15 Estimation of Dynamic Causal Effects
It sometimes is of interest to know the size of current and future reaction of \(Y\) to a change in \(X\). This is called the dynamic causal effect on \(Y\) of a change in \(X\). This Chapter we discusses how to estimate dynamic causal effects in R applications, where we investigate the dynamic effect of cold weather in Florida on the price of orange juice concentrate.
The discussion covers:
- estimation of distributed lag models
- heteroskedasticity- and autocorrelation-consistent (HAC) standard errors
- generalized least squares (GLS) estimation of ADL models
To reproduce code examples, install the R packages listed below beforehand and make sure that the subsequent code chunk executes without any errors.
- AER (Christian Kleiber and Zeileis 2020)
- dynlm (Zeileis 2019)
- nlme (Pinheiro, Bates, and R-core 2021)
- orcutt (Spada 2018)
- quantmod (Ryan and Ulrich 2020)
- stargazer (Hlavac 2018)
library(AER) library(quantmod) library(dynlm) library(orcutt) library(nlme) library(stargazer)