What happens when regression variables or the dependent variable contains measurement errors — Measurement errors can seep into an experiment for a variety of reasons such as by the measuring instrument itself, the format of the experiment, or erroneous responses submitted by survey respondents. One may argue that more often than not a variable will be measured with some error. …

We’ll learn how to use the 2SLS technique to estimate linear models containing Instrumental Variables — In this article, we’ll learn about two different ways to estimate a linear model using the Instrumental Variables technique. In the previous article, we learnt about Instrumental Variables, what they are, and when and how to use them. Let’s recap what we learnt: Consider the following linear model:

We’ll learn about Instrumental Variables, and how to use them for estimating a linear regression model — In this article, we’ll learn about an ingenious technique for estimating linear regression models using an artifact called the Instrumental Variable. Instrumental Variables (IV) based estimation allows the regression modeler to deal with a serious situation that bedevils a large fraction of regression models, one where one or more regression…

How to use proxies in place of variables that cannot be observed — Sometimes, one or more explanatory variables in a regression model cannot be observed or measured. In this article, we’ll look at how to use proxy variables in place of such unmeasurable variables. We’ll explain what makes for a good versus a bad proxy. …

While exogeneity is a good thing, endogeneity can put into question your model’s effectiveness — In this article, we’ll look at what exogenous and endogenous variables are in the context of regression analysis. We’ll also explain what happens to your regression model when one or more regression variables turn out to be endogenous. …

We’ll look at how to predict the median and other quantile points — In a regression model, one is normally interested in estimating the conditional mean of the response variable. For example, consider the following plot of vehicle prices versus number of cylinders:

We’ll learn how to identify and measure these effects in a regression model using suitable examples. — In this article, we’ll figure out how to calculate the partial (or marginal) effect, the main effect, and the interaction effect of regression variables on the response variable of a regression model. We’ll also learn how to interpret the coefficients of the regression model in terms of the appropriate effect.

We’ll study how to model one commonly occurring case of discontinuous data — Most real-world processes that generate data are at the mercy of real-world events. Stock prices plunge and soar in response to quarterly results announcements and news of natural and man-made disasters. Macroeconomic indicators such as unemployment and oil prices undergo sudden trend reversals in response to the onsets of financial…

We’ll show how to use the DID model to estimate the effect of hurricanes on house prices — In this article, we will study the Difference-In-Differences regression model. The DID model is a powerful and flexible regression technique that can be used to estimate the differential impact of a ‘Treatment’ on the treated group of individuals or things. Defining the terms: Treatment, treated group, control group