48 datasets found

This excel file includes the observation time, Q, concentration, and lag-time used by the sampling strategies. Types of sampling strategies: Time frequency sampling...

We introduce a simple stochastic time-series model (regime-and-memory model, RMM) for concentrations in the river that accounts for fluctuating release and transport with...

In the era of big data, the increasing availability of huge data sets can paradoxically be harmful when our causal inference method is designed to search for a causal model that...

The recent availability of huge, many-dimensional data sets, like those arising from genome-wide association studies (GWAS), provides many opportunities for strengthening causal...

Gene regulatory networks play a crucial role in controlling an organism’s biological processes, which is why there is significant interest in developing computational methods...

We investigate the issue of model uncertainty in cross-country growth regressions using Bayesian Model Averaging (BMA). We find that the posterior probability is spread widely...

I study whether and how US shocks are transmitted to eight Latin American countries. US shocks are identified using sign restrictions and treated as exogenous with respect to...

We construct a model of participation and bidding at multi-unit, sequential, clock auctions when bidders have multi-unit demand. We describe conditions sufficient to...

We use counterfactual experiments to investigate the sources of the large volatility reduction in US real GDP growth in the 1980s. Contrary to an existing literature that...

This paper introduces a new measure of dependence or jointness among explanatory variables. Jointness is based on the joint posterior distribution of variables over the model...

We consider the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of...

The paper addresses the issue of forecasting a large set of variables using multivariate models. In particular, we propose three alternative reduced rank forecasting models and...

Since empirical studies have shown that cannabis users are much more likely to initiate hard drug use, a causal linkage has been suggested (?gateway hypothesis?). However,...

Ciccone and Jaroci-ski (American Economic Journal: Macroeconomics 2010; 2: 222-246) show that inference in Bayesian model averaging (BMA) can be highly sensitive to small data...

We investigate whether return volatility is predictable by macroeconomic and financial variables to shed light on the economic drivers of financial volatility. Our approach is...

This paper analyzes patterns in the earnings development of young labor market entrants over their life cycle. We identify four distinctly different types of transition patterns...

This paper proposes Bayesian forecasting in a vector autoregression using a democratic prior. This prior is chosen to match the predictions of survey respondents. In particular,...

We use a macro-finance model, incorporating macroeconomic and financial factors, to study the term premium in the US bond market. Estimating the model using Bayesian techniques,...

Although speculative activity is central to black markets for currency, the out-of-sample performance of structural models in those settings is unknown. We substantially update...

This paper considers a panel data stochastic frontier model that disentangles unobserved firm effects (firm heterogeneity) from persistent (time-invariant/long-term) and...