Econometric Modeling and Evaluation of Fiscal-Monetary Policy Interactions

Thesis (Ph.D.) - Indiana University, Economics, 2015How do fiscal and monetary policies interact to determine inflation? The conventional view rests on the Taylor principle, that central banks can control inflation by raising nominal interest rate more than one-for-one with inflation. This principle embeds an implicit assumption that the government always adjusts taxes or spending to assure fiscal solvency. But when the required fiscal adjustments are not assured, as may occur during periods of fiscal stress, monetary policy may no longer be able to determine inflation. Under this alternative view, policy roles are reversed, with fiscal policy determining the price level and monetary policy acting to stabilize debt. Because these two policy regimes imply starkly different policy advice, identifying the prevailing regime is a prerequisite to understanding the macro economy and to making good policy choices. This dissertation employs econometric modeling and evaluation techniques to examine the empirical implications of the dynamic interactions between post-war U.S. fiscal and monetary policies. Chapter One compares two econometric interpretations of a dynamic macro model designed to study U.S. policy interactions. Two main findings emerge. First, the data overwhelmingly support the conventional view of inflation determination under the prevailing, "strong" econometric interpretation that takes literally all of the model's implications for the data. But this result is susceptible to any potential model misspecification. Second, according to the alternative, "minimal" econometric interpretation that is immune to the difficulties with the strong interpretation, the two views of inflation determination can explain the data about equally well. These findings imply that the apparent statistical support in favor of the conventional view over the alternative in the literature stems largely from the strong interpretation rather than from compelling empirical evidence. Therefore, a prudent policymaker should broaden her perspective beyond any single view on the inflation process. Chapter Two, joint with Todd B. Walker, develops an analytic function approach to solving generalized multivariate linear rational expectations models. This solution method is shown to provide important insights into equilibrium dynamics of well-known models. Chapter Three further demonstrates the usefulness of this method via a conventional new Keynesian model

Traffic congestion in interconnected complex networks

Traffic congestion in isolated complex networks has been investigated extensively over the last decade. Coupled network models have recently been developed to facilitate further understanding of real complex systems. Analysis of traffic congestion in coupled complex networks, however, is still relatively unexplored. In this paper, we try to explore the effect of interconnections on traffic congestion in interconnected BA scale-free networks. We find that assortative coupling can alleviate traffic congestion more readily than disassortative and random coupling when the node processing capacity is allocated based on node usage probability. Furthermore, the optimal coupling probability can be found for assortative coupling. However, three types of coupling preferences achieve similar traffic performance if all nodes share the same processing capacity. We analyze interconnected Internet AS-level graphs of South Korea and Japan and obtain similar results. Some practical suggestions are presented to optimize such real-world interconnected networks accordingly.Comment: 8 page

Workforce-Planning-and-Development.pdf

This paper includes a survey of different training courses and resources available to councils, and outlines some key elements which might form part of any programme on workforce planning and development

Enhancement Of Methodology And Definition For Serial Protocol Electrical Specification Compatibility For Non-Compliant Fpga

A new methodology and definition for a clearer compatibility for middle-range and low-end Field Programmable Gate Array (FPGA) transceiver is presented in this research. It has been a big challenge to balance between cost and performance in order to meet the full industrial protocol specification for middle-range and low-end FPGA. When the products are not able to achieve full protocol specification, the company will still market these products and claim that they are compatible. With this kind of situation, there are no guidelines can be followed; hence, users will not have confidence to design the product. In this research, a clearer compatible specification is obtained to provide channel loss requirement by extracting the timing margin at different test points. The research also ensures that quantifiable margin is allocated when compatible specification is defined. The middle-range FPGA that used in this research is Arria V GT device to define the new compatible specification with reference to IEEE 10GBASE-KR protocol where the compatible specification is used for board-based 10Gbps applications. The methodology used is by extracting the timing margin and find out the channel loss from 3 different test points which include Scenario 1) Transmitter is Arria V GT; Receiver is Compliant Receiver, Scenario 2) Transmitter and Receiver are from Arria V GT and Scenario 3) Transmitter is Compliant Transmitter; Receiver is from Arria V GT. 16-ps of timing margin is obtained from the research, while the channel loss for Scenario 1 is -16dB, Scenario 2 is -12dB and Scenario 3 is -17dB with error-free transfer for BER10-12

Word And Speaker Recognition System

In this report, a system which combines user dependent Word Recognition and text dependent speaker recognition is described. Word recognition is the process of converting an audio signal, captured by a microphone, to a word. Speaker Identification is the ability to recognize a person identity base on the specific word he/she uttered. A person's voice contains various parameters that convey information such as gender, emotion, health, attitude and identity. Speaker recognition identifies who is the speaker based on the unique voiceprint from the speech data. Voice Activity Detection (VAD), Spectral Subtraction (SS), Mel-Frequency Cepstrum Coefficient (MFCC), Vector Quantization (VQ), Dynamic Time Warping (DTW) and k-Nearest Neighbour (k-NN) are methods used in word recognition part of the project to implement using MATLAB software. For Speaker Recognition part, Vector Quantization (VQ) is used. The recognition rate for word and speaker recognition system that was successfully implemented is 84.44% for word recognition while for speaker recognition is 54.44%