Abrevaya, J. 1999a. “Leapfrog Estimation of a Fixed-Effects Model with Unknown Transformation of the Dependent Variable.” Journal of Econometrics 93: 203–228.Google Scholar
Abrevaya, J. 1999b. “Rank Estimation of a Transformation Model with Observed Truncation.” Econometrics Journal 2 (2): 292–305, Available at http://ideas.repec.org/a/ect/emjrnl/v2y1999i2p292-305.html.Crossref
Alan, S., and S. Leth-Petersen. 2006. “Tax Incentives and Household Portfolios: A Panel Data Analysis.” Center for Applied Microeconometrics, University of Copenhagen, Working paper number 2006–2013.Google Scholar
Amemiya, T. 1985. Advanced Econometrics. Cambridge, MA, USA: Harvard University Press.Google Scholar
Andrews, M., T. Schank, and R. Simmons. 2005. “Does Worksharing Work? Some Empirical Evidence from the IAB-Establishment Panel.” Scottish Journal of Political Economy 52 (2): 141–176.CrossrefGoogle Scholar
Bakija, J. 2000. “The Effect of Taxes on Portfolio Choice: Evidence from Panel Data Spanning the Tax Reform Act of 1986.” Williams College.Google Scholar
de Figueriredo, J., and E. Tiller. 2001. “The Structure and Conduct of Corporate Lobbying: How Firms Lobby the Federal Communications Commission.” Journal of Economics and Management Strategy 10 (1): 91–122.Google Scholar
Fehr, E., E. Kirchler, A. Weichbold, and S. Gachter. 1998. “When Social Norms Over-power Competition: Gift Exchange in Experimental Labor Markets.” Journal of Labor Economics 16 (2): 324–351.CrossrefGoogle Scholar
Ferrie, J. P., and K. Rolf. 2011. “Socioeconomic Status in Childhood and Health after Age 70: A New Longitudinal Analysis for the U.S., 1895–2005.” SSRN eLibrary.Google Scholar
Gifford, K., and J. Bernard. 2005. “Influencing Consumer Purchase Likelihood of Organic Food.” International Journal of Consumer Studies, pp. 1–9.Google Scholar
Heckman, J. J., and B. E. Honoré. 1989. “The Identifiability of the Competing Risks Model.” Biometrika 76: 325–330.Google Scholar
Honoré, B. E. 1992. “Trimmed LAD and Least Squares Estimation of Truncated and Censored Regression Models with Fixed Effects.” Econometrica 60: 533–565.Google Scholar
Honoré, B. E. 2008. On Marginal Effects in Semiparametric Censored Regression Models. Princeton, NJ, USA: Princeton University Press.Google Scholar
Huang, M., and R. Hauser. 1998. “Trends in Black-White Test Score Differences: WORDSUM Vocabulary Test.” In The Rising Curve: Long-IQ and Related Measures, edited by by U. Neisser, 303–332. Washington, DC: American Psychological Association.Google Scholar
Huang, M., and R. Hauser. 2001. “Convergent Trends in Black-White Verbal Test Score Differentials in the U.S.: Period and Cohort Perspectives.” EurAmerica 31 (2): 185–230.Google Scholar
Ioannides, Y. 1992. “Dynamics of the Composition of Household Asset Portfolios and Life Cycle.” Applied Financial Economics 2 (3), 145–159.Google Scholar
Kennickell, A., and L. Woodburn. 1997. Weighting Design for the 1983–89 SCF Panel. Washington, DC: Federal Reserve Board of Governors.Google Scholar
Khan, S., M. Ponomareva, and E. Tamer. 2011. Identification of Panel Data Models with Endogenous Censoring. MPRA Paper 30373, University Library of Munich, Germany.Google Scholar
Kyriazidou, E. 1997. “Estimation of a Panel Data Sample Selection Model.” Econometrica 65: 1335–1364.Google Scholar
Lafontaine, F. 1993. “Contractual Arrangements as Signalling Devices: Evidence from Franchising.” Journal of Law, Economics, and Organization 9 (2): 256–289.Google Scholar
Manski, C. 1987. “Semiparametric Analysis of Random Effects Linear Models from Binary Panel Data.” Econometrica 55: 357–362.Google Scholar
Nickerson, J., and B. Silverman. 2003. “Why Aren’t All Truck Drivers Owner-Operators? Asset Ownership and the Employment Relation in Interstate For-Hire Trucking.” Journal of Economics and Management Strategy 12 (1), 91–118.Google Scholar
Pakes, A., and D. Pollard. 1989. “Simulation and the Asymptotics of Optimization Estimators.” Econometrica 57: 1027–1057.Google Scholar
Poterba, J., and A. Samwick. 2002. “Taxation and Household Portfolio Composition: US Evidence from the 1980s and 1990s.” Journal of Public Economics 87 (1): 5–38.Google Scholar
Poterba, J. M. 2002. “Taxation and Portfolio Structure: Issues and Implications.” In Household Portfolios, edited by L. Guiso, M. Haliassos, and T. Jappelli. Cambridge, MA: MIT Press.Google Scholar
Powell, J. L. 1987. Semiparametric Estimation of Bivariate Latent Models. Working Paper no. 8704, Social Systems Research Institute, University of Wisconsin-Madison.Google Scholar
Ridder, G. 1990. “The Non-Parametric Identification of Generalized Accelerated Failure-Time Models.” Review of Economic Studies 57: 167–182.Google Scholar
Samwick, A. 2000. “Portfolio Responses to Taxation: Evidence from the End of the Rainbow.” In Does Atlas Shrug? The Economic Consequences of Taxing the Rich, edited by J. Slemrod. Cambridge, MA: Harvard University Press.Google Scholar
About the article
Published Online: 2013-04-09
Published in Print: 2014-01-01
1930 is the latest year for which this linkage is feasible.
Of course one could combine the insight in (4) and (5) to get even more general moment conditions. See also the discussions in Arellano and Honoré (2001) and Honoré and Hu (2004).
Clearly, one can also use the fact that differences in functions of the re-censored residuals will be orthogonal to functions for the explanatory variables. As discussed in Arellano and Honoré (2001), one can also construct moment conditions based on symmetry under the additional assumption that (εi1, …,εiT) is exchangeable conditional on (xi1, …,xiT). This is the motivation for the approach in Honoré (1992).
Consistency follows from theorem 4.1.1 of Amemiya (1985) and asymptotic normality from, for example, theorem 3.3 of Pakes and Pollard (1989).
Strictly speaking, Manski (1987) only considers the binary choice model, while Han (1987) deals with a more general model. However, Abrevaya (1999a) has demonstarted how the approach in Manski (1987) can be generalized to deal with models like the ones in Han (1987).
Ridder (1990). See also Heckman and Honoré (1989).
Bakija (2000) uses the limited panel module of the American Survey of Consumer Finances (SCF) to study portfolio changes around the 1988 tax reform. However, his data set is very small (984 households) and unrepresentative due to the well-known attrition problem in the SCF panel module; see Kennickell and Woodburn (1997). More important in this context, the estimators applied do not exploit the full potential of the panel data in handling unobserved heterogeneity. Ioannides (1992) also employs the 1983–1986 SCF panel module but does not control for unobserved heterogeneity.
Alan and Leth-Petersen (2006) document that the reduced value of the interest deduction led households to liquidate financial assets to lower their mortgage debt. This was possible because pre-payment of mortgage debt is not restricted in Denmark.
Approximately 20% of the population belong in the top bracket.
The median household in the sample holding stocks received dividends corresponding to 2% of the value of the stocks. The median household in the sample holding bonds received interest payments from these corresponding to 10% of the value of the bonds.
For assessing portfolio reshuffling renters could have been included. We have chosen to leave them out of this analysis because there are only a few renters (898) with positive financial wealth of at least 5000 DKK in 1984. Moreover, renters generally do not provide a good comparison group for homeowners, since different preference parameters may govern their behavior.
See Alan and Leth-Petersen (2006) for a more detailed analysis.
An alternative identification strategy could be based on comparing the behavior of households in different tax brackets. Households in the lowest tax bracket faced only very small changes in marginal tax rates on capital income, and households in the middle tax bracket faced different changes in marginal tax rates than households in the highest tax bracket. In our case this is not a natural approach to follow. High- and low-income people are different in terms of wealth levels and portfolio composition and possibly different with respect to preference parameters such as the discount rate and the level of risk aversion. Households in lower tax brackets therefore do not represent a natural control group for high-income households.
As explained in Honoré (2008), the parameter estimates for both the random effects and the fixed effects models can be converted to marginal effects by multiplying them by the fraction of observations that are not censored.
Since both the difference in the re-censored residuals and u are continuous in d, it is not necessary to distinguish between closed and open intervals in the following discussion.