Below are the solutions to these exercises on Instrumental Variables (Part-2). ############### # # # Exercise 1 # # # ############### library(AER) ## Warning: package ‘lmtest’ was built under R version 3.3.3 ## Warning: package ‘zoo’ was built under R version 3.3.3 data("PSID1976") df <- subset(PSID1976, participation=="yes") ############### # # # Exercise 2 # # […]

# statistics

## Introduction to copulas Exercises (Part-2)

Copulas are a powerful statistical tool commonly used in the finance sector to generate samples from a given multivariate joint distribution. The principal advantage of using those types of function over other methods is that copulas describe the multivariate joint distribution as his margin and the dependence structure between them, which give the user the […]

## Instrumental Variables in R exercises (Part-1)

One of the most frequently encountered issues in econometrics is endogeneity. Consider the simple Ordinary Least Squares (OLS) regression setting in which we model wages as a function of years of schooling (education): One of the main assumption of OLS is that the independent variables are not correlated with the error term. However, this is […]

## Call Center Productivity Boosting with ML Exercises

The telephone had rung when Jean was watching her favorite TV Show. It was a call center selling newspaper, so she got really upset. This situation is not unpleasant just for Jean. The call center is losing too! By calling a person the will never buy whatever is been sold, the call center is wasting […]

## Introduction to copulas Exercises (Part-1)

Copulas are a powerful statistical tool commonly used in the finance sector to generate samples from a given multivariate joint distribution. The principal advantage of using those types of function over other methods is that copulas describe the multivariate joint distribution as his margin and the dependence structure between them, which give the user the […]

## Forecasting: ARIMAX Model Exercises (Part-5)

The standard ARIMA (autoregressive integrated moving average) model allows to make forecasts based only on the past values of the forecast variable. The model assumes that future values of a variable linearly depend on its past values, as well as on the values of past (stochastic) shocks. The ARIMAX model is an extended version of […]

## Data Science for Operational Excellence (Part-5)

Operations need to have demand forecasts in order to establish optimal resource allocation policies. But, when we make predictions the only thing that we assure is the occurrence of prediction errors. Fortunately, there is no need to be 100% accurate to succeed, we just need to perform better than our competitors. In this exercise we […]

## Forecasting for small business Exercises (Part-4)

Uncertainty is the biggest enemy of a profitable business. That is especially true of small business who don’t have enough resources to survive an unexpected diminution of revenue or to capitalize on a sudden increase of demand. In this context, it is especially important to be able to predict accurately the change in the markets […]

## Forecasting: Multivariate Regression Exercises (Part-4)

In the previous exercises of this series, forecasts were based only on an analysis of the forecast variable. Another approach to forecasting is to use external variables, which serve as predictors. This set of exercises focuses on forecasting with the standard multivariate linear regression. Running regressions may appear straightforward but this method of forecasting is […]

## Data science for Doctors: Variable importance Exercises

Data science enhances people’s decision making. Doctors and researchers are making critical decisions every day. Therefore, it is absolutely necessary for those people to have some basic knowledge of data science. This series aims to help people that are around medical field to enhance their data science skills. We will work with a health related […]