Distributed-lag Linear Structural Equation Models in R: the dlsem Package
In this paper, an extension of linear Markovian structural causal models is introduced,
called distributed-lag linear structural equation models (DLSEMs),
where each factor of the joint probability distribution is a
distributed-lag linear regression with constrained lag shapes.
DLSEMs account for temporal delays in the dependence relationships
among the variables and allow to assess dynamic causal effects.
As such, they represent a suitable methodology to investigate the effect
of an external impulse on a multidimensional system through time.
In this paper, we present the dlsem package for R
implementing inference functionalities for DLSEMs.
The use of the package is illustrated through an example on simulated data
and a real-world application aiming at assessing the impact of agricultural
research expenditure on multiple dimensions in Europe.
Akaike H (1974). “A New Look at the Statistical Identification Model.” IEEE Transactions on Automatic Control, 19, 716–723. doi:10.1109/TAC.1974.1100705.
Almon S (1965). “The Distributed Lag between Capital Appropriations and Net Expenditures.” Econometrica, 33, 178–196. doi:10.2307/1911894.
Andreou E, Ghysels E, Kourtellos A (2007). “Regression Models with Mixed Sampling Frequencies.” Journal of Econometrics, 158, 246–261. doi:10.1016/j.jeconom.2010.01.004.
Dempster AP, Laird NM, Rubin DB (1977). “Maximum Likelihood from Incomplete Data via the EM Algorithm.” Journal of the Royal Statistical Society, Series B, 39(1), 1–38.
Dickey DA, 467 Fuller WA (1981). “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root.” Econometrica, 49, 1057–1072. doi:10.2307/1912517.
Gasparrini A (2011). “Distributed Lag Linear and Non-Linear Models in R: The Package dnlm.” Journal of Statistical Software, 43(8), 1–20. doi:10.18637/jss.v043.i08.
Ghysels E, Kvedaras V, Zemlys V (2016). “Mixed Frequency Data Sampling Regression Models: The R Package midasr.” Journal of Statistical Software, 72(4), 1–35. doi:10.
Ghysels E, Sinko A, Valkanov R (2007). “MIDAS Regressions: Further Results and New Directions.” Econometric Reviews, 26(1), 53–90. doi:10.1080/07474930600972467.
Granger CWJ, Newbold P (1974). “Spurious Regressions in Econometrics.” Journal of Econometrics, 2(2), 111–120. doi:10.1016/0304-4076(74)90034-7.
Haavelmo T (1943). “The Statistical Implications of a System of Simultaneous Equations.” Econometrica, 11(1), 1–12. doi:0012-9682(194301)11:1.
Judge GG, Griffiths WE, Hill RC, Lutkepohl H, Lee TC (1985). The Theory and Practice of Econometrics. 2nd edition. John Wiley & Sons, New York, US-NY.
Koopmans TC, Rubin H, Leipnik RB (1950). “Measuring the Equation Systems of Dynamic Economics.” In TC Koopmans (ed.), Statistical Inference in Dynamic Economic Models,
pp. 53–237. John Wiley & Sons, New York, US-NY.
Levin A, Lin C, Chub CJ (2002). “Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties.” Journal of Econometrics, 108, 1–24. doi:10.1016/S0304-4076(01)
Magrini A, Di Blasi S, Stefanini FM (2017). “A Conditional Linear Gaussian Network to Assess the Impact of Several Agronomic Settings on the Quality of Tuscan Sangiovese
Grapes.” Biometrical Letters, 54(1), 25–42. doi:10.1515/bile-2017-0002.
Magrini A, Pantani OL, Bartolini AB, Stefanini FM (2016). “On Prefermentative Maceration Techniques: Statistical Analysis of Sensory Descriptors in Sangiovese Wine.” Biometrical
Letters, 53(1), 1–20. doi:10.1515/bile-2016-0001.
Martins LC, Pereira LAA, Lin CA, Santos UP, Prioli G, do Carmo Luiz O, Saldiva PHN, Ferreira Braga AL (2006). “The Effects of Air Pollution on Cardiovascular Diseases: Lag Structures.” Revista de Salud Pública, 40(4). doi:10.1590/S0034-89102006000500018.
Pearl J (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge, UK.
Pearl J (2012). “The Causal Foundations of Structural Equation Modelling.” In RH Hoyle (ed.), Handbook of Structural Equation Modelling, pp. 68–91. Guilford Press, New York, US-NY.
Stefanini FM, Pantani OL (2013). “A Bayesian Model to Compare Vinification Procedures.” Biometrical Letters, 50(2), 61–80. doi:10.2478/bile-2013-0018.
Steinvil A, Fireman E, Kordova-Biezuner L, Cohen M, Shapira I, Berliner S, Rogowski O (2009). “Environmental Air pollution Has Decremental Effects on Pulmonary Function
Test Parameters up to One Week after Exposure.” American Journal of Medical Science, 338(4), 273–279. doi:10.1097/MAJ.0b013e3181adb3ed.
Wright S (1934). “The Method of Path Coefficients.” Annals of Mathematical Statistics, 5(3), 161–215. doi:10.1214/aoms/1177732676.
The Austrian Journal of Statistics publish open access articles under the terms of the Creative Commons Attribution (CC BY) License.
The Creative Commons Attribution License (CC-BY) allows users to copy, distribute and transmit an article, adapt the article and make commercial use of the article. The CC BY license permits commercial and non-commercial re-use of an open access article, as long as the author is properly attributed.
Copyright on any research article published by the Austrian Journal of Statistics is retained by the author(s). Authors grant the Austrian Journal of Statistics a license to publish the article and identify itself as the original publisher. Authors also grant any third party the right to use the article freely as long as its original authors, citation details and publisher are identified.
Manuscripts should be unpublished and not be under consideration for publication elsewhere. By submitting an article, the author(s) certify that the article is their original work, that they have the right to submit the article for publication, and that they can grant the above license.