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.

Forward Guidance and the State of the Economy

Abstract: This paper examines the economic effects of forward guidance using a New Keynesian model with a zero lower bound (ZLB) constraint on the short-term nominal interest rate. Forward guidance is modeled with anticipated news shocks to the monetary policy rule. There are five key findings: (1) the stimulative effect of forward guidance falls as the economy deteriorates or as households expect a slower recovery because there is a smaller margin to lower expected policy rates; (2) longer forward guidance horizons do not generate increasingly larger impact effects on output when the total amount of news is fixed, unlike with an exogenous interest rate peg; (3) in steady state, an unanticipated shock has a larger impact effect on output than a news shock, but a news shock has a larger cumulative effect in every state of the economy; (4) at the ZLB, the cumulative effect on output from lengthening the forward guidance horizon increases over short horizons but decreases thereafter, which indicates the central bank faces limits on how far forward guidance can extend into the future and continue to add stimulus; and (5) forward guidance is stimulative in the absence of other shocks, but the observed effect on output is smaller or even negative if another shock simultaneously reduces demand.

Seasonal Adjustment of State and Metro CES Jobs Data

Abstract: Hybrid time series data often require special care in estimating seasonal factors. Series such as the state and metro area Current Employment Statistics produced by the Bureau of Labor Statistics (BLS) are composed of two different source series that often have two different seasonal patterns. In this paper we address the process to test for differing seasonal patterns within the hybrid series. We also discuss how to apply differing seasonal factors to the separate parts of the hybrid series. Currently the BLS simply juxtaposes the two different sets of seasonal factors at the transition point between the benchmark part of the data and the survey part. We argue that the seasonal factors should be extrapolated at the transition point or that an adjustment should be made to the level of the unadjusted data to correct for a bias in the survey part of the data caused by differing seasonal factors at the transition month.

Do historical events matter in geographic agglomeration? The case of South Korea

Abstract: This study examines whether historical events are as important as Krugman (1991a) had suggested they are in determining geographic agglomeration. Using the time series of Korean manufacturing (1955–2003), which is longer than other country studies, the study also examines how the mean reversion factor and dispersion factor have evolved during substantial economic development. The results confirm that industry mobility in Korea is high and the historical events may not be so important in geographic agglomeration of industries. The analysis of the mean reversion factor and dispersion factor supports the argument that transport costs are a major source of change in geographic agglomeration in the long run.

Texas Maintains Top Exporter Standing While its Trade Remains Concentrated

Abstract: While Texas has become the nation's top exporting state, benefiting from trade of intermediate goods to Mexico and a global presence as an energy hub, its export activity remains concentrated relative to the U.S. and other states.

Inflation as a Global Phenomenon - Some Implications for Policy Analysis and Forecasting

Abstract: We evaluate the performance of inflation forecasts based on the open-economy Phillips curve by exploiting the spatial pattern of international propagation of inflation. We model these spatial linkages using global inflation and either domestic slack or oil price fluctuations, motivated by a novel interpretation of the forecasting implications of the workhorse open-economy New Keynesian model (Martinez-Garcia and Wynne (2010), Kabukcuoglu and Martinez-Garcia (2014)). We find that incorporating spatial interactions yields significantly more accurate forecasts of local inflation in 14 advanced countries (including the U.S.) than a simple autoregressive model that captures only the temporal dimension of the inflation dynamics.