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Using Lasso-Type Penalties to Model Time-Varying Covariate Effects in Panel Data Regressions - A Novel Approach Illustrated by the 'Death of Distance' in International Trade

When analyzing panel data using regression models, it is often reasonable to allow for time-varying covariate effects. We propose a novel approach to modelling timevarying coefficients in panel data regressions, which is based on penalized regression techniques. To illustrate the usefulness of this approach, we revisit the well-known empirical puzzle of the 'death of distance' in international trade. We find significant differences between results obtained with the proposed estimator and those obtained with 'traditional' methods. The proposed method can also be used for model selection, and to allow covariate effects to vary over other dimensions than time.

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File URL: http://project.nek.lu.se/publications/workpap/papers/WP13_5.pdf
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Paper provided by Lund University, Department of Economics in its series Working Papers with number 2013:5.

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Length: 30 pages
Date of creation: 19 Feb 2013
Date of revision:
Handle: RePEc:hhs:lunewp:2013_005
Contact details of provider: Postal: Department of Economics, School of Economics and Management, Lund University, Box 7082, S-220 07 Lund,Sweden
Phone: +46 +46 222 0000
Fax: +46 +46 2224613
Web page: http://www.nek.lu.se/en
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  1. Berthelon, Matias & Freund, Caroline, 2004. "On the conservation of distance in international trade," Policy Research Working Paper Series 3293, The World Bank.
  2. Joao Santos Silva & Silvana Tenreyro, 2005. "The log of gravity," LSE Research Online Documents on Economics 3744, London School of Economics and Political Science, LSE Library.
  3. Joakim Westerlund & Fredrik Wilhelmsson, 2009. "Estimating the gravity model without gravity using panel data," Applied Economics, Taylor & Francis Journals, vol. 43(6), pages 641-649.
  4. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
  5. Hao Helen Zhang & Wenbin Lu, 2007. "Adaptive Lasso for Cox's proportional hazards model," Biometrika, Biometrika Trust, vol. 94(3), pages 691-703.
  6. Head, Keith & Mayer, Thierry & Ries, John, 2008. "The Erosion of Colonial Trade Linkages After Independence," CEPR Discussion Papers 6951, C.E.P.R. Discussion Papers.
  7. Lukas Meier & Sara van de Geer & Peter Bühlmann, 2008. "The group lasso for logistic regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 53-71.
  8. David T Coe & Arvind Subramanian & Natalia T Tamirisa, 2007. "The Missing Globalization Puzzle: Evidence of the Declining Importance of Distance," IMF Staff Papers, Palgrave Macmillan, vol. 54(1), pages 34-58, May.
  9. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Applications to Poisson Models," Econometrica, Econometric Society, vol. 52(3), pages 701-20, May.
  10. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67.
  11. Jean-François Brun & Céline Carrère & Patrick Guillaumont & Jaime de Melo, 2005. "Has Distance Died? Evidence from a Panel Gravity Model," World Bank Economic Review, World Bank Group, vol. 19(1), pages 99-120.
  12. Robert Tibshirani & Michael Saunders & Saharon Rosset & Ji Zhu & Keith Knight, 2005. "Sparsity and smoothness via the fused lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(1), pages 91-108.
  13. Céline CARRERE & Maurice SCHIFF, 2004. "On the Geography of Trade: Distance is Alive and Well," Working Papers 200423, CERDI.
  14. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291, March.
  15. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May.
  16. Jan Gertheiss & Gerhard Tutz, 2009. "Penalized Regression with Ordinal Predictors," International Statistical Review, International Statistical Institute, vol. 77(3), pages 345-365, December.
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