We propose a new short-rate process which appropriately captures the salient features of the negative interest rate environment. The model combines the advantages of the Vasicek and Cox–Ingersoll–Ross (CIR) dynamics: it is flexible, tractable and displays positive skewness without imposing a strict lower bound. In addition, a novel calibration procedure is introduced which focuses on minimizing the Jensen–Shannon (JS) divergence between the model- and market-implied forward rate densities rather than focusing on the minimization of price or volatility discrepancies.

Economists have reported results based on populations for every country in the world for the past two thousand years. The source, McEvedy and Jones’ Atlas of World Population History, includes many estimates that are little more than guesses and that do not reflect research since 1978. McEvedy and Jones often infer population sizes from their view of a particular economy, making their estimates poor proxies for economic growth. Their rounding means their measurement error is not “classical.” Some economists augment that error by disaggregating regions in unfounded ways.

We explore time-varying networks for high-dimensional locally stationary time series, using the large VAR model framework with both the transition and (error) precision matrices evolving smoothly over time. Two types of time-varying graphs are investigated: one containing directed edges of Granger causality linkages, and the other containing undirected edges of partial correlation linkages.

We apply the Hole algorithm to evaluate the REF2021 output quality exercise. We find that the implied journal ranking agrees quite closely with the ABS-SCOB journal ranking, and in particular the GPA’s agree with a 91% correlation.

We show that three prominent consumption-based asset pricing models - the Bansal-Yaron, Campbell-Cochrane and Cecchetti-Lam-Mark models - cannot explain the dynamic properties of stock market returns. We show this by estimating these models with GMM, deriving ex-ante expected returns from them and then testing whether the difference between realised and expected returns is a martingale difference sequence, which it is not. Mincer-Zarnowitz regressions show that the models’ out-of-sample expected returns are systematically biased.

In this paper, we study a class of high dimensional moment restriction panel data models with interactive effects, where factors are unobserved and factor loadings are nonparametrically unknown smooth functions of individual characteristics variables. We allow the dimension of the parameter vector and the number of moment conditions to diverge with sample size. This is a very general framework and includes many existing linear and nonlinear panel data models as special cases.