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University of Groningen
Latent instrumental variables
Ebbes, P.
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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.