This paper studies the interplay between climate, health, and the economy in a stylized world with four heterogeneous regions, labeled ‘West’ (cold and rich), ‘China’ (cold and poor), ‘India’ (warm and poor), and ‘Africa’ (warm and very poor). We introduce health impacts into a...

We specify a stochastic economy-climate model, adapting Nordhaus' deterministic economy-climate model by allowing for Weitzman-type stochasticity. We show that, under expected power utility, the model is fragile to heavy-tailed distributional assumptions and we derive necessary and sufficient...

A Bayesian typically uses data and a prior to produce a posterior. In practice, the data and the posterior are often observed but not the prior. We shall follow the opposite route, using data and the posterior information to reveal the prior. We then apply this theory to (equilibrium) climate...

In Bayesian theory, the data together with the prior produce a posterior. We show that it is also possible to follow the opposite route, that is, to use data and posterior information (both of which are observable) to reveal the prior (which is not observable). We then apply the theory to...

Common sense is a dynamic concept and it is natural that our (statistical) common sense lags behind the development of statistical science. What is not so easy to understand is why common sense lags behind as much as it does. We conduct a survey among Japanese students and try to understand why...

In this paper, we propose a robust approach against heteroskedasticity, error serial correlation and slope heterogeneity for large linear panel data models. First, we establish the asymptotic validity of the Wald test based on the widely used panel heteroskedasticity and autocorrelation...

This paper proposes a novel test of zero pricing errors for the linear factor pricing model when the number of securities, N, can be large relative to the time dimension, T, of the return series. The test is based on Student t tests of individual securities and has a number of advantages over...

This paper develops an instrumental variable (IV) estimator for consistent estimation of dynamic panel data models with a multifactor error structure when both N and T, the cross-sectional and time series dimensions respectively, are large. Our approach projects out the common factors from...

In this paper, we consider statistical inference for high-dimensional approximate factor models. We posit a weak factor structure, in which the factor loading matrix can be sparse and the signal eigenvalues may diverge more slowly than the cross-sectional dimension, N. We propose a novel...

This paper puts forward a new instrumental variables (IV) approach for linear panel datamodels with interactive effects in the error term and regressors. The instruments are transformed regressors and so it is not necessary to search for external instruments. The proposed method asymptotically...