Learning and Shifts in Long-Run Growth

Shifts in the long-run rate of productivity growth--such as those those experienced by the U.S. economy in the 1970s and 1990s--are difficult in real time to distinguish from transitory fluctuations. In this paper, we explore how economists' projections of trend productivity growth gradually evolved during the 1970s and 1990s, and examine the consequences of such real-time learning on the dynamic responses to shifts in trend growth in the context of a dynamic stochastic general equilibrium model. We find that a simple updating rule based on an estimated Kalman filter model using real-time data describes the evolution of economists' long-run growth expectations extremely well. We then show that incorporating learning in this fashion has profound implications for the dynamic effects of shifts in trend productivity growth, whether they are concentrated in the investment-goods sector or affect the entire economy. If immediately recognized, increases in the trend growth rate cause long-term interest rates to rise and produce a sharp decline in employment and investment. In contrast, with learning, a productivity acceleration sets off a sustained boom in employment and investment, and long-term interest rates rise only gradually in a pattern consistent with the experience of the 1990sKalman filter, real-time data, signal extraction

Science’s Harmful Power

The focus of this thesis is to address and acknowledge issues identifying how applied science’s progressive impact can harm people in any society. The advancement of scientific technology can cause detrimental results to the general public. A few examples are dropping of the atomic bomb; prescription medications dispensed to patients before adequate testing studies have been completed; and scientific fraud. The scientific community promotes the scientist based on their research without thoroughly testing the theory or discovery. The scientist will go to extreme lengths to achieve specific results can cause damaging effects on society. Scientists can falsely influence society and gain the public’s trust. The underlying reason behind these issues may emanate from the scientist’s lack of human values when implementing a theory. Does “science” contribute and demonstrate to Western society only as a positive outcome? Do scientists’ discoveries only generate positive results when they are utilizing the effects on the general public? The purpose of this study is to acknowledge society’s perpetual faith in science and scientist’s theory. This thesis will not uncover the truth, because each individual views the “truth” differently. Rather will concentrate on how Western society views the truth and their trust in the scientist, physicians, and scientific community

Integrative Review on the Initiation of a Systematic Oral Care Education Program

Hospital-acquired infections, such as non-ventilated hospital-acquired pneumonia (NVHAP), are significant patient safety concerns. The lack of oral care in the non-ventilated acute care patient population is a significant contributor to NVHAP. The nursing staff is on the front line in providing oral care to hospitalized patients but often lacks knowledge and understanding regarding the relationship between oral care and NVHAP. Searching MEDLINE, PubMed, CINAHL, and EBSCO databases for current, peer-reviewed, scholarly articles for English language articles between 2010-2020 led to supporting findings. These findings revealed missed oral care as the most common modifiable risk factor contributing to increased hospital-acquired pneumonia incidences. Other findings revealed the impact an evidence-based educational program would have on oral care and outcomes. The influence of the learner\u27s engagement with education and training has proven to have substantial benefits

Welfare-maximizing monetary policy under parameter uncertainty

This paper examines welfare-maximizing monetary policy in an estimated micro-founded general equilibrium model of the U.S. economy where the policymaker faces uncertainty about model parameters. Uncertainty about parameters describing preferences and technology implies not only uncertainty about the dynamics of the economy. It also implies uncertainty about the model's utility-based welfare criterion and about the economy's natural rate measures of interest and output. We analyze the characteristics and performance of alternative monetary policy rules given the estimated uncertainty regarding parameter estimates. We find that the natural rates of interest and output are imprecisely estimated. We then show that, relative to the case of known parameters, optimal policy under parameter uncertainty responds less to natural-rate terms and more to other variables, such as price and wage inflation and measures of tightness or slack that do not depend on natural rates.Monetary policy

Potential for regional air pollution episodes in Colorado

November 1983.Includes bibliographical references.Sponsored by the National Science Foundation ATM-8015309

Optimum design and control of amine scrubbing in response to electricity and CO2 prices

AbstractThis paper presents steady state and dynamic modelling of post combustion CO2 capture using 30 wt% MEA integrated with models of CO2 compression and the steam power cycle. It uses multivariable optimization tools to maximize hourly profit of a 100 MWe coal-fired power plant. Steady state optimization for design provided optimum lean loading and CO2 removal as a function of price ratio (CO2 price/electricity price). The results indicated that for price ratio between 2.1 and 7, the plant should be designed at removal between 70% and 98% and lean loading in the range of 0.22–0.25. Dynamic optimization determined the operation of the capture system in response to two partial load scenarios (reboiler steam load reduction and power plant boiler load reduction) and provided optimum set points for steam rate, solvent circulation rate and stripper pressure control loops. Maximum profit is maintained by allowing the stripper pressure to drop and implementing a ratio control between solvent and steam rate (and flue gas rate for partial boiler load operation)

Using an Interaction Parameter in Model-Based Phase I Trials for Combination Treatments? A Simulation Study.

There is growing interest in Phase I dose-finding studies studying several doses of more than one agent simultaneously. A number of combination dose-finding designs were recently proposed to guide escalation/de-escalation decisions during the trials. The majority of these proposals are model-based: a parametric combination-toxicity relationship is fitted as data accumulates. Various parameter shapes were considered but the unifying theme for many of these is that typically between 4 and 6 parameters are to be estimated. While more parameters allow for more flexible modelling of the combination-toxicity relationship, this is a challenging estimation problem given the typically small sample size in Phase I trials of between 20 and 60 patients. These concerns gave raise to an ongoing debate whether including more parameters into combination-toxicity model leads to more accurate combination selection. In this work, we extensively study two variants of a 4-parameter logistic model with reduced number of parameters to investigate the effect of modelling assumptions. A framework to calibrate the prior distributions for a given parametric model is proposed to allow for fair comparisons. Via a comprehensive simulation study, we have found that the inclusion of the interaction parameter between two compounds does not provide any benefit in terms of the accuracy of selection, on average, but is found to result in fewer patients allocated to the target combination during the trial