Federal Reserve Bank of St. Louis

About: Federal Reserve Bank of St. Louis is a other organization based out in St Louis, Missouri, United States. It is known for research contribution in the topics: Monetary policy & Inflation. The organization has 203 authors who have published 1650 publications receiving 46084 citations.

Spatial hedonic models of airport noise, proximity, and housing prices*

Abstract: Despite the refrain that housing prices are determined by “location, location, and location,” few studies of airport noise and housing prices have incorporated spatial econometric techniques. We compare various spatial econometric models and estimation methods in a hedonic price framework to examine the impact of noise on 2003 housing prices near the Atlanta airport. Spatial effects are best captured by a model including both spatial autocorrelation and autoregressive parameters estimated by a generalized moments approach. In our preferred model, houses located in an area in which noise disrupts normal activities (defined by a day–night sound level of 70–75 decibels) sell for 20.8 percent less than houses located where noise does not disrupt normal activities (defined by a day–night sound level below 65 decibels). The inclusion of spatial effects magnifies the negative price impacts of airport noise. Finally, after controlling for noise, houses farther from the airport sell for less; the price elasticity with respect to distance is −0.15, implying that airport proximity is an amenity.

Are Government Spending Multipliers Greater during Periods of Slack? Evidence from Twentieth-Century Historical Data †

Abstract: A key question that has arisen during recent debates is whether government spending multipliers are larger during periods of slack. Some researchers and policymakers have argued that while government spending multipliers are estimated to be modest on average, they might become greater during times when resources are underutilized. Auerbach and Gorodnichenko (2012, forthcoming) —henceforth, AG—test this hypothesis and find larger multipliers dur

The Use and Abuse of Real-Time Data in Economic Forecasting

Abstract: We distinguish between three different strategies for estimating forecasting equations with real-time data and argue that the most popular approach should generally be avoided. The point is illustrated with a model that uses current-quarter monthly industrial production, employment, and retail sales data to predict real GDP growth. When the model is estimated using either of our two alternative methods, its out-of-sample forecasting performance is superior to that obtained using conventional estimation and compares favorably with that of the Blue Chip consensus.

Improving forecast accuracy by combining recursive and rolling forecasts

Abstract: This article presents analytical, Monte Carlo, and empirical evidence on combining recursive and rolling forecasts when linear predictive models are subject to structural change. Using a characterization of the bias–variance trade-off faced when choosing between either the recursive and rolling schemes or a scalar convex combination of the two, we derive optimal observation windows and combining weights designed to minimize mean square forecast error. Monte Carlo experiments and several empirical examples indicate that combination can often provide improvements in forecast accuracy relative to forecasts made using the recursive scheme or the rolling scheme with a fixed window width.