This article proposes a test for the conditional superior predictive ability (CSPA) of a family of forecasting methods with respect to a benchmark. The test is functional in nature: under the null hypothesis, the benchmark’s conditional expected loss is no more than those of the competitors, uniformly across all conditioning states. By inverting the CSPA tests for a set of benchmarks, we obtain confidence sets for the uniformly most superior method. The econometric inference pertains to testing conditional moment inequalities for time series data with general serial dependence, and we justify its asymptotic validity using a uniform non-parametric inference method based on a new strong approximation theory for mixingales. The usefulness of the method is demonstrated in empirical applications on volatility and inflation forecasting.

We use a unique quasi-experiment–spillovers from the imposition of purchase restrictions on local housing to nearby unregulated cities–to study the effects of out-of-town housing demand on house prices and consumer spending. While these restrictions effectively stymied the surge in local house prices, they induced capital flight and sharp abnormal increases in house prices in nearby unregulated cities. The effect of the house price increases on consumer spending is positive in the aggregate, but echoing Favilukis and Van Nieuwerburgh (2021), is redistributive, that is, negative for renters and positive for homeowners.