Federal Reserve Bank of Dallas

About: Federal Reserve Bank of Dallas is a other organization based out in Dallas, Texas, United States. It is known for research contribution in the topics: Monetary policy & Inflation. The organization has 196 authors who have published 994 publications receiving 35508 citations.

Optimal Interest Rate Rules and Inflation Stabilization versus Price-Level Stabilization

Abstract: This paper compares the properties of interest rate rules such as simple Taylor rules and rules that respond to price-level fluctuations — called Wicksellian rules — in a basic forward-looking model. By introducing appropriate history dependence in policy, Wicksellian rules perform better than optimal Taylor rules in terms of welfare and robustness to alternative shock processes, and they are less prone to equilibrium indeterminacy. A simple Wicksellian rule augmented with a high degree of interest rate inertia resembles a robustly optimal rule — that is, a monetary policy rule that implements the optimal plan and is also completely robust to the specification of exogenous shock processes.

Small-Business Lending Languishes as Community Banking Weakens

Abstract: Community banks are key providers of loans to small businesses, which are important contributors to the local economy and international trade. While regulatory burden on small banks and its impact on lending has received attention, it is difficult to isolate the most significant driver of sluggish small-business lending.

Do High-Frequency Financial Data Help Forecast Oil Prices? The Midas Touch at Work

Abstract: The substantial variation in the real price of oil since 2003 has renewed interest in the question of how to forecast monthly and quarterly oil prices. There also has been increased interest in the link between financial markets and oil markets, including the question of whether financial market information helps forecast the real price of oil in physical markets. An obvious advantage of financial data in forecasting oil prices is their availability in real time on a daily or weekly basis. We investigate whether mixed-frequency models may be used to take advantage of these rich data sets. We show that, among a range of alternative high-frequency predictors, especially changes in U.S. crude oil inventories produce substantial and statistically significant real-time improvements in forecast accuracy. The preferred MIDAS model reduces the MSPE by as much as 16 percent compared with the no-change forecast and has statistically significant directional accuracy as high as 82 percent. This MIDAS forecast also is more accurate than a mixed-frequency real-time VAR forecast, but not systematically more accurate than the corresponding forecast based on monthly inventories. We conclude that typically not much is lost by ignoring high-frequency financial data in forecasting the monthly real price of oil.