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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.



Hlavac, Marek. 2018. stargazer: Well-Formatted Regression and Summary Statistics Tables (version 5.2.2).
Kleiber, Christian, and Achim Zeileis. 2020. AER: Applied Econometrics with R (version 1.2-9).
Pinheiro, José, Douglas Bates, and R-core. 2021. nlme: Linear and Nonlinear Mixed Effects Models (version 3.1-152).
Ryan, Jeffrey A., and Joshua M. Ulrich. 2020. quantmod: Quantitative Financial Modelling Framework (version 0.4.18).
Spada, Stefano. 2018. orcutt: Estimate Procedure in Case of First Order Autocorrelation (version 2.3).
Zeileis, Achim. 2019. dynlm: Dynamic Linear Regression (version 0.3-6).