Nonparametric Additive Instrumental Variable Estimator: A Group Shrinkage Estimation Perspective

REFERENCES

• Acemoglu, D., and Ventura, J. (2002), “The World Income Distribution,” Quarterly Journal of Economics, 117, 659–694.
   Web of Science ®Google Scholar
• Amemiya, T. (1974), “The Non-Linear Two-Stage Least Squares Estimator,” Journal of Econometrics, 2, 105–110.
   Google Scholar
• Anderson, J.A. (1979), “A Theoretical Foundation for the Gravity Equation,” American Economic Review, 69, 106–116.
   Web of Science ®Google Scholar
• Andrews, D., Moreira, M., and Stock, J. (2006), “Optimal Two-Sided Invariant Similar Tests for Instrumental Variables Regression,” Econometrica, 74, 715–752.
   Web of Science ®Google Scholar
• Angrist, J., and Krueger, A. (1991), “Does Compulsory School Attendance Affect Schooling and Earnings? Quarterly Journal of Economics, 106, 979–1014.
   Web of Science ®Google Scholar
• Bai, J., and Ng, S. (2010), “Instrumental Variable Estimation in a Data Rich Environment,” Econometric Theory, 26, 1577–1606.
   Web of Science ®Google Scholar
• Behrman, J., Olivia, M., Soo, C., and Bravo, D. (2012), “How Financial Literacy Affects Household Wealth Accumulation,” American Economic Review, 102, 300–304.
   PubMed Web of Science ®Google Scholar
• Belloni, A., Chen, D., Hansen, C. (2012), “Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain,” Econometrica, 80, 2369–2429.
   Web of Science ®Google Scholar
• Berkowitz, D., Caner, M., and Fang, Y. (2012), “The Validity of Instruments Revisited,” Journal of Econometrics, 166, 255–266.
   Web of Science ®Google Scholar
• Bound, J., Jaeger, D.A., and Baker, R. (1995), “Problems With Instrumental Variables Estimation When the Correlation Between the Instruments and the Endogenous Explanatory Variables is Weak,” Journal of the American Statistical Association, 90, 443–450.
   Web of Science ®Google Scholar
• Breheny, P. (2014), R Package grpreg: Regularization Paths for Regression Models with Grouped Covariates, available at http://cran.r-project.org/web/packages/grpreg/index.html
   Google Scholar
• Candes, E., and Tao, T. (2007), “The Dantzig Selector: Statistical Estimation When p is Much Larger Than n,” The Annals of Statistics, 35, 2313–2351.
   Web of Science ®Google Scholar
• Caner, M., and Fan, Q. (2015), “Hybrid Generalized Empirical Likelihood Estimators: Instrument Selection With Adaptive Lasso,” Journal of Econometrics, 187, 256–274.
   Web of Science ®Google Scholar
• Carrasco, M. (2012), “A Regularization Approach to the Many Instruments Problem,” Journal of Econometrics, 170, 383–398.
   Web of Science ®Google Scholar
• Chamberlain, G. (1987), “Asymptotic Efficiency in Estimation With Conditional Moment Restrictions,” Journal of Econometrics, 34, 305–334.
   Web of Science ®Google Scholar
• Chao, J., and Swanson, N. (2005), “Consistent Estimation With a Large Number of Weak Instruments,” Econometrica, 73, 1673–1692.
   Web of Science ®Google Scholar
• Chen, J., and Chen, Z. (2008), “Extended Bayesian Information Criteria for Model Selection With Large Model Spaces,” Biometrika, 95, 759–771.
   Web of Science ®Google Scholar
• de la Pena, V., Lai, T., and Shao, Q. (2009), Self-Normalized Processes: Limit Theory and Statistical Applications, New York: Springer-Verlag.
   Google Scholar
• Disdier, A., and Head, K. (2008), “The Puzzling Persistence of the Distance Effect on Bilateral Trade,” The Review of Economics and Statistics, 90, 37–48.
   Web of Science ®Google Scholar
• Dollar, D., and Kraay, A. (2004), “Trade, Growth and Poverty,” Economic Journal, 114, 22–49.
   Web of Science ®Google Scholar
• Donald, S.G., and Newey, W. (2001), “Choosing the Number of Instruments,” Econometrica, 69, 1161–1191.
   Web of Science ®Google Scholar
• Edwards, S. (1997), “Trade Policy, Growth, and Income Distribution,” American Economic Review, 87, 205–210.
   Web of Science ®Google Scholar
• Fan, J., Feng, Y., and Song, R. (2011), “Nonparametric Independence Screening in Sparse Ultra-High Dimensional Additive Models,” Journal of the American Statistical Association, 106, 544–557.
   PubMed Web of Science ®Google Scholar
• Fan, J., and Li, R. (2001), “Variable Selection via Nonconcave Penalized Likelihood and Its Oracle Properties,” Journal of the American Statistical Association, 96, 1348–1360.
   Web of Science ®Google Scholar
• Fan, J., and Liao, Y. (2014), “Endogeneity in High Dimensions,” The Annals of Statistics, 42, 872–917.
   PubMed Web of Science ®Google Scholar
• Frankel, J., and Romer, D. (1999), “Does Trade Cause Growth? American Economic Review, 89, 379–399.
   Web of Science ®Google Scholar
• Grossman, G., and Helpman, E. (1991), Innovation and Growth in the Global Economy, Cambridge, MA: MIT Press.
   Google Scholar
• Hahn, J., and Hausman, J. (2002), “A New Specification Test for the Validity of Instrumental Variables,” Econometrica, 70, 163–189.
   Web of Science ®Google Scholar
• Hansen, C., Hausman, J., and Newey, W. (2008), “Estimation With Many Instrumental Variables,” Journal of Business and Economic Statistics, 26, 398–422.
   Web of Science ®Google Scholar
• Hausman, J., Newey, W., Woutersen, T., Chao, J., and Swanson, N. (2012), “Instrumental Variable Estimation with Heteroskedasticity and Many Instruments,” Quantitative Economics, 3, 211–255.
   Web of Science ®Google Scholar
• Huang, J., Horowitz, J., and Wei, F. (2010), “Variable Selection in Nonparametric Additive Models,” The Annals of Statistics, 38, 2282–2313.
   PubMed Web of Science ®Google Scholar
• Krueger, A. (1990), “Asian Trade and Growth Lessons,” American Economic Review, 80, 108–112.
   Web of Science ®Google Scholar
• Lin, W., Feng, R., and Li, H. (2015), “Regularization Methods for High-Dimensional Instrumental Variables Regression With an Application to Genetical Genomics,” Journal of the American Statistical Association, 110, 270–288.
   PubMed Web of Science ®Google Scholar
• Linton, O., Chen, R., Wang, N., and Härdle, W. (1997), “An Analysis of Transformations for Additive Nonparametric Regression,” Journal of the American Statistical Association, 92, 1512–1521.
   Web of Science ®Google Scholar
• Matsuyama, K. (1992), “Agricultural Productivity, Comparative Advantage, and Economic Growth,” Journal of Economic Theory, 58, 317–334.
   Web of Science ®Google Scholar
• Melitz, M. (2003), “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity,” Econometrica, 71, 1695–1725.
   Web of Science ®Google Scholar
• Newey, W. (1990), “Efficient Instrumental Variables Estimation of Nonlinear Models,” Econometrica, 58, 809–837.
   Web of Science ®Google Scholar
• Newey, W., and Powell, J. (2003), “Instrumental Variable Estimation of Nonparametric Models,” Econometrica, 71, 1565–1578.
   Web of Science ®Google Scholar
• Ozabaci, D., Henderson, D., and Su, L. (2014), “Additive Nonparametric Regression in the Presence of Endogenous Regressors,” Journal of Business and Economic Statistics, 32, 555–575.
   Web of Science ®Google Scholar
• Rodrik, D. (2000), “How Far Will International Economic Integration Go? The Journal of Economic Perspectives, 14, 177–186.
   Web of Science ®Google Scholar
• Rose, A., and van Wincoop, E. (2001), “National Money as a Barrier to International Trade: The Real Case for Currency Union,” American Economic Review, 91, 386–390.
   Web of Science ®Google Scholar
• Sachs, J., and Warner, A. (2001), “The Curse of Natural Resources,” European Economic Review, 45, 827–838.
   Web of Science ®Google Scholar
• Sala-i-Martin, X. (1997), “I Just Ran Two Million Regressions,” American Economic Review, 87, 178–183.
   Web of Science ®Google Scholar
• Schwartz, G. (1978), “Estimating the Dimension of a Model,” The Annals of Statistics, 6, 461–464.
   Web of Science ®Google Scholar
• Staiger, D., and Stock, J. (1997), “Instrumental Variables Regression With Weak Instruments,” Econometrica, 65, 557–586.
   Web of Science ®Google Scholar
• Stone, C. (1985), “Additive Regression and Other Nonparametric Models,” The Annals of Statistics, 13, 689–705.
   Web of Science ®Google Scholar
• Tibshirani, R. (1996), “Regression Shrinkage and Selection via Lasso,” Journal of the Royal Statistical Society, Series B, 58, 267–288.
   Google Scholar
• Tinbergen, J. (1962), Shaping the World Economy: Suggestions for an International Economic Policy, New York: The Twentieth Century Fund.
   Google Scholar
• Wang, H., Li, R., and Tsai, C. (2007), “Tuning Parameter Selectors for the Smoothly Clipped Absolute Deviation Method,” Biometrika, 94, 553–568.
   PubMed Web of Science ®Google Scholar
• Yuan, M., and Lin, Y. (2006), “Model Selection and Estimation in Regression With Grouped Variables,” Journal of the Royal Statistical Society, Series B, 68, 49–67.
   Google Scholar
• Zou, H. (2006), “The Adaptive Lasso and Its Oracle Properties,” Journal of the American Statistical Association, 101, 1418–1429.
   Web of Science ®Google Scholar