Random Projection Estimation of Discrete-Choice Models with Large Choice Sets

References

• Achlioptas D (2003) Database-friendly random projections: Johnson-Lindenstrauss with binary coins. J. Comput. System Sci. 66(4):671–687.Crossref, Google Scholar
• Belloni A, Chernozhukov V, Hansen C (2014) High-dimensional methods and inference on structural and treatment effects. J. Econom. Perspect. 28(2):29–50.Crossref, Google Scholar
• Belloni A, Chen D, Chernozhukov V, Hansen C (2012) Sparse models and methods for optimal instruments with an application to eminent domain. Econometrica 80(6):2369–2429.Crossref, Google Scholar
• Berry S, Levinsohn J, Pakes A (1995) Automobile prices in market equilibrium. Econometrica 63(4):841–890.Crossref, Google Scholar
• Berry ST, Haile PA (2014) Identification in differentiated products markets using market level data. Econometrica 82(5):1749–1797.Crossref, Google Scholar
• Chernozhukov V, Hong H, Tamer E (2007) Estimation and confidence regions for parameter sets in econometric models. Econometrica 75(5):1243–1284.Crossref, Google Scholar
• Chevalier JA, Kashyap AK, Rossi PE (2003) Why don’t prices rise during periods of peak demand? Evidence from scanner data. Amer. Econom. Rev. 93(1):15–37.Crossref, Google Scholar
• Chintagunta P, Hanssens DM, Hauser JR (2016) Editorial—Marketing science and big data. Marketing Sci. 35(3):341–342.Link, Google Scholar
• Chiong K, Galichon A, Shum M (2016) Duality in dynamic discrete choice models. Quant. Econom. 7:83–115.Crossref, Google Scholar
• Ciliberto F, Tamer E (2009) Market structure and multiple equilibria in airline markets. Econometrica 77(6):1791–1828.Crossref, Google Scholar
• Coibion O, Gorodnichenko Y, Hong GH (2015) The cyclicality of sales, regular and effective prices: Business cycle and policy implications. Amer. Econom. Rev. 105(3):993–1029.Crossref, Google Scholar
• Dasgupta S, Gupta A (2003) An elementary proof of a theorem of Johnson and Lindenstrauss. Random Structures Algorithms 22(1):60–65.Crossref, Google Scholar
• Davis DR, Dingel JI, Monras J, Morales E (2016) How segregated is urban consumption? Technical report, Columbia University, New York.Google Scholar
• Fosgerau M, De Palma A (2015) Generalized entropy models. Technical report, Working paper, Technical University of Denmark.Google Scholar
• Fosgerau M, McFadden D (2012) A theory of the perturbed consumer with general budgets. NBER Working Paper 17953, National Bureau of Economic Research, Cambridge, MA.Google Scholar
• Fox JT (2007) Semiparametric estimation of multinomial discrete-choice models using a subset of choices. RAND J. Econom. 38(4): 1002–1019.Crossref, Google Scholar
• Fox JT, Bajari P (2013) Measuring the efficiency of an FCC spectrum auction. Amer. Econom. J. Microeconom. 5(1):100–146.Crossref, Google Scholar
• Gandhi A, Lu Z, Shi X (2013) Estimating demand for differentiated products with error in market shares. Technical report, University of Wisconsin–Madison, Madison.Google Scholar
• Gentzkow M, Shapiro J, Taddy M (2016) Measuring polarization in high-dimensional data: Method and application to congressional speech. Technical report, Stanford University, Stanford, CA.Google Scholar
• Gillen BJ, Montero S, Moon HR, Shum M (2015) BLP-Lasso for aggregate discrete choice models of elections with rich demographic covariates. USC-INET Research Paper 15-27Google Scholar
• Goeree JK, Holt CA, Palfrey TR (2005) Regular quantal response equilibrium. Experiment. Econom. 8(4):347–367.Crossref, Google Scholar
• Haile PA, Hortaçsu A, Kosenok G (2008) On the empirical content of quantal response equilibrium. Amer. Econom. Rev. 98(1):180–200.Crossref, Google Scholar
• Han AK (1987) Non-parametric analysis of a generalized regression model: The maximum rank correlation estimator. J. Econometrics 35(2):303–316.Crossref, Google Scholar
• Hausman J, McFadden D (1984) Specification tests for the multinomial logit model. Econometrica 52(5):1219–1240.Crossref, Google Scholar
• Hausman JA, Abrevaya J, Scott-Morton FM (1998) Misclassification of the dependent variable in a discrete-response setting. J. Econometrics 87(2):239–269.Crossref, Google Scholar
• Heffetz O, Ligett K (2014) Privacy and data-based research. J. Econom. Perspect. 28(2):75–98.Crossref, Google Scholar
• Hotz VJ, Miller RA (1993) Conditional choice probabilities and the estimation of dynamic models. Rev. Econom. Stud. 60(3):497–529.Crossref, Google Scholar
• Huang D, Luo L (2016) Consumer preference elicitation of complex products using fuzzy support vector machine active learning. Marketing Sci. 35(3):445–464.Link, Google Scholar
• Ichimura H, Lee L-F (1991) Semiparametric least squares estimation of multiple index models: Single equation estimation. Nonparametric and Semiparametric Methods in Econometrics and Statist.: Proc. Fifth Internat. Sympos. Econom. Theory and Econometrics, 3–49.Google Scholar
• Jacobs BJ, Donkers B, Fok D (2016) Model-based purchase predictions for large assortments. Marketing Sci. 35(3):389–404.Link, Google Scholar
• Keane M, Wasi N (2012) Estimation of discrete choice models with many alternatives using random subsets of the full choice set: With an application to demand for frozen pizza. Technical report, Oxford University, Oxford, UK.Google Scholar
• Lee L-F (1995) Semiparametric maximum likelihood estimation of polychotomous and sequential choice models. J. Econometrics 65(2):381–428.Crossref, Google Scholar
• Li P, Hastie TJ, Church KW (2006) Very sparse random projections. Proc. 12th ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining (ACM, New York), 287–296.Google Scholar
• Li P, Mitzenmacher M, Shrivastava A (2014) Coding for random projections. ICML’14 Proc. 31st Internat. Conf. Machine Learning, Beijing, 676–684.Google Scholar
• Liu X, Singh PV, Srinivasan K (2016) A structured analysis of unstructured big data by leveraging cloud computing. Marketing Sci. 35(3):363–388.Link, Google Scholar
• Manski CF (1975) Maximum score estimation of the stochastic utility model of choice. J. Econometrics 3(3):205–228.Crossref, Google Scholar
• Manski CF (1985) Semiparametric analysis of discrete response: Asymptotic properties of the maximum score estimator. J. Econometrics 27(3):313–333.Crossref, Google Scholar
• McFadden D (1974) Conditional logit analysis of qualitative choice behaviour. Zarembka P, ed. Frontiers in Econometrics (Academic Press, New York), 105–142.Google Scholar
• McFadden D (1978) Modelling the choice of residential location. Technical report, Institute of Transportation Studies, University of California, Berkeley, Berkeley.Google Scholar
• McFadden D (1981) Econometric models of probabilistic choice. Manski C, McFadden D, eds. Structural Analysis of Discrete Data with Econometric Applications (MIT Press, Cambridge, MA), 198–272.Google Scholar
• Melo E, Pogorelskiy K, Shum M (2015) Testing the quantal response hypothesis. Technical report, California Institute of Technology, Pasadena.Google Scholar
• Ng S (2015) Opportunities and challenges: Lessons from analyzing terabytes of scanner data. Technical report, Columbia University.Google Scholar
• Nunan D, Di Domenico M (2016) Exploring reidentification risk is anonymisation a promise we can keep? Internat. J. Market Res. 58(1):19–34.Crossref, Google Scholar
• Pollard D (1991) Asymptotics for least absolute deviation regression estimators. Econometric Theory 7(02):186–199.Crossref, Google Scholar
• Powell JL, Ruud PA (2008) Simple estimators for semiparametric multinomial choice models. Technical report, University of California, Berkeley, Berkeley.Google Scholar
• Rockafellar RT (1970) Convex Analysis (Princeton University Press, Princeton, NJ).Crossref, Google Scholar
• Schneider MJ, Gupta S (2016) Forecasting sales of new and existing products using consumer reviews: A random projections approach. Internat. J. Forecasting 32(2):243–256.Crossref, Google Scholar
• Shi X, Shum M, Song W (2016) Estimating semi-parametric panel multinomial choice models using cyclic monotonicity. Technical report, University of Wisconsin–Madison, Madison.Google Scholar
• Train KE, McFadden DL, Ben-Akiva M (1987) The demand for local telephone service: A fully discrete model of residential calling patterns and service choices. RAND J. Econom. 18(1):109–123.Crossref, Google Scholar
• Vempala S (2000) The Random Projection Method. Series in Discrete Mathematics and Theoretical Computer Science (DIMACS), Vol. 65 (American Mathematical Society, Providence, RI).Google Scholar
• Villani C (2003) Topics in Optimal Transportation, Graduate Studies in Mathematics, Vol. 58 (American Mathematical Society, Providence, RI).Crossref, Google Scholar