Wavelet analysis is a new mathematical method developed as a unified field of science over the last decade or so. As a spatially adaptive analytic tool, wavelets are useful for capturing serial correlation where the spectrum has peaks or kinks, as can arise from persistent dependence, seasonality, and other kinds of periodicity. This paper proposes a new class of generally applicable wavelet-based tests for serial correlation of unknown form in the estimated residuals of a panel regression model, where error components can be one-way or two-way, individual and time effects can be fixed or random, and regressors may contain lagged dependent variables or deterministic/stochastic trending variables. Our tests are applicable to unbalanced heterogenous panel data. They have a convenient null limit N(0,1) distribution. No formulation of an alternative model is required, and our tests are consistent against serial correlation of unknown form even in the presence of substantial inhomogeneity in serial correlation across individuals. This is in contrast to existing serial correlation tests for panel models, which ignore inhomogeneity in serial correlation across individuals by assuming a common alternative, and thus have no power against the alternatives where the average of serial correlations among individuals is close to zero. We propose and justify a data-driven method to choose the smoothing parameter—the finest scale in wavelet spectral estimation, making the tests completely operational in practice. The data-driven finest scale automatically converges to zero under the null hypothesis of no serial correlation and diverges to infinity as the sample size increases under the alternative, ensuring the consistency of our tests. Simulation shows that our tests perform well in small and finite samples relative to some existing tests.

We discuss the identification and estimation of discrete games of complete information. Following Bresnahan and Reiss (1990, 1991), a discrete game is a generalization of a standard discrete choice model where utility depends on the actions of other players. Using recent algorithms to compute all of the Nash equilibria to a game, we propose simulation-based estimators for static, discrete games. We demonstrate that the model is identified under weak functional form assumptions using exclusion restrictions and an identification at infinity approach. Monte Carlo evidence demonstrates that the estimator can perform well in moderately sized samples. As an application, we study entry decisions by construction contractors to bid on highway projects in California. We find that an equilibrium is more likely to be observed if it maximizes joint profits, has a higher Nash product, uses mixed strategies, and is not Pareto dominated by another equilibrium.

Structural measures of higher order risk attitudes have well‐developed foundations in Expected Utility Theory (EUT), but little is known about their empirical magnitudes. We introduce a novel experimental design and a companion econometric model that allows us to structurally estimate indices of risk aversion, prudence, and temperance under EUT without imposing restrictions on their interdependence. We find that indices of absolute risk aversion, prudence, and temperance exhibit distinct patterns of variation over income, and that predicted risk premia under EUT and Rank‐Dependent Utility Theory gradually converge as the order of risk increases. These findings are obscured by regular parametric utility functions, which inherently bias results toward prudence and temperance when subjects are risk averse. The results remain robust in subsamples of moderate size, which suggests that our approach can be adopted in broader studies that link higher order risk attitudes to other domains of latent individual preferences and economic behavior.

Earlier version: June 16, 2004; Revision: June 22, 2006

Emissions abatement alone cannot address the consequences of global warming for weather disasters. We model how society adapts to manage disaster risks to capital stock. Optimal adaptation—a mix of firm‐level efforts and public spending—varies as society learns about the adverse consequences of global warming for disaster arrivals. Taxes on capital are needed alongside those on carbon to achieve the first best. We apply our model to country‐level control of flooding from tropical cyclones. Learning rationalizes empirical findings, including the responses of Tobin's q , equity risk premium, and risk‐free rate to disaster arrivals. Adaptation is more valuable under learning than a counterfactual no‐learning environment. Learning alters social‐cost‐of‐carbon projections due to the interaction of uncertainty resolution and endogenous adaptive response.

We investigate the welfare properties of the one-sector neoclassical growth model with uninsurable idiosyncratic shocks. We focus on the notion of constrained efficiency used in the general equilibrium literature. Our characterization of constrained efficiency uses the first-order condition of a constrained planner’s problem. This condition highlights the margins of relevance for whether capital is too high or too low: the factor composition of income of the (consumption-) poor. Using three calibrations commonly considered in the literature, we illustrate that there can be either over- or underaccumulation of capital in steady state and that the constrained optimum may or may not be consistent with a nondegenerate long-run distribution of wealth. For the calibration that roughly matches the income and wealth distribution, the constrained inefficiency of the market outcome is rather striking: it has much too low a steady-state capital stock.