This study examines the ability of the linear-rational square-root model to simultaneously capture cross-sectional and time-series dynamics of bond yields and their variances. The preferred model specification comprises five factors, two of which are not spanned by the yield curve, introducing unspanned stochastic volatility (USV). This specification provides a close in-sample fit to yields and yield variances, emphasizing the need for USV. Out-of-sample testing demonstrates low variance forecast errors. The specification provides evidence of USV in conditional yield variance and bond risk premia, linked to macroeconomic uncertainty.

Abstract We shed light on housing price inertia by investigating how the home-sale mechanism affects housing price dynamics. Using Australian data, we find that auction prices forecast better and display less momentum than negotiated prices. These findings are robust to alternative price measurements and different sample selection corrections. Motivated by microtheory that predicts different weights for buyer and seller values in auction and negotiated prices, we decompose housing prices into two diffusion processes and interpret them as buyer value and seller value, respectively. The seller value updates much more slowly, which could be an important driver of housing price inertia.

Abstract This paper studies mental health spillovers among new mothers. We exploit variation in the mental health of peers in mother groups in the Danish public postnatal care program. We show that municipal nurses assign mothers arbitrarily to groups conditional on a narrow set of well-defined characteristics. Exposure to a depressed peer in the group increases mothers' mental health care uptake by 11 percent two years after birth. We document worse self-reported mental health and labor market outcomes for treated mothers. Exploring heterogeneity, we find suggestive evidence for mental health deterioration, rather than increased demand for health care, as mechanism

Abstract This article presents and analyzes an approach to cluster-based inference for dependent data. The primary setting considered here is with spatially indexed data in which the dependence structure of observed random variables is characterized by a known, observed dissimilarity measure over spatial indices. Observations are partitioned into clusters with the use of an unsupervised clustering algorithm applied to the dissimilarity measure. Once the partition into clusters is learned, a cluster-based inference procedure is applied to a statistical hypothesis testing procedure. The procedure proposed in the article allows the number of clusters to depend on the data, which gives researchers a principled method for choosing an appropriate clustering level. The article gives conditions under which the proposed procedure asymptotically attains correct size. A simulation study shows that the proposed procedure attains near nominal size in finite samples in a variety of statistical testing problems with dependent data.