Commonly used methods of production function estimation assume that a firm’s output quantity can be observed as data, but typical datasets contain only revenue, not output quantity. We examine the nonparametric identification of production function from revenue data when a firm faces a general...

We propose two novel methods to "bring ABMs to the data". First, we put forward a new Bayesian procedure to estimate the numerical values of ABM parameters that takes into account the time structure of simulated and observed time series. Second, we propose a method to forecast aggregate time...

We identify total factor productivity (TFP) news shocks using standard VAR methodology and document a new stylized fact: in response to news about future increases in TFP, inventories rise and comove positively with other major macroeconomic aggregates. We show that the standard theoretical...

One of the most important controversies in health economics concerns the question whether the imminent aging of the population in most OECD countries will place an additional burden on the tax payers who finance public health care systems. Proponents of the "red-herring hypothesis" argue that...

This paper investigates the effect of physicians on infant mortality, stillbirths and the incidence of common childhood diseases. We construct a new panel data set covering German municipalities from 1928 to 1936 based on historical sources. The endogeneity of health care supply is addressed by...

This paper presents an empirical model of sponsored search auctions in which advertisers are ranked by bid and ad quality. We introduce a new nonparametric estimator for the advertiser’s ad value and its distribution under the ‘incomplete information’ assumption. The ad value is...

Employing an endogenous growth model with human capital, this paper explores how productivity shocks in the goods and human capital producing sectors contribute to explaining aggregate fluctuations in output, consumption, investment and hours. Given the importance of accounting for both the...

We present a general framework for Bayesian estimation and causality assessment in epidemiological models. The key to our approach is the use of sequential Monte Carlo methods to evaluate the likelihood of a generic epidemiological model. Once we have the likelihood, we specify priors and rely...

We examine how financial crises redistribute risk, employing novel empirical methods and micro data from the largest financial crisis of the 20th century – the Great Depression. Using balance-sheet and systemic risk measures at the bank level, we build an econometric model with incidental...