Abstract This paper uses fractional integration methods to examine persistence, trends and structural breaks in United States house prices, more specifically the monthly Federal Housing Finance Agency House Price Index for census divisions, as a whole over period from January 1991 August 2022. The full sample estimates imply that order of series is above one all cases, particularly high aggrega...

Structural change has been conjectured to lead to an upward bias in the estimated forward expectations coefficient in New-Keynesian Phillips curves. We present a simple New-Keynesian model that enables us to assess this proposition. In particular, we investigate the issue of upward bias in the estimated coefficients of the expectations variable in the New-Keynesian Phillips curve based on a mod...

We propose a modified version of the nonparametric level crossing random walk test, in which the crossing level is determined locally. This modification results in a test that is robust to unknown multiple structural breaks in the level and slope of the trend function under both the null and alternative hypothesis. No knowledge regarding the number or timing of the breaks is required. An algori...

On purpose to extract trend and cycle from a time series many competing techniques have been developed. The probably most prevalent is the Hodrick Prescott filter. However this filter suffers from diverse shortcomings, especially the subjective choice of its penalization parameter. To this point penalized splines within a mixed model framework offer the advantage of a data driven derivation of ...

This paper presents a Bayesian assessment of the likelihood of unit roots in the unemployment rates of 16 OECD countries. Bayesian techniques for detecting multiple structural breaks in time series have recently been developed by Wang and Zivot (2000). I apply these tests to a data set recently analyzed by Papell et al (2000). I also treat the number of structural breaks as an additional parame...

We propose a flexible model that is able to simultaneously approximate long memory behavior as well as incorporate structural breaks in the model parameters. Our model is an extension of the heterogeneous autoregressive (HAR) model, which is designed to model and forecast volatility of financial time series. In an extensive empirical evaluation involving several volatility series, we demonstrat...