A New Partially Linear Regression with an Application to the Price of Coffee Before and After the Pandemic

We propose a partially linear regression linear model to explain coffee prices before and after the COVID-19 pandemic. This new regression model incorporates the fundamental assumption of linearity and nonlinearity between these variables. We consider the penalized quasi-likelihood method for parameter estimation and present residual analysis for the new regression model. A simulation study examines penalized quasi-likelihood estimators and the empirical distribution of the quantile residuals. Furthermore, the article aims to identify variables that influence changes in coffee prices, such as the price of Arabica and Robusta varieties, supply (expressed in millions of bags of production), global consumption, exchange rates, inflation, and the pandemic.