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The Efficient Mart Stocket - Forbes
Accelerated Data and Decisions is a first-year MBA course in statistics and regression analysis. The course is taught using a flipped classroom model that combines extensive online materials with a more lab-based classroom approach. Traditional lecture content will be learned through online videos, simulations, and exercises, while time spent in the classroom will be discussions, problem solving, or computer lab sessions. Content covered includes sampling techniques, hypothesis testing, t-tests, linear regression, and prediction models. The group regression project is a key component of the course, and all students will learn the statistical software package R. The accelerated course is designed for students with strong quantitative backgrounds. Students taking this course need to be comfortable with mathematical notation, algebra, and basic probability. Students without quantitative backgrounds should consider enrolling in the base version of the course.
Same as: Flipped Classroom
This course prepares the student to do empirical behavioral research. It will cover all aspects of the research process, from hypothesis generation to experimental design to data analysis to writing up your results and submitting them for publication.
Migrant Worker Remittances,Micro-finance and the …
This is the base version of D&D. This course introduces the fundamental concepts and techniques for analyzing risk and formulating sound decisions in uncertain environments. Approximately half of the course focuses on probability and its application. The remainder of the course examines statistical methods for interpreting and analyzing data including sampling concepts, regression analysis, and hypothesis testing. Applications include inventory management, demand analysis, portfolio analysis, surveys and opinion polls, A/B testing, environmental contamination, online advertising and the role of analytics in business settings more generally. The course emphasizes analytical techniques and concepts that are broadly applicable to business problems.
Base Data and Decisions is a first-year MBA course in statistics and regression analysis. The course is taught using a flipped classroom model that combines extensive online materials with a lab-based classroom approach. Traditional lecture content will be learned through online videos, simulations, and exercises, while time spent in the classroom will be discussions, problem solving, or computer lab sessions. Content covered includes basic probability, sampling techniques, hypothesis testing, t-tests, linear regression, and prediction models. The group regression project is a key component of the course, and all students will learn the statistical software package R.
Same as: Flipped Classroom
A new database on actual and equilibrium exchange …
This paper examines the emerging challenges to the art of monetary policymaking using the case study of the Reserve Bank of India (RBI) in light of developments in the Indian economy during the last decade (2003–04 to 2013–14). The paper uses Hyman P. Minsky’s financial instability hypothesis as the conceptual framework for evaluating the endogenous nature of financial instability and its potential impact on monetary policymaking, and addresses the need to pursue regulatory policy as a tool that is complementary to monetary policy in light of the agenda of reforms put forward by Minsky. It further reviews the extensions to the Minskyan hypothesis in the areas of setting fiscal policy, managing cross-border capital flows, and developing financial institutional infrastructure. The lessons learned from the interplay of policy choices in these areas and their impact on monetary policymaking at the RBI are presented.
Since the beginning of the fall of monetarism in the mid-1980s, mainstream macroeconomics has incorporated many of the principles of post-Keynesian endogenous money theory. This paper argues that the most important critical component of post-Keynesian monetary theory today is its rejection of the “natural rate of interest.” By examining the hidden assumptions of the loanable funds doctrine as it was modified in light of the idea of a natural rate of interest—specifically, its implicit reliance on an “efficient markets hypothesis” view of capital markets—this paper seeks to show that the mainstream view of capital markets is completely at odds with the world of fundamental uncertainty addressed by post-Keynesian economists, a world in which Keynesian liquidity preference and animal spirits rule the roost. This perspective also allows us to shed new light on the debate that has sprung up around the work of Hyman Minsky, calling into question to what extent he rejected the loanable funds view of financial markets. When Minsky’s theories are examined against the backdrop of the natural rate of interest version of the loanable funds theory, it quickly becomes clear that Minsky does not fall into the loanable funds camp.
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Real Estate Bubble and Housing Market Indicators
This course will cover statistical methods based on the machine learning literature that can be used for causal inference. In economics and the social sciences more broadly, empirical analyses typically estimate the effects of counterfactual policies, such as the effect of implementing a government policy, changing a price, showing advertisements, or introducing new products. Recent advances in supervised and unsupervised machine learning provide systematic approaches to model selection and prediction, methods that are particularly well suited to datasets with many observations and/or many covariates. This course will review when and how machine learning methods can be used for causal inference, and it will also review recent modifications and extensions to standard methods to adapt them to causal inference and provide statistical theory for hypothesis testing. We consider the estimation of average treatment effects as well as personalized policies. Applications to the evaluation of large-scale experiments, including online A/B tests and experiments on networks, will receive special attention.
Housing Market Indicators, House Pricing and The Housing Bubble
Poverty status is an important factor influencing household production and the unpaid work time associated with it due to the role of household production as a coping strategy in mitigating the impact of economic downturns. In this paper, we examine the presence of poverty-based asymmetries in the unpaid work time changes of men and women during the Great Recession. Using the 2003–12 American Time Use Survey, we find that these changes indeed varied by poverty status. In particular, nonpoor women drove the reduction in unpaid work time among women. Among men, the lack of the change in unpaid work time masked the increase in poor men’s time and the decrease in nonpoor men’s time. Oaxaca-Blinder decompositions of the changes in the unpaid work time reveal that shifts in own and spousal employment status largely account for the gender-based differences in these changes, while shifts in the household structure partially explain the poverty-based differences. Nevertheless, sizable portions of the changes in time use remain unexplained by the shifting individual and household characteristics. The latter finding supports the hypothesis of poverty-based variation in the unpaid work time adjustments in that poor and nonpoor individuals appeared to have responded to the recession in different ways.
Monetary Policy in Nigeria - ArticlesNG
Recent episodes of housing bubbles, which occurred in several economies after the burst of the United States housing market, suggest studying the evolution of housing prices from a global perspective. We utilize a theoretical model for the purposes of this contribution, which identifies the main drivers of housing price appreciation—for example, income, residential investment, financial elements, fiscal policy, and demographics. In the second stage of our analysis, we test our theoretical hypothesis by means of a sample of 18 Organisation for Economic Co-operation and Development (OECD) countries from 1970 to 2011. We employ the vector error correction econometric technique in terms of our empirical analysis. This allows us to model the long-run equilibrium relationship and the short-run dynamics, which also helps to account for endogeneity and reverse-causality problems.
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