Why Hasn’t the US Economic Stimulus Been More Effective? The Debate on Tax and Expenditure Multipliers

Recent dissatisfaction with the impact of expenditure stimulus on economic activity in the United States, along with the results of academic research, have once again raised questions about the effectiveness of fiscal stimulus policies and about whether stimulus to a recessionary economy should be in the form of tax cuts or expenditure increases. This paper considers alternative methods for evaluating the impacts of stimulus policy strategies. We discuss conceptual challenges involved in effectiveness measurement, and we review alternative empirical approaches applied in recent studies. We then present our own estimates of policy multipliers based on simulations of the IHS Global Insight model of the US economy. Based on this review and analysis, we address the question of why recent US stimulus programs have not been more effective.United States (US) recession and recovery; fiscal and monetary policy; tax and expenditure multipliers; econometric model forecast simulation.

Minimum-Information LQG Control - Part I: Memoryless Controllers

With the increased demand for power efficiency in feedback-control systems, communication is becoming a limiting factor, raising the need to trade off the external cost that they incur with the capacity of the controller's communication channels. With a proper design of the channels, this translates into a sequential rate-distortion problem, where we minimize the rate of information required for the controller's operation under a constraint on its external cost. Memoryless controllers are of particular interest both for the simplicity and frugality of their implementation and as a basis for studying more complex controllers. In this paper we present the optimality principle for memoryless linear controllers that utilize minimal information rates to achieve a guaranteed external-cost level. We also study the interesting and useful phenomenology of the optimal controller, such as the principled reduction of its order

Interest Rate Rules, Inflation Stabilization, and Imperfect Credibility: The Small Open Economy Case

The paper examines the robustness of Interest Rate Rules, IRRs, in the context of an imperfectly credible stabilization program, closely following the format of much of the literature in open-economy models, e.g., Calvo and Végh (1993 and 1999). A basic result is that IRRs, like Exchange Rate Based Stabilization, ERBS, programs, could give rise to macroeconomic distortion, e.g., underutilization of capacity and real exchange rate misalignment. However, while under imperfect credibility EBRS is associated with overheating and current account deficits, IRRs give rise to somewhat opposite results. Moreover, the paper shows that popular policies to counteract misalignment, like Strategic Foreign Exchange Market Intervention or Controls on International Capital Mobility may not be effective or could even become counterproductive. The bottom line is that the greater exchange rate flexibility granted by IRRs is by far not a sure shot against the macroeconomic costs infringed by imperfect credibility.

Booms and Busts: New Keynesian and Behavioral Explanations

Capitalism is characterized by booms and busts. Periods of strong growth in output alternate with periods of declines in economic growth. Every macro-economic theory should attempt to explain these endemic business cycle movements. In this paper I present two paradigms that attempt to explain these booms and busts. One is the DSGE-paradigm in which agents have unlimited cognitive abilities. The other paradigm is a behavioural one in which agents are assumed to have limited cognitive abilities. These two types of models produce a radically different macroeconomic dynamics. I analyze these differences. I also study the different policy implications of these two paradigms.DSGE-model, imperfect information, heuristics, animal spirits

SELECTIVE CREDIT CONTROLS AND THE MONEY SUPPLY PROCESS IN TRANSITIONAL ECONOMIES: THE CASE OF BULGARIA

This paper develops an unified framework for analyzing the influence of both direct and indirect instruments of monetary control on the money supply process. The resulting formal model is then applied in the empirical evaluation of the effectiveness of credit ceilings in limiting the growth of domestic monetary aggregates in Bulgaria.selective credit controls credit ceilings money multiplier

Decomposition Algorithms in Stochastic Integer Programming: Applications and Computations.

In this dissertation we focus on two main topics. Under the first topic, we develop a new framework for stochastic network interdiction problem to address ambiguity in the defender risk preferences. The second topic is dedicated to computational studies of two-stage stochastic integer programs. More specifically, we consider two cases. First, we develop some solution methods for two-stage stochastic integer programs with continuous recourse; second, we study some computational strategies for two-stage stochastic integer programs with integer recourse. We study a class of stochastic network interdiction problems where the defender has incomplete (ambiguous) preferences. Specifically, we focus on the shortest path network interdiction modeled as a Stackelberg game, where the defender (leader) makes an interdiction decision first, then the attacker (follower) selects a shortest path after the observation of random arc costs and interdiction effects in the network. We take a decision-analytic perspective in addressing probabilistic risk over network parameters, assuming that the defender\u27s risk preferences over exogenously given probabilities can be summarized by the expected utility theory. Although the exact form of the utility function is ambiguous to the defender, we assume that a set of historical data on some pairwise comparisons made by the defender is available, which can be used to restrict the shape of the utility function. We use two different approaches to tackle this problem. The first approach conducts utility estimation and optimization separately, by first finding the best fit for a piecewise linear concave utility function according to the available data, and then optimizing the expected utility. The second approach integrates utility estimation and optimization, by modeling the utility ambiguity under a robust optimization framework following \cite{armbruster2015decision} and \cite{Hu}. We conduct extensive computational experiments to evaluate the performances of these approaches on the stochastic shortest path network interdiction problem. In third chapter, we propose partition-based decomposition algorithms for solving two-stage stochastic integer program with continuous recourse. The partition-based decomposition method enhance the classical decomposition methods (such as Benders decomposition) by utilizing the inexact cuts (coarse cuts) induced by a scenario partition. Coarse cut generation can be much less expensive than the standard Benders cuts, when the partition size is relatively small compared to the total number of scenarios. We conduct an extensive computational study to illustrate the advantage of the proposed partition-based decomposition algorithms compared with the state-of-the-art approaches. In chapter four, we concentrate on computational methods for two-stage stochastic integer program with integer recourse. We consider the partition-based relaxation framework integrated with a scenario decomposition algorithm in order to develop strategies which provide a better lower bound on the optimal objective value, within a tight time limit

Designing the Liver Allocation Hierarchy: Incorporating Equity and Uncertainty

Liver transplantation is the only available therapy for any acute or chronic condition resulting in irreversible liver dysfunction. The liver allocation system in the U.S. is administered by the United Network for Organ Sharing (UNOS), a scientific and educational nonprofit organization. The main components of the organ procurement and transplant network are Organ Procurement Organizations (OPOs), which are collections of transplant centers responsible for maintaining local waiting lists, harvesting donated organs and carrying out transplants. Currently in the U.S., OPOs are grouped into 11 regions to facilitate organ allocation, and a three-tier mechanism is utilized that aims to reduce organ preservation time and transport distance to maintain organ quality, while giving sicker patients higher priority. Livers are scarce and perishable resources that rapidly lose viability, which makes their transport distance a crucial factor in transplant outcomes. When a liver becomes available, it is matched with patients on the waiting list according to a complex mechanism that gives priority to patients within the harvesting OPO and region. Transplants at the regional level accounted for more than 50% of all transplants since 2000.This dissertation focuses on the design of regions for liver allocation hierarchy, and includes optimization models that incorporate geographic equity as well as uncertainty throughout the analysis. We employ multi-objective optimization algorithms that involve solving parametric integer programs to balance two possibly conflicting objectives in the system: maximizing efficiency, as measured by the number of viability adjusted transplants, and maximizing geographic equity, as measured by the minimum rate of organ flow into individual OPOs from outside of their own local area. Our results show that efficiency improvements of up to 6% or equity gains of about 70% can be achieved when compared to the current performance of the system by redesigning the regional configuration for the national liver allocation hierarchy.We also introduce a stochastic programming framework to capture the uncertainty of the system by considering scenarios that correspond to different snapshots of the national waiting list and maximize the expected benefit from liver transplants under this stochastic view of the system. We explore many algorithmic and computational strategies including sampling methods, column generation strategies, branching and integer-solution generation procedures, to aid the solution process of the resulting large-scale integer programs. We also explore an OPO-based extension to our two-stage stochastic programming framework that lends itself to more extensive computational testing. The regional configurations obtained using these models are estimated to increase expected life-time gained per transplant operation by up to 7% when compared to the current system.This dissertation also focuses on the general question of designing efficient algorithms that combine column and cut generation to solve large-scale two-stage stochastic linear programs. We introduce a flexible method to combine column generation and the L-shaped method for two-stage stochastic linear programming. We explore the performance of various algorithm designs that employ stabilization subroutines for strengthening both column and cut generation to effectively avoid degeneracy. We study two-stage stochastic versions of the cutting stock and multi-commodity network flow problems to analyze the performances of algorithms in this context