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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 \(X\). In this Chapter we discuss 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. 2022. Stargazer: Well-Formatted Regression and Summary Statistics Tables. Bratislava, Slovakia: Social Policy Institute.
Kleiber, Christian, and Achim Zeileis. 2008. Applied Econometrics with R. New York: Springer-Verlag.
Pinheiro, José, Douglas Bates, and R Core Team. 2023. nlme: Linear and Nonlinear Mixed Effects Models (version 3.1-162).
Ryan, Jeffrey A., and Joshua M. Ulrich. 2023. quantmod: Quantitative Financial Modelling Framework (version 0.4.25).
Stefano, Spada, Matteo Quartagno, Marco Tamburini, and David Robinson. 2018. orcutt: Estimate Procedure in Case of First Order Autocorrelation.
Zeileis, Achim. 2019. dynlm: Dynamic Linear Regression.