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At the beginning of the 20th century, three intertwined ambitions drove federal legislation over wildlife and biodiversity: establishment of multiple-use federal lands, the economic development of natural resources, and the maintenance of option values. We examine this federal intervention in natural resource use by analyzing roll call votes over the past century with a Random Utility Model (Manski, 1977) and conclude that economics mattered. So did ideology, but not uniformly. After World War II, the pro-environment vote which had been conservative shifted to being liberal. All these votes involved decisions regarding public land that reallocated the returns to users by changing the asset’s physical character or its usage rights. We suggest that long-term consequences affecting current resource allocations arose from disparities between broadly dispersed benefits and locally concentrated socioeconomic and geophysical (spatial) costs. We show that a primary intent of public land management has become to preserve multiple-use option values and identify important factors in computing those option values. We do this by demonstrating how the willingness to forego current benefits for future ones depends on the community’s resource endowments. These endowments are defined not only in terms of users’ current wealth accumulation but also from their expected ability to extract utility from natural resources over time.

Recently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample than random walk forecasts or Bayesian vector autoregression (VAR) forecasts. Del Negro and Schorfheide (2013) in particular suggest that the DSGE model forecast should become the benchmark for forecasting horse-races. We compare the real-time forecasting accuracy of the Smets and Wouters (2007) DSGE model with that of several reduced-form time series models. We first demonstrate that none of the forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically simple AR models are most accurate at short horizons and DSGE models are most accurate at long horizons when forecasting output growth, while for inflation forecasts the results are reversed. Moreover, the relative accuracy of all models tends to evolve over time. Third, we show that there is no support to the common practice of using large-scale Bayesian VAR models as the forecast benchmark when evaluating DSGE models. Indeed, low-dimensional unrestricted AR and VAR forecasts may forecast more accurately.

This study applies theoretical perspectives from urban, environmental, and organization studies to examine if “smart growth” represents an ecological restructuring of the political economy of conventional urban development, long theorized as a “growth machine” (Molotch, H. (1976) The city as growth machine: Toward a political economy of place. American Journal of Sociology, 82, 309–332; Logan & Molotch, 2007); the purpose is to determine if there is a “smart growth machine.”

Nine smart growth projects (SGPs) in four cities in California and Oregon were identified and semistructured interviews were held with the respective developers, architects, and civic officials involved in their implementation process. Comparative, descriptive, and grounded approaches were used to generate themes from interviews and other data sources.

The findings suggest that an ecological modernization of urban political economy occurs through the coordination of entrepreneurial action, technical expertise, and “smart” regulation. Individual and institutional entrepreneurs shift the organizational field of urban development. Technical expertise is needed to make projects sustainable and financially feasible. Finally, a “smart” regulatory framework that balances regulations and incentives is needed to forge cooperative relationships between local governments and developers. This constellation of actors and institutions represents a smart growth machine.

The author questions whether urban growth can become “smart” using an original study of nine SGPs in four cities across California and Oregon.

The authors propose novel tests for the detection of Markov switching deviations from forecast rationality. Existing forecast rationality tests either focus on constant deviations from forecast rationality over the full sample or are constructed to detect smooth deviations based on non-parametric techniques. In contrast, the proposed tests are parametric and have an advantage in detecting abrupt departures from unbiasedness and efficiency, which the authors demonstrate with Monte Carlo simulations. Using the proposed tests, the authors investigate whether Blue Chip Financial Forecasts (BCFF) for the Federal Funds Rate (FFR) are unbiased. The tests find evidence of a state-dependent bias: forecasters tend to systematically overpredict interest rates during periods of monetary easing, while the forecasts are unbiased otherwise. The authors show that a similar state-dependent bias is also present in market-based forecasts of interest rates, but not in the forecasts of real GDP growth and GDP deflator-based inflation. The results emphasize the special role played by monetary policy in shaping interest rate expectations above and beyond macroeconomic fundamentals.