We propose an approach for Bayesian inference in time-varying structural vector autoregressions (SVARs) identified with sign restrictions. The linchpin of our approach is a class of rotation-invariant time-varying SVARs in which the prior and posterior densities of any sequence of structural parameters belonging to the class are invariant to orthogonal transformations of the sequence. Our methodology is new to the literature. In contrast to existing algorithms for inference based on sign restrictions, our algorithm is the first to draw from a uniform distribution over the sequences of orthogonal matrices given the reduced-form parameters. We illustrate our procedure for inference by analyzing the role played by monetary policy during the latest inflation surge.

We estimate the first cross-sectional index of transaction-based land values for every U.S. metropolitan area. The index accounts for geographic selection and incorporates novel shrinkage methods using a prior belief based on urban economic theory. Land values at the city center increase with city size, as do land-value gradients; both are highly variable across cities. Urban land values are estimated at more than two times GDP in 2006. These estimates are higher and less volatile than estimates from residual (total - structure) methods. Five urban agglomerations account for 48% of all urban land value in the United States.