The authors model COVID infections and COVID deaths, both reported and implied, for the 50 U.S. states as well as the District of Columbia, and separately for a sample of 33 countries, as a function of pre-existing circumstances that citizens have no ability to control over the short term. These...

This paper proposes a particular behavioral assumption to characterize the stochastic structure of intertemporal discrete choice models in the absence of state dependence. This assumption extends Luce's axiom; Independence from Irrelevant Alternatives, to the intertemporal context. Under certain...

This paper examines the effect of systematic self-report bias, the non-random deviation between the self-reported and true values of the same measure. This bias may be constant or variable, and can mislead empirical analyses based on descriptive statistics, program evaluation and instrumental...

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...

Precise estimates of price elasticities are important for alcohol tax policy. Using meta-analysis, this paper corrects average beer elasticities for heterogeneity, dependence, and publication selection bias. A sample of 191 estimates is obtained from 114 primary studies. Simple and weighted...

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...

Health expenditure data almost always include extreme values. Such heavy tails can be a threat to the commonly adopted least squares methods. To accommodate extreme values, we propose the use of an estimation method that recovers the often ignored right tail of health expenditure distributions....

This chapter presents a unified set of estimation methods for fitting a rich array of models describing dynamic relationships within a longitudinal data setting. The discussion surveys approaches for characterizing the micro dynamics of continuous dependent variables both over time and across...

We develop a network-based vector autoregressive approach to uncover the interactions amongfinancial assets by integrating multiple realized measures based on high-frequency data. Undera restricted parameter structure, our approach allows the capture of cross-sectional and time ependencies...

We assess the bias and the efficiency of state-of-the-art dynamic panel data estimators by means of model-based Monte Carlo simulations. The underlying data-generating process consists of a standard theoretical growth model of income convergence based on capital accumulation. While we impose a...