11,510 research outputs found

    Reserve requirements: A modern perspective

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    The discussion in many money and banking textbooks would suggest that the Federal Reserve requires depository institutions to hold a minimum level of non-interest-earning reserves because (1) reserve requirements are a monetary policy tool that allows the Fed to expand the money supply and lower interest rates, and (2) reserve requirements improve the safety and soundness of depository institutions. This article argues that this "conventional wisdom" view is too narrow. ; The Fed often uses reserve requirement changes, the authors contend, to achieve non-monetary-policy objectives, as it did in 1992 to improve the profitability of depository institutions and ease the credit crunch of that time. The authors also challenge the notion that higher reserve requirements necessarily lead to greater safety and lower default risk for depository institutions. ; The article examines the relationship between reserve requirement changes and monetary policy, with the aim of demonstrating the recent, limited usefulness of reserve requirements as a monetary policy tool. The article proposes a more modern view of reserve requirements as a tax on depository institutions, ponders who really bears this tax, and summarizes a large and growing literature suggesting that perceived bank profitability is inversely affected by announced changes in reserve requirement ratios. The article also provides new evidence that the 1992 reserve requirement reductions were not associated with an increase in default risk for financial institutions that issue reservable instruments, as the conventional view would suggest.Monetary policy ; Financial institutions ; Bank reserves

    A selective control information detection scheme for OFDM receivers

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    In wireless communications, both control information and payload (user-data) are concurrently transmitted and required to be successfully recovered. This paper focuses on block-level detection, which is applicable for detecting transmitted control information, particularly when this information is selected or chosen from a finite set of information that are known at both transmitting and receiving devices. Using an orthogonal frequency division multiplexing architecture, this paper investigates and evaluates the performance of a time-domain decision criterion in comparison with a form of Maximum Likelihood (ML) estimation method. Unlike the ML method, the proposed time-domain detection technique requires no channel estimation as it uses the correlation (in the time-domain) that exists between the received and the transmitted selective information as a means of detection. In comparison with the ML method, results show that the proposed method offers improved detection performance, particularly when the control information consists of at least 16. However, the implementation of the proposed method requires a slightly increased number of mathematical computations

    A low complexity SI sequence estimator for pilot-aided SLM–OFDM systems

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    Selected mapping (SLM) is a well-known method for reducing peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. However, as a consequence of implementing SLM, OFDM receivers often require estimation of some side information (SI) in order to achieve successful data recovery. Existing SI estimation schemes have very high computational complexities that put additional constraints on limited resources and increase system complexity. To address this problem, an alternative SLM approach that facilitates estimation of SI in the form of phase detection is presented. Simulations show that this modified SLM approach produces similar PAPR reduction performance when compared to conventional SLM. With no amplifier distortion and in the presence of non-linear power amplifier distortion, the proposed SI estimation approach achieves similar data recovery performance as both standard SLM–OFDM (with perfect SI estimation) and also when SI estimation is implemented through the use of an existing frequency-domain correlation (FDC) decision metric. In addition, the proposed method significantly reduces computational complexity compared with the FDC scheme and an ML estimation scheme
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