Regression Analysis: A Constructive Critique identifies a wide variety of problems with regression analysis as it is commonly used and then provides a number of ways in which practice could be improved.
First some terminology is explained. Then the interpretations of the coefficients and constants of the function are discussed. Afterwards the zero conditional mean assumption regarding the residual is problematized. Lastly, a graphical representation of a regression line is given and the least sum of squared errors is introduced and the equation for the coefficient of the linear function as well as for the intercept is given.
This course introduces the main topics in Econometrics by using R statistical software. The relation of themes is comprehensive and includes the basic notions such as linear regression, multiple regression, causal inference, regression discontinuity and instrumental variable. In total, the course covers thirteen chapters that are common in any undergraduate econometrics course.
First some properties about the Sum of squared residuals and the linear regression function are restated. In particular three properties that an ideal fitted regression line must fulfill are discussed. Then, the R squared is defined using the measures of the Sum of squared residuals, the total sum of squares and the sum of explained squares.
First some definitions regarding econometrics, regressions, types of data and independent and dependent variables are given. Then the basic function of a simple regression analysis is explained. Lastly, there is discussion of the meaning of the error term.
There are three things one can do in this website - 1. Learn 2. Help Teach 3. Sign up MOOC. This is a semester-long graduate course in Econometrics. This course is intended for graduate students in economics-related fields and more generally in social sciences. The course includes an overview of the models and theory and applications using Stata, R, or SAS programs. This econometrics class covers about 15 of the most commonly used econometric models in economics, such as linear regression, panel data models, probit and logit models, limited dependent variable models, count data models, time series models, and many more.
How do people make decisions? There is a class of models in psychology which seek to answer this question but have received scant attention in economics despite some clear empirical successes. In a previous post I discussed one of these, Decision by Sampling, and this post will look at another: the so-called Fast and Frugal heuristics pioneered by the German psychologist Gerd Gigerenzer. Here the individual seeks out sufficient information to make a reasonable decision. They are ‘fast’ because they do not require massive computational effort to make a decision so can be done in seconds, and they are ‘frugal’ because they use as little information as possible to make the decision effectively.
Getting to the policy discussion table is one of the objectives pursued by feminist scholars and advocates. However, some participants in this process have remarked that “you cannot get to the policy discussion table until you have proven that you can crunch the numbers.”
Here we look at the effect of the 2008 Climate Change Act passed in Parliament in the United Kingdom as an effort to curb emissions in all sectors. The Act aside from setting goals to become a low-carbon economy sets up an independent committee on Climate Change to ensure the implementation of policies to comply with the ultimate goal of 80% reduction in total emissions in 2050. I make use of the Synthetic Control Method (SCM) to create a comparative case study in which the creation of a synthetic UK serves as a counterfactual where the treatment never occurred (Cunningham, 2018).
The core of Georgism is a policy known as the Land Value Tax (LVT), a policy which Georgists claim will solve many of society and the economy’s ills. Georgism is an interesting school of thought because it has the twin properties that (1) despite a cult following, few people in either mainstream or (non-Georgist) heterodox economics pay it much heed; (2) despite not paying it much heed, both mainstream and heterodox economists largely tend to agree with Georgists. I will focus on the potential benefits Georgists argue an LVT will bring and see if they are borne out empirically. But I will begin by giving a nod to the compelling theoretical and ethical dimensions of George’s analysis, which are impossible to ignore.
Economic sociology is an entire subfield and one could write an series on it, so I’m going to stick to probably the most prominent economic sociologist and the founder of ‘new economic sociology’, Mark Granovetter.
If there’s one method economists have neglected the most, it’s qualitative research. Whereas economists favour mathematical models and statistics, qualitative research seeks to understand the world through intensive investigation of particular circumstances, which usually entails interviewing people directly about their experiences. While this may sound simple to quantitative types the style, purpose, context, and interpretation of an interview can vary widely. Because of this variety, I have written a longer post than usual on this topic rather than doing it a disservice. Having said that, examples of qualitative research in economics are sadly scant enough that it doesn’t warrant multiple posts. In this post I will introduce qualitative research in general with nods to several applications including the study of firm behaviour, race, Austrian economics, and health economics. More than usual I will utilise block quotes, which I feel is in the spirit of the topic.
Stratification economics is defined as a systemic and empirically grounded approach to addressing intergroup inequality. Stratification economics integrates economics, sociology and social psychology to distinctively analyze inequality across groups that are socially differentiated, be it by race, ethnicity, gender, caste, sexuality, religion or any other social differentiation.
This course describes Bayesian statistics in which one s inferences about parameters or hypotheses are updated as evidence accumulates You will learn to use Bayes rule to transform prior probabilities into posterior probabilities and be introduced to the underlying theory and perspective of the Bayesian paradigm The course will apply …
Evolutionary economics focuses on economic change. Hence processes of change such as growth, innovation, structural and technological change, as well as economic development in general are analysed. Evolutionary economics often gives emphasis to populations and (sub-)systems.
The goal of this course is to explore these differences in economic outcomes observed among women and men, measured by such things as earnings, income, hours of work, poverty, and the allocation of resources within the household. It will evaluate women’s perspectives and experiences in the United States and around the world, emphasizing feminist economics.
This statistics and data analysis course will introduce you to the essential notions of probability and statistics We will cover techniques in modern data analysis estimation regression and econometrics prediction experimental design randomized control trials and A B testing machine learning and data visualization We will illustrate these concepts with …
There are three things one can do on this website - 1. Learn 2. Help Teach 3. Sign up for the MOOC.
Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance.