptions on minimum number of observations in each regime. However, using a flexible semiparametric model we uncover that the predictive function is highly complex and behaves quite differently at low, intermediate and high levels of debt. Of particular interest to the recent debate on effects of higher public indebtedness on growth, we find that average annual GDP growth gradually declines by about 0.5% as debt ratio climbs from about 75% to 100%, with most of the effect taking place over the 85–95% range.

In this paper the performance of different information criteria for simultaneous model class and lag order selection is evaluated using simulation studies. We focus on the ability of the criteria to distinguish linear and nonlinear models. In the simulation studies, we consider three different versions of the commonly known criteria AIC, SIC and AICc. In addition, we also assess the performance of WIC and evaluate the impact of the error term variance estimator. Our results confirm the findings of different authors that AIC and AICc favor nonlinear over linear models, whereas weighted versions of WIC and all versions of SIC are able to successfully distinguish linear and nonlinear models. However, the discrimination between different nonlinear model classes is more difficult. Nevertheless, the lag order selection is reliable. In general, information criteria involving the unbiased error term variance estimator overfit less and should be preferred to using the usual ML estimator of the error term variance.