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Dear Ms. Lock, Unfortunately I am still at my "hausarzt" office until 15:15 today filling out the chronic illness form that you sent me. I am sorry to postpone this appointment again, is it possible for me to send you the form via email today? Dear Ms. Lock, I am running approximately 20 minutes late due to my dr's appointment in zurich. apologies

Chapter 1 econometric basics Causal Effect Experimental Data Ceteris Paribus Nonexperimental Data Cross-Sectional Data Set Observational Data Data Frequency Panel Data Econometric Model Pooled Cross Section Economic Model Random Sampling Empirical Analysis Time Series Data chapter 2 simple regression model Coefficient of Determination Constant Elasticity Model Control Variable Covariate Degrees of Freedom Dependent Variable Elasticity Error Term (Disturbance) Error Variance Explained Sum of Squares (SSE) Explained Variable Explanatory Variable First Order Conditions Fitted Value Heteroskedasticity Homoskedasticity Independent Variable Intercept Parameter Ordinary Least Squares (OLS) OLS Regression Line Population Regression Function (PRF) Predicted Variable Predictor Variable Regressand Regression Through the Origin Regressor Residual Residual Sum of Squares (SSR) Response Variable R-squared Sample Regression Function (SRF) Semi-elasticity Si...

SUMMARY We have introduced the simple linear regression model in this chapter, and we have covered its basic properties. Given a random sample, the method of ordinary least squares is used to estimate the slope and intercept parameters in the population model. We have demonstrated the algebra of the OLS regression line, including computation of fitted values and residuals, and the obtaining of predicted changes in the dependent variable for a given change in the independent variable. In Section 2.4, we discussed two issues of practical importance: (1) the behavior of the OLS estimates when we change the units of measurement of the dependent variable or the independent variable; (2) the use of the natural log to allow for constant elasticity and constant semi-elasticity models. In Section 2.5, we showed that, under the four Assumptions SLR.1 through SLR.4, the OLS estimators are unbiased. The key assumption is that the error term u has zero mean given any value of the indepe...

In Chapter 2, we learned how to use simple regression analysis to explain a dependent variable, y, as a function of a single independent variable, x. The primary drawback in using simple regression analysis for empirical work is that it is very difficult to draw ceteris paribus conclusions about how x affects y: the key assumption, SLR.3—that all other factors affecting y are uncorrelated with x—is often unrealistic. Multiple regression analysis is more amenable to ceteris paribus analysis because it allows us to explicitly control for many other factors which simultaneously affect the dependent variable. This is important both for testing economic theories and for evaluating policy effects when we must rely on nonexperimental data. Because multiple regression models can accommodate many explanatory variables that may be correlated, we can hope to infer causality in cases where simple regression analysis would be misleading. Naturally, if we add more factors to our model tha...

The simple regression model can be used to study the relationship between two variables. For reasons we will see, the simple regression model has limitations as a general tool for empirical analysis. Nevertheless, it is sometimes appropriate as an empirical tool. Learning how to interpret the simple regression model is good practice for studying multiple regression, which we’ll do in subsequent chapters. 2.1 DEFINITION OF THE SIMPLE REGRESSION MODEL Much of applied econometric analysis begins with the following premise: y and x are two variables, representating some population, and we are interested in “explaining y in terms of x,” or in “studying how y varies with changes in x.”We discussed some examples in Chapter 1, including: y is soybean crop yield and x is amount of fertilizer; y is hourly wage and x is years of education; y is a community crime rate and x is number of police officers. In writing down a model that will “explain y in terms of x,” we must confront thre...

1.4CAUSALITY AND THE NOTION OF CETERIS PARIBUS IN ECONOMETRIC ANALYSIS In most tests of economic theory, and certainly for evaluating public policy, the economist’s goal is to infer that one variable has a causal effect on another variable (such as crime rate or worker productivity). Simply finding an association between two or more variables might be suggestive, but unless causality can be established, it is rarely compelling. The notion of ceteris paribus—which means “other (relevant) factors being equal”—plays an important role in causal analysis. This idea has been implicit in some of our earlier discussion, particularly Examples 1.1 and 1.2, but thus far we have not explicitly mentioned it. Chapter 1 The Nature of Econometrics and Economic Data 13 You probably remember from introductory economics that most economic questions are ceteris paribus by nature. For example, in analyzing consumer demand, we are interested in knowing the effect of changing the price of a go...

https://www.nybooks.com/articles/2020/02/13/how-big-law-makes-big-money/ Dear prof. Telios, I would be interested in writing an essay on this topic, specifically concerning how the "liberal" value of natural rights has come to equivocate property with other rights as equivalent. No doubt marx considers this stance very narrowly dogmatic and so I would like to adress the legal background of accumulation with an approximate essay titled something like "property rights and the legal system as liberal garantor of opression". I would be very interested in any feedback on the topic that you might have.