R&D? A Small Contribution to Productivity Growth

In this paper I evaluate the contribution of R&D investments to productivity growth. The basis for the analysis are the free entry condition and the fact that most R&D innovations are embodied. Free entry yields a relationship between the resources devoted to R&D and the growth rate of technology. Since innovators are small, this relationship is not directly affected by the size of the R&D externalities, or the presence of aggregate diminishing returns in R&D after controlling for the growth rate of output and the interest rate. The embodiment of R&D- driven innovations bounds the size of the production externalities. The resulting contribution of R&D to productivity growth in the US is smaller than three to five tenths of one percentage point. This constitutes an upper bound for the case where innovators internalize the consequences of their R&D investments on the cost of conducting future innovations. From a normative perspective, this analysis implies that, if the innovation technology takes the form assumed in the literature, the actual US R&D intensity may be the socially optimal.Research and Development, productivity growth, total factor productivity

Specific factors meet intermediate inputs: implications for strategic complementarities and persistence.

A central challenge to monetary business-cycle theory is to find a solution to the problem of persistence and delay in the real effects of monetary shocks. Previous research has identified separately specific factors and intermediate inputs as two promising mechanisms for generating the persistence and delay in a staggered price-setting framework. Models based on either of these two mechanisms have also been used in the design of optimal monetary policy. ; By examining a staggered price model that features both specific factors and intermediate inputs, the author finds an offsetting interaction between the two individually promising mechanisms, which leads to a cancellation of much of the impact of each in propagating monetary shocks. This finding posits a challenge to the search for a robust monetary transmission mechanism and design of optimal monetary policy.Business cycles

What does a technology shock do? A VAR analysis with model-based sign restrictions

This paper estimates the effects of technology shocks in VAR models of the U.S., identified by imposing restrictions on the sign of impulse responses. These restrictions are consistent with the implications of a popular class of DSGE models, with both real and nominal frictions, and with sufficiently wide ranges for their parameters. This identification strategy thus substitutes theoretically-motivated restrictions for the atheoretical assumptions on the time-series properties of the data that are key to long-run restrictions. Stochastic technology improvements persistently increase real wages, consumption, investment and output in the data; hours worked are very likely to increase, displaying a hump-shaped pattern. Contrary to most of the related VAR evidence, results are not sensitive to a number of specification assumptions, including those on the stationarity properties of variables.technology shocks, DSGE models, bayesian VAR methods, identification

Thomas J. Sargent and Christopher A. Sims: Empirical Macroeconomics

One of the main tasks for macroeconomists is to explain how macroeconomic aggregates -such as GDP, investment, unemployment, and inflation- behave over time. How are these variables affected by economic policy and by changes in the economic environment? A primary aspect in this analysis is the role of the central bank and its ability to influence the economy. How effective can monetary policy be in stabilizing unwanted fluctuations in macroeconomic aggregates? How effective has it been historically? Similar questions can be raised about fiscal policy. Thomas J. Sargent and Christopher A. Sims have developed empirical methods that can answer these kinds of questions. This year's prize recognizes these methods and their successful application to the interplay between monetary and fi scal policy and economic activity.Causation; macroeconomics

Inventories, Inflation Dynamics and the New Keynesian Phillips Curve

We introduce inventories into an otherwise standard New Keynesian model and study the implications for in.ation dynamics. Inventory holdings are motivated as a means to generate sales for demand-constrained .rms. We derive various representa- tions of the New Keynesian Phillips curve with inventories and show that one of these speci.cations is observationally equivalent to the standard model with respect to the behavior of in.ation when the model.s cross-equation restrictions are imposed. How- ever, the driving variable in the New Keynesian Phillips curve - real marginal cost - is unobservable and has to be proxied by, for instance, unit labor costs. An alternative approach is to impute marginal cost by using the model.s optimality conditions. We show that the stock-sales ratio is linked to marginal cost. We also estimate these various speci.cations of the New Keynesian Phillips curve using GMM. We .nd that predictive power of the inventory-speci.cation at best approaches that of the standard model, but does not improve upon it. We conclude that inventories do not play a role in explaining in.ation dynamics within our New Keynesian Phillips curve framework.

MIncreasing Market Interconnection: an analysis of the Italian Electricity Spot Market

We estimate the bene ts resulting from completely interconnecting the Italian electricity spot market. The�market is currently divided into two geographic zones - North and South - with limited interzonal transmission capacity that often induces congestion, and hence potential inefficiency. By simulating a fully interconnected market for May 2004, we predict that the total spot market expenditure reduces substantially by almost four percent. Our analysis finds evidence that the (partly State owned) major firm in the market does not currently maximize its short-term profit, and would benefit as well from improved interconnection.Transmission constraints,self-regulated monopoly,zonal pricing,congestion

Memory-Aware Scheduling for Fixed Priority Hard Real-Time Computing Systems

As a major component of a computing system, memory has been a key performance and power consumption bottleneck in computer system design. While processor speeds have been kept rising dramatically, the overall computing performance improvement of the entire system is limited by how fast the memory can feed instructions/data to processing units (i.e. so-called memory wall problem). The increasing transistor density and surging access demands from a rapidly growing number of processing cores also significantly elevated the power consumption of the memory system. In addition, the interference of memory access from different applications and processing cores significantly degrade the computation predictability, which is essential to ensure timing specifications in real-time system design. The recent IC technologies (such as 3D-IC technology) and emerging data-intensive real-time applications (such as Virtual Reality/Augmented Reality, Artificial Intelligence, Internet of Things) further amplify these challenges. We believe that it is not simply desirable but necessary to adopt a joint CPU/Memory resource management framework to deal with these grave challenges. In this dissertation, we focus on studying how to schedule fixed-priority hard real-time tasks with memory impacts taken into considerations. We target on the fixed-priority real-time scheduling scheme since this is one of the most commonly used strategies for practical real-time applications. Specifically, we first develop an approach that takes into consideration not only the execution time variations with cache allocations but also the task period relationship, showing a significant improvement in the feasibility of the system. We further study the problem of how to guarantee timing constraints for hard real-time systems under CPU and memory thermal constraints. We first study the problem under an architecture model with a single core and its main memory individually packaged. We develop a thermal model that can capture the thermal interaction between the processor and memory, and incorporate the periodic resource sever model into our scheduling framework to guarantee both the timing and thermal constraints. We further extend our research to the multi-core architectures with processing cores and memory devices integrated into a single 3D platform. To our best knowledge, this is the first research that can guarantee hard deadline constraints for real-time tasks under temperature constraints for both processing cores and memory devices. Extensive simulation results demonstrate that our proposed scheduling can improve significantly the feasibility of hard real-time systems under thermal constraints

The Role of Consumer's Risk Aversion on Price Rigidity

This paper aims to contribute to the research agenda on the sources of price rigidity. Based on broadly accepted assumptions on the behavior of economic agents, we show that firms’ competition can lead to the adoption of sticky prices as a sub-game perfect equilibrium strategy to optimally deal with consumers’ risk aversion, even if firms have no adjustment costs. To this end, we build a model economy based on consumption centers with several complete markets and relax some traditional assumptions used in standard monetary policy models by assuming that households have imperfect information about the inefficient time-varying cost shocks faced by the .rms. Furthermore, we assume that the timing of events is such that, at every period, consumers have access to the actual prices prevailing in the market only after choosing a particular consumption center. Since such choices under uncertainty may decrease the expected utilities of risk-averse consumers, competitive firms adopt some degree of price stickiness in order to minimize the price uncertainty and "attract more customers".

A study of the chemosynthetic gas exchanger Quarterly progress report, 24 Sep. 1968- 22 Jan. 1969

Efficiency of growth by hydrogen bacteria and growth rate limiting factors on efficiency of energy conversio

Lower Bounds on Approximation Errors to Numerical Solutions of Dynamic Economic Models

We propose a novel methodology for evaluating the accuracy of numerical solutions to dynamic economic models. It consists in constructing a lower bound on the size of approximation errors. A small lower bound on errors is a necessary condition for accuracy: If a lower error bound is unacceptably large, then the actual approximation errors are even larger, and hence, the approximation is inaccurate. Our lower‐bound error analysis is complementary to the conventional upper‐error (worst‐case) bound analysis, which provides a sufficient condition for accuracy. As an illustration of our methodology, we assess approximation in the first‐ and second‐order perturbation solutions for two stylized models: a neoclassical growth model and a new Keynesian model. The errors are small for the former model but unacceptably large for the latter model under some empirically relevant parameterizations.Lilia Maliar and Serguei Maliar acknowledge support from the Hoover Institution and Department of Economics at Stanford University, Santa Clara University, Ivie, University of Alicante, and the MINECO/FEDER Grant ECO2015-70540-P