Sloan School of Management https://hdl.handle.net/1721.1/1777 2020-11-28T13:29:52Z 2020-11-28T13:29:52Z Predicting the obvious : a machine learning approach to superstar inventions Raymond, Lindsey Rebecca. https://hdl.handle.net/1721.1/128608 2020-11-24T03:29:36Z 2019-01-01T00:00:00Z Predicting the obvious : a machine learning approach to superstar inventions Raymond, Lindsey Rebecca. While patent citations are a common way to measure innovative output, their use as a measure of invention quality involves a paradox. How can we use an ex-post measure of impact (the number of received citations) to identify the ex-ante quality of a given innovation? This paper proposes a novel method of measuring patent quality using patent text and sections of the patent citation distribution with the highest signal to noise ratio. We provide empirical evidence that the bias from using citations to measure quality varies by location in the patent distribution and, contrary to what one might expect, superstar patents are the most predictable while patents in the middle of the distribution are most contaminated with noise. We show predictability of patents increases monotonically over the patent distribution - with the most valuable being the most predictable - and removing the middle of the distribution has little impact on accuracy. We also provide suggestive evidence on the importance of patent text in measuring quality and conclude with suggestive geometric evidence we are capturing differences in underlying patent characteristics. As our model demonstrates, our empirical results generalize to other situations involving highly skewed processes observed with noise. This paper also has implications for empirical work using citation weighted metrics. Thesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, September, 2019; Cataloged from PDF of thesis.; Includes bibliographical references (pages 36-41). 2019-01-01T00:00:00Z Quantifying a fair labor value for garment production through prediction of factory efficiency Mourabet, Jawad E. https://hdl.handle.net/1721.1/128599 2020-11-24T03:04:07Z 2019-01-01T00:00:00Z Quantifying a fair labor value for garment production through prediction of factory efficiency Mourabet, Jawad E. Li&Fung is a world leader in logistics, sourcing, and procurement; connecting manufacturing vendors with major retail brands. Recently, there has been an increasing consumer demand for more sustainably produced and sourced garments. When sourcing a garment, costs such as materials, shipping, and taxes are widely understood, but there is a lot of ambiguity on labor's contribution to the cost breakdown. This ambiguity can lead to incorrect garment pricing which can result in factories using cost cutting measures in order to meet production agreements. This in turn can lead to poor production quality as well as adverse conditions to the health and wellbeing of workers. Through advancements in technology and data collection it has become possible to digitize a large portion of the sourcing process, allowing for a quantifiable approach to costing out labor. The primary goal of this thesis is to detail a methodology to quantify a fair labor value for new orders placed with Li&Fung. A fair labor wage, in accordance with the UN International Labor Organization (ILO), will satisfy the basic needs of workers and their families while providing some discretionary income. The resulting framework was used to create a model that leverages Li&Fung's internal supplier data currently being collected, as well as external environmental data that drives garment production efficiency. The model provides a standardized, intuitive method for both Li&Fung merchandisers and their customers to determine what a fair labor value is when producing a garment. Additionally, Li&Fung can leverage the tool as a method to attract new brands who are interested in offering fair trade products to their customers, but do not have the capacity or supply chain expertise to do so. Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, June, 2019; Thesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, in conjunction with the Leaders for Global Operations Program at MIT, June, 2019; Cataloged from the official PDF of thesis.; Includes bibliographical references (pages 65-66). 2019-01-01T00:00:00Z Essays in financial economics Montecinos Bravo, Alexis. https://hdl.handle.net/1721.1/128598 2020-11-24T03:26:05Z 2019-01-01T00:00:00Z Essays in financial economics Montecinos Bravo, Alexis. This thesis consists of three chapters on financial economics: an empirical analysis of government banks, a dynamic stochastic general equilibrium (DSGE) model with financial intermediaries, and a DSGE model for capital utilization and leverage. Chapter 1 presents an empirical analysis of government banks and their effect on aggregate economic variables, such as real per capita GDP growth and GDP growth volatility. It shows that government banks are still pervasive worldwide. I perform several regressions in order to estimate the effects of government banks on the economy and whether these effects are different from those found in previous studies. I find that the effect of state-owned banks is heterogeneous and ultimately depends on how deep the financial market is and how well the political institutions function in every country. Chapter 2 introduces a new DSGE model with heterogeneous households and heterogeneous financial intermediaries: private and government banks. Using empirical evidence about the stabilization role of state-owned banks during recessions, I show that the inclusion of these intermediaries in the aggregate can improve our understanding of the reaction of certain variables, such as GDP, employment, and consumption. I show that the final effect on these variables depends on how deep the financial market is and how important the level of inefficiency in government banks is. In Chapter 3, I document, together with Diogo Duarte and Hamilton Galindo, the relationship between capital utilization and leverage. We find a positive and significant relationship between these variables, which is especially strong between capital utilization and short-run debt. Using our empirical result, we develop a DSGE model to characterize the mechanism behind this relationship. We show that omitting capital utilization as a key mechanism in the business cycle can generate misleading conclusions. Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, September, 2019; Cataloged from PDF version of thesis. "The Table of Contents does not accurately represent the page numbering"--Disclaimer page.; Includes bibliographical references (pages 112-116). 2019-01-01T00:00:00Z Evaluation of scheduling strategies in a semiconductor wafer fab using simulation Ullo, Silvia Liberata. https://hdl.handle.net/1721.1/128456 2020-11-13T03:15:28Z 1992-01-01T00:00:00Z Evaluation of scheduling strategies in a semiconductor wafer fab using simulation Ullo, Silvia Liberata. Thesis (M.S.)--Massachusetts Institute of Technology, Sloan School of Management, 1992.; Includes bibliographical references (leaves 105-107). 1992-01-01T00:00:00Z 狠狠躁天天躁中文字幕_日韩欧美亚洲综合久久_漂亮人妻被中出中文字幕