University of Groningen Latent instrumental variables Ebbes, P. IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2004 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): Ebbes, P. (2004). Latent instrumental variables: a new approach to solve for endogeneity s.n. Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons). Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum. Download date: 12-07-2017 Bibliography Anderson, T. W. and Rubin, H. (1949). Estimators on the parameters of a single equation in a complete set of stochastic equations. Annals of Mathematical Statistics, 21:570–582. Andrews, D. W. K. (1999). Estimation when a parameter is on the boundary. Econometrica, 67:1341–1383. Andrews, R. L. and Currim, A. S. (2003). Retention of latent segments in regression-based marketing models. International Journal of Research in Marketing, 20:315–321. Angrist, J. D. (1990). Lifetime earnings and the vietnam era draft lottery: Evidence from social security administrative records. The American Economic Review, 80:313–336. Angrist, J. D., Imbens, G. W., and Krueger, A. B. (1999). Jacknife instrumental variables estimation. Journal of Applied Econometrics, 14:57–67. Angrist, J. D., Imbens, G. W., and Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association, 91:444–455. Angrist, J. D. and Krueger, A. B. (1991). Does compulsory school attendance affect schooling and earnings? The Quarterly Journal of Economics, 56:979–1014. Antoniak, C. E. (1974). Mixtures of dirichlet processes with applications to bayesian nonparametric problems. The Annals of Statistics, 2:1152–1174. Apostol, T. M. (1969). Calculus, 2nd Ed., Vol. 1: One-Variable Calculus, with an Introduction to Linear Algebra. Blaisdell, Waltham (MA). Aptech (2000). GAUSS Language Reference. Aptech Systems, Inc., Maple Valley. 237 238 Bibliography Arellano, M. (2002). Sargan’s instrumental variables estimation and the generalized method of moments. Journal of Business & Economic Statistics, 20:450–459. Arellano, M. and Bover, O. (1995). Another look at the instrumental variables estimation of error-components models. Journal of Econometrics, 68:29– 51. Asher, H. B. (1983). Causal modelling (2nd edition). In Quantitative Applications in the Social Sciences, No. 07-003. Sage Publications, Newbury Park (CA). Bagozzi, R. P., Yi, Y., and Nassen, K. D. (1999). Representation of measurement error in marketing variables: Review of approaches and extension to three-faced designs. Journal of Econometrics, 89:393–421. Baltagi, B. H. (2001). Econometric Analysis of Panel Data. John Wiley & Sons, Ltd, Chichester. Bekker, P. A. (1994). Alternative approximations to the distributions of instrumental variable estimators. Econometrica, 62:657–681. Bekker, P. A. and Kleibergen, F. (2003). Finite-sample instrumental variables inference using an asymptotic pivotal statistic. Econometric Theory, 19:744–753. Belsley, D. A., Kuh, E., and Welsch, R. E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. John Wiley & Sons, Inc., New York. Berry, S. (2003). Comment: Bayesian analysis of simultaneous demand and supply. Quantitative Marketing and Economics, 1:251–275. Berry, S., Levinsohn, J., and Pakes, A. (1995). Automobile prices in market equilibrium. Econometrica, 63:841–890. Berry, S. T. (1994). Estimating discrete-choice models of product differentiation. The RAND Journal of Economics, 25:242–262. Besanko, D., Dubé, J.-P., and Gupta, S. (2000). Heterogeneity and target marketing using aggregate retail data: A structural approach. Cornell University. Besanko, D., Gupta, S., and Jain, D. (1998). Logit demand estimation under competitive pricing behavior: An equilibrium framework. Management Science, 44:1533–1547. Bibliography 239 Biernacki, C., Celeux, G., and Govaert, G. (2000). Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22:719 – 725. Blackburn, M. L. and Neumark, D. (1993). Omitted-ability bias and the increase in the return to schooling. Journal of Labor Economics, 11:521– 544. Blundell, R. and Powell, J. L. (2001a). Endogeneity in nonparametric and semiparametric regression models. Working paper, University College London. Blundell, R. and Powell, J. L. (2001b). Endogeneity in semiparametric binary response models. CEMMAP working paper, CWP05/01. Bonjour, D., Cherkas, L. F., Haskel, J. E., Hawkes, D. D., and Spector, T. D. (2003). Returns to education: Evidence from U.K. twins. The American Economic Review, 93:1799–1812. Bound, J. and Jaeger, D. A. (1996). On the validity of season of birth as an instrument in wage equations: A comment on Angrist and Krueger’s “does compulsory school attendance affect schooling and earnings?”. Technical Report 5835, NBER. Bound, J., Jaeger, D. A., and Baker, R. M. (1995). Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. Journal of the American Statistical Association, 90:443–450. Bowden, R. J. and Turkington, D. A. (1984). Instrumental Variables. Cambridge University Press, New York. Bronnenberg, B. J. and Mahajan, V. (2001). Unobserved retailer behavior in multimarket data: Joint spatial dependence in market shares and promotion variables. Marketing Science, 20:284–299. Brooks, S. P. and Roberts, G. O. (1998). Convergence assessment techniques for Markov Chain Monte Carlo. Statistics and Computing, 8:319–335. Bryk, A. S. and Raudenbush, S. W. (1992). Hierarchical Linear Models, Applications and Data Analysis Methods. Sage Publications, Newbury Park, CA. Buse, A. (1992). The bias of instrumental variables estimators. Econometrica, 60:173–180. 240 Bibliography Card, D. (1995). Using geographical variation in college proximity to estimate the return to schooling. In Christofides, L. N., Grant, E., and Swidinsky, R., editors, Aspects of Labour Market Behaviour: Essays in Honour of John Vanderkamp, pages 201–222. University of Toronto Press, Toronto. Card, D. (1999). The causal effect of education on earnings. In Ashenfelter, O. C. and Card, D., editors, Handbook of Labor Economics, volume 3A, pages 1801–1863. Elsevier Science B.V., North-Holland. Card, D. (2001). Estimating the return to schooling: Progress on some persistent econometric problems. Econometrica, 69:1127–1160. Carroll, R. J., Roeder, K., and Wasserman, L. (1999). Flexible parametric measurement error models. Biometrics, 55:44–54. Carroll, R. J., Ruppert, D., and Stefanski, L. A. (1995). Measurement Error in Nonlinear Models. Chapman & Hall, London. Chamberlain, G. (1980). Analysis of covariance with qualitative data. The Review of Economic Studies, 47:225–238. Chamberlain, G. (1982). Multivariate regression models for panel data. Journal of Econometrics, 18:5–46. Chamberlain, G. (1984). Panel data. In Griliches, S. and Intriligator, M. D., editors, Handbook of Econometrics, Volume II, pages 1247–1318. Elsevier, Amsterdam: North Holland. Cheng, R. C. H. and Liu, W. B. (2001). The consistency of estimators in finite mixture models. The Scandinavian Journal of Statistics, 28:603–616. Chintagunta, P. K. (2001). Endogeneity and heterogeneity in a probit demand model: Estimation using aggregate data. Marketing Science, 20:442–456. Chintagunta, P. K., Kadiyali, V., and Vilcassim, N. J. (2003). Endogeneity and simultaneity in competitive pricing and advertising: A logit demand analysis. Working paper, University of Chicago. Chintagunta, P. K., Dubé, J.-P., and Goh, K. Y. (2004). Beyond the endogeneity bias: The effect of unmeasured brand characteristics on household-level brand choice models. Working paper, University of Chicago. Cook, R. D. and Weisberg, S. (1982). Residuals and Influence in Regression. Chapman and Hall, New York. Bibliography 241 Cowles, M. K. and Carlin, B. P. (1996). Markov Chain Monte Carlo convergence diagnostics: A comparative review. Journal of the American Statistical Association, 91:883–904. Cramer, M. . (2004). Omitted variable bias in discrete models. Working paper, Tinbergen institute. Davidson, R. and MacKinnon, J. G. (1993). Estimation and Inference in Econometrics. Oxford University Press, New York. Dey, D., Müller, P., and Sinha, D. (1998). Practical Nonparametric and Semiparametric Bayesian Statistics. Springer-Verlag, New York. Dhrymes, P. J. (2003). Tests for endogeneity and instrument suitability. Working paper, Columbia University. Dijk, van, A., Heerde, van, H. J., Leeflang, P. S. H., and Wittink, D. R. (2004). Similarity-based spatial methods for estimating shelf space elasticities from correlational data. Quantitative Marketing and Economics, 2:257– 277. Donald, S. G. and Newey, W. K. (2001). Choosing the number of instruments. Econometrica, 69:1161–1191. Draganska, M. and Jain, D. (2004). A likelihood approach to estimating market equilibrium models. Management Science, 50:605–616. Dubé, J.-P. and Chintagunta, P. K. (2003). Comment: Bayesian analysis of simultaneous demand and supply. Quantitative Marketing and Economics, 1:293–298. Ebbes, P., Böckenholt, U., and Wedel, M. (2004). Regressor and randomeffects dependencies in multilevel models. Statistica Neerlandica, 58:161–178. Erickson, T. (2001). Constructing instruments for regressions with measurement error when no additional data are available: Comment. Econometrica, 69:221–222. Erickson, T. and Whited, T. M. (2002). Two-step GMM estimation of the errors-in-variables model using high-order moments. Econometric Theory, 18:776–799. Escobar, M. D. (1994). Estimating normal means with a dirichlet process prior. Journal of the American Statistical Association, 89:268–277. 242 Bibliography Escobar, M. D. and West, M. (1995). Bayesian density estimation and inference using mixtures. Journal of the American Statistical Association, 90:577–588. Escobar, M. D. and West, M. (1998). Computing bayesian nonparametric hierarchical models. In Dey, D., Müller, P., and Sinha, D., editors, Practical Nonparametric and Semiparametric Bayesian Statistics, pages 1–22. Springer-Verlag, New York. Fahrmeir, L. and Tutz, G. (1994). Multivariate Statistical Modelling Based on Generalized Linear Models. Springer-Verlag, New York. Ferguson, T. S. (1973). A bayesian analysis of some nonparametric problems. The Annals of Statistics, 1:209–230. Ferguson, T. S. (1996). A Course in Large Sample Theory. Chapman & Hall, New York. Foster, E. M. (1997). Instrumental variables for logistic regression: An illustration. Social Science Research, 26:487–504. Fox, J. (1991). Regression Diagnostics. Sage Publications, inc., London. Fuller, W. (1977). Some properties of a modification of the limited information estimator. Econometrica, 45:939–953. Garen, J. (1984). The returns to schooling: A selectivity bias approach with a continuous choice variable. Econometrica, 52:1199–1218. Gasmi, F., Laffont, J. J., and Vuong, Q. (1992). Econometric analysis of collusive behavior in a soft-drink market. Journal of Economics and Management Strategy, 1:277–311. Goldstein, H. (1995). Multilevel Statistical Models. John Wiley & Sons Ltd., New York. Gönül, F. F., Kim, B.-D., and Shi, M. (2000). Mailing smarter to catalog customers. Journal of Interactive Marketing, 14:2–16. Greene, W. H. (2000). Econometric Analysis. Prentice-Hall, Inc., Upper Saddle River, New Jersey. Griliches, Z. (1977). Estimating the returns to schooling: Some econometric problems. Econometrica, 45:1–22. Bibliography 243 Hahn, J. (2002). Optimal inference with many instruments. Econometric Theory, 18:140–168. Hahn, J. and Hausman, J. (2002). A new specification test for the validity of instrumental variables. Econometrica, 70:163–189. Hahn, J. and Hausman, J. (2003). Weak instrumens: Diagnosis and cures in empirical econometrics. Recent Advances in Econometric Methodology, 93:118–125. Hamilton, B. H. and Nickerson, J. A. (2003). Correcting for endogeneity in strategic management research. Strategic Organization, 1:51–78. Harmon, C. and Walker, I. (1995). Estimates of the economic return to schooling for the united kingdom. American Economic Review, 85:1278–1286. Hartog, J. (1988). An ordered response model for allocation and earnings. Kyklos, 41:113–141. Hausman, J. A. (1978). Specification tests for econometrics. Econometrica, 46:1251–1271. Hausman, J. A. and Taylor, W. E. (1981). Panel data and unobservable individual effects. Econometrica, 49:1377–1398. Hennig, C. (2000). Identifiability of models for clusterwise linear regression. Journal of Classification, 17:273–296. Hertz, T. (2003). Upward bias in the estimated returns to education: Evidence from south africa. The American Economic Review, 93:1354–1368. Honore, B. O. and Hu, L. (2004). On the performance of some robust instrumental variables estimators. Journal of Business & Economic Statistics, 22:30–39. Hsiao, C. (1986). Analysis of Panel Data. Cambridge University Press, New York. Ibrahim, J. G. and Kleinman, K. P. (1998). Semiparametric bayesian methods for random effects models. In Dey, D., Müller, P., and Sinha, D., editors, Practical Nonparametric and Semiparametric Bayesian Statistics, pages 89–114. Springer-Verlag, New York. Im, K. S., Ahn, S. C., Schmidt, P., and Wooldridge, J. M. (1999). Efficient estimation of panel data models with strictly exogenous explanatory variables. Journal of Econometrics, 93:177–201. 244 Bibliography Isacsson, G. (2004). Estimating the economic return to educational levels using data on twins. Journal of Applied Econometrics, 19:99–119. Judge, G. G., Griffiths, W. E., Hill, R. C., Lütkepohl, H., and Lee, T.-C. (1985). The Theory and Practice of Econometrics. John Wiley & Sons Inc., New York. Kiefer, J. and Wolfowitz, J. (1956). Consistency of the maximum likelihood estimator in the presence of infinitely many incidental parameters. Annals of Mathematical Statistics, 27:887–906. Kim, J. G., Menzefricke, U., and Feinberg, F. M. (2004). Assessing heterogeneity in discrete choice models using a dirichlet process prior. Review of Marketing Science, 2:1–39. Kleibergen, F. (2002). Pivotal statistics for testing structural parameters in instrumental variables regression. Econometrica, 70:1781–1803. Kleibergen, F. and Zivot, E. (2003). Bayesian and classical approaches to instrumental variables regression. Journal of Econometrics, 114:29–72. Leeflang, P. S. H. (1994). Probleemgebied Marketing: De Marktinstrumenten. Stenfert Kroese, Houten. Lenk, P. J. (2001). Bayesian inference and Markov Chain Monte Carlo. Notes, University of Michigan. Lenk, P. J., DeSarbo, W. S., Green, P. E., and Young, M. R. (1996). Hierarchical bayes conjoint analysis: Recovery of partworth heterogeneity from reduced experimental designs. Marketing Science, 15:173–191. Lewbel, A. (1997). Constructing instruments for regressions with measurement error when no additional data are available, with an application to patents and R&D. Econometrica, 65:1201–1213. Longford, N. T. (1993). Random Coefficient Models. Oxford University Press, New York. MacEachern, S. N. (1998). Computational methods for mixture of dirichlet process models. In Dey, D., Müller, P., and Sinha, D., editors, Practical Nonparametric and Semiparametric Bayesian Statistics, pages 23–43. Springer-Verlag, New York. Madansky, A. (1959). The fitting of straight lines when both variables are subject to error. Journal of the American Statistical Association, 54:173– 205. Bibliography 245 Manchanda, P., Rossi, P. E., and Chintagunta, P. K. (2004). Response modeling with non-random marketing mix variables. Journal of Marketing Research, forthcoming. Meng, X.-L. (1994). Posterior predictive P-values. The Annals of Statistics, 22:1142–1160. Mroz, T. A. (1987). The sensitivity of an empirical model of married women’s hours of work to economic and statistical assumptions. Econometrica, 55:765–799. Mullahy, J. (1997). Instrumental-variable estimation of count data models: Applications to models of cigarette smoking behavior. The Review of Economics and Statistics, pages 586–593. Mundlak, Y. (1978). On the pooling of time-series and cross section data. Econometrica, 46:69–85. Naik, P. A., Shi, P., and Tsai, C.-L. (2003). Extending Akaike information criterion to mixture regression models. Working paper. Nelson, C. R. and Startz, R. (1990). Some further results on the exact small sample properties of the instrumental variable estimator. Econometrica, 58:967–976. Nevo, A. (2000). A practitioner’s guide to estimation of random-coefficients logit models of demand. Journal of Economics & Management Strategy, 9:513–548. Nevo, A. (2001). Measuring market power in the ready-to-eat cereal industry. Econometrica, 69:307–342. Neyman, J. and Scott, E. L. (1951). On certain methods of estimating the linear structural relation. The Annals of Mathematical Statistics, 22:352–361. Pagan, A. (1984). Econometric issues in the analysis of regressions with generated regressors. International Economic Review, 25:221–247. Petrin, A. and Train, K. (2000). Omitted product attributes in discrete choice models. Working paper, University of Berkeley. Plat, F. W. (1988). Modelling for Markets: Applications of Advanced Models and Methods for Data Analysis. PhD thesis, Rijksuniversiteit Groningen. Ploeg, van der, J. (1997). Instrumental Variable Estimation and GroupAsymptotics. PhD thesis, SOM, University of Groningen. 246 Bibliography Pudney, S. E. (1978). The estimation and testing of some error components models. Technical report, London school of economics. Redner, R. A. and Walker, H. F. (1984). Mixture densities, maximum likelihood and the EM algorithm. SIAM Review, 26:195–239. Reiersøl, O. (1950). Identifiability of a linear relation between variables which are subject to error. Econometrica, 18:375–389. Ruud, P. A. (2000). An Introduction to Classical Econometric Theory. Oxford University Press, New York. Sargan, J. D. (1958). The estimation of economic relationships using instrumental variables. Econometrica, 26:393–415. Sargan, J. D. (1959). The estimation of relationships with autocorrelated residuals by the use of instrumental variables. Journal of the Royal Statistical Society, Series B, 21:91–105. Sellke, T., Bayarri, M. J., and Berger, J. O. (2001). Calibration of P-values for testing precise null hypotheses. The American Statistician, 55:62–71. Shugan, S. M. (2004). Endogeneity in marketing decision models. Marketing Science, 23:1–3. Snijders, T. A. B. and Bosker, R. J. (1999). Multilevel Analysis. SAGE Publications, London. Spencer, N. H. and Fielding, A. (1998a). A comparison of modelling strategies for value-added analyses of educational data. Working paper, University of Hertfordshire. Spencer, N. H. and Fielding, A. (1998b). An instrumental variable consistent estimation procedure to overcome the problem of endogenous variables in multilevel models. Working paper, University of Hertfordshire. Staiger, D. and Stock, J. H. (1997). Instrumental variables regression with weak instruments. Econometrica, 65:557–586. Stern, S. (2004). Do scientist pay to be scientist? 50:835–853. Management Science, Stock, J. H., Wright, J. H., and Yogo, M. (2002). A survey of weak instruments and weak identification in generalized method of moments. Journal of Business & Economic Statistics, 20:518–529. Bibliography 247 Sudhir, K. (2001). Competitive pricing behavior in the auto market: A structural analysis. Marketing Science, 20:42–60. Teicher, H. (1963). Identifiability of finite mixtures. The Annals of Mathematical Statistics, 34:1265–1269. Titterington, D. M., Smith, A. F. M., and Makov, U. E. (1985). Statistical Analysis of Finite Mixture Distributions. John Wiley & Sons Ltd., Chichester. Uusitalo, R. (1999). Essays in Economics of Education. PhD thesis, University of Helsinki. Vella, F. (1998). Estimating models with sample selection bias: A survey. The Journal of Human Resources, 33:127–169. Vella, F. and Verbeek, M. (1998). Whose wages do unions raise? a dynamic model of unionism and wage rate determination for young men. Journal of Applied Econometrics, 13:163–183. Verbeek, M. (2000). A Guide to Modern Econometrics. John Wiley & Sons Ltd., Chichester. Vilcassim, N. J. and Chintagunta, P. K. (1995). Investigating retailer product category pricing from household scanner panel data. Journal of Retailing, 71:103–128. Villas-Boas, J. M. and Winer, R. S. (1999). Endogeneity in brand choice models. Management Science, 45:1324–1338. Wald, A. (1940). The fitting of straight lines if both variables are subject to error. The Annals of Mathematical Statistics, 11:284–300. Wang, P., Puterman, M. L., Cockburn, I., and Le, N. (1996). Mixed poisson regression models with covariate dependent rates. Biometrics, 52:381– 400. Wansbeek, T. and Meijer, E. (2000). Measurement Error and Latent Variables in Econometrics. Elsevier, Amsterdam. Wedel, M. and Kamakura, W. A. (2000). Market Segmentation. Kluwer Academic Publishers, Boston. Weisstein, E. W. (2004a). Multinomial distribution. From Mathworld – A Wolfram Web Resource. 248 Bibliography Weisstein, E. W. (2004b). Leptokurtic. From Mathworld – A Wolfram Web Resource. Weisstein, E. W. (2004c). Power. From Mathworld – A Wolfram Web Resource. West, M. (1992). Hyperparameter estimation in dirichlet process mixture models. ISDS Discussion Paper, no. 92-A02, Duke University. West, M., Müller, P., and Escobar, M. D. (1994). Hierarchical priors and mixture models, with applications in regression and density estimation. In Freeman, P. R. and Smith, A. F. M., editors, Aspects of Uncertainty, a Tribute to D. V. Lindley, pages 363–386. John Wiley & Sons Ltd., Chichester. White, H. (1980). A heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity. Econometrica, 48:817–838. White, H. (1982). Maximum likelihood estimation of misspecified models. Econometrica, 50:1–25. White, H. (2001). Asymptotic Theory for Econometricians. Academic Press, New York. Wooldridge, J. M. (2002). Econometric Analysis of Cross Section and Panel Data. Massachusetts Institute of Technology, Cambridge. Yakowitz, S. J. and Spragins, J. D. (1968). On the identifiability of finite mixtures. The Annals of Mathematical Statistics, 39:209–214. Yang, S., Chen, Y., and Allenby, G. M. (2003). Bayesian analysis of simultaneous demand and supply. Quantitative Marketing and Economics, 1:251– 275. Zhu, H.-T. and Zhang, H. (2004). Hypothesis testing in mixture regression models. Journal of the Royal Statistical Society, Series B, 66:3–16.
© Copyright 2024 Paperzz