Until the early decades of the 20th century, women spent more than 60% of their prime-age years either pregnant or nursing. Since then, the introduction of infant formula reduced women's comparative advantage in infant care, by providing an effective breast milk substitute. In addition, improved...

This paper unveils a new resource for macroeconomic research: a long-run dataset covering disaggregated bank credit for 17 advanced economies since 1870. The new data show that the share of mortgages on banks’ balance sheets doubled in the course of the 20th century, driven by a sharp rise of...

We develop a novel Bayesian pooling technique to estimate aggregate production functions for the previously centrally planned economies (PCPEs) of Eastern Europe and for Western economies, as well as for a group of developing countries. This technique adjusts for the low quality of the PCPE data...

Empirical models of differentiated product demand are widely used by both academics and practitioners. While these methods treat carefully the potential endogeneity of price, until recently they have assumed the number and characteristics of the products offered by firms are exogenous. This...

This paper compares the predictive ability of the factor models of Stock and Watson (2002) and Forni, Hallin, Lippi, and Reichlin (2005) using a large panel of US macroeconomic variables. We propose a nesting procedure of comparison that clarifies and partially overturns the results of similar...

We study workplace peer effects in fertility decisions using a game theory model of strategic interactions among coworkers that allows for multiple equilibria. Using register-based data on fertile-aged women working in medium sized establishments in Denmark, we uncover negative average peer...

We propose to use the attractiveness of pooling relatively short time series that display similar dynamics, but without restricting to pooling all into one group. We suggest estimating the appropriate grouping of time series simultaneously along with the group-specific model parameters. We cast...

The estimation of large Vector Autoregressions with stochastic volatility using standard methods is computationally very demanding. In this paper we propose to model conditional volatilities as driven by a single common unobserved factor. This is justified by the observation that the pattern of...

This paper describes an algorithm to compute the distribution of conditional forecasts, i.e. projections of a set of variables of interest on future paths of some other variables, in dynamic systems. The algorithm is based on Kalman filtering methods and is computationally viable for large...

This paper considers Bayesian regression with normal and double exponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range...