A dissertation
Download 1.47 Mb.
|
- Navigate this page:
- 5.6. References
5.5. Tests for InstrumentsThere are three tests that instruments should pass to be considered valid instruments. For linear IV models, most statistical programs provide tests that can be used to easily check to see if a set of instrumental variables is valid. These methods are well known and documented in past literature. The following three tests can be run in Stata® (SE version 10) as post estimation commands after running a 2SLS regression using the command ivregress 2sls: 1. Test for weak instruments Post-estimation command: estat first stage Interpretation: If “Prob > F” is insignificant and/or the F statistic is less than 10, then the set of IV’s are considered to be weak instruments. 2. Test for an endogenous regressor Post-estimation command: estat endog Interpretation: The null hypothesis is that the variable being tested is exogenous. A significant p-value indicates the variable is endogenous. 3. Test for validity of instruments Post-estimation command: estat overid Interpretation: The null hypothesis is that the instruments are valid. A significant p-value indicates the instruments may not be valid. So, the goal is to find a set of instruments with an insignificant p-value for this test. For non-linear models, there are currently no Stata® functions to perform these tests, and until recently there was not an easy way to test for validity of instruments. Guevara-Cue (2010) proposes a new test, called the Direct Test, which is simple to calculate from the log likelihoods of two discrete choice models, one in which endogeneity has not been controlled for and one in which endogeneity has been controlled for. Guevara-Cue shows that this simple test out performs other more complicated tests (see Guevara-Cue, 2010 for details about the test). Since most statistical programs currently provide modules for running IV models and testing for validity of instruments in linear models but not non-linear models, when searching for a valid set of instruments it may be easier to first use linear models and tests readily available in Stata® and later move to non-linear models. 5.6. ReferencesAter, I. and Orlov, E. (2010) The effect of the internet on on-time performance in the airline industry. Working Paper, Tel-Aviv University and Compass Lexecon. Berry, S.T. (1994) Estimating discrete-choice models of product differentiation. The RAND Journal of Economics, 25 (2), 242-262. Berry, S. and Jia, P. (2009) Tracing the woes: An empirical analysis of the airline industry. National Bureau of Economic Research Working Paper Series. Berry, S., Levinsohn, J. and Pakes, A. (1995) Automobile prices in market equilibrium. Econometrica, 63 (4), 841-890. Berry, S., Levinsohn, J. and Pakes, A. (2004) Differentiated products demand systems from a combination of micro and macro data: The new car market. Journal of Political Economy, 112 (1), 68-105. Bhat, C.R. (2003) Econometric choice formulations: Alternative model structures, estimation techniques, and emerging directions. Resource paper for Econometric Models of Choice: Formulation and Estimation workshop. 2003 IATBR Conference, Lucerne, Switzerland, 1-54. Branstetter, L., Chatterjee, C. and Higgins, M.J. (2011) Regulation and welfare: Evidence from Paragraph-IV generic entry in the pharmaceutical industry. Working paper, Carnegie Mellon University and Georgia Institute of Technology. Bresnahan, T.F. (1997) The Apple-Cinnamon Cheerios war: Valuing new goods, identifying market power, and economic measurement. Unpublished paper, Department of Economics, Stanford University. Blundell, R.W. and Powell, J.L. (2004) Endogeneity in semiparametric binary response models. The Review of Economic Studies, 71 (3), 655-679. Cleanthous, P. (2002) Patient welfare implications of innovation in the U.S. antidepressant market. Job market paper, Department of Economics,Yale University. Dick, A.A. (2008) Demand estimation and consumer welfare in the banking industry. Journal of Banking and Finance, 32 (2008), 1661-1676. Dutta, A. (2011) From free entry to patent protection: Welfare implications for the Indian pharmaceutical industry. The Review of Economics and Statistics, 93 (1), 160-178. Gayle, P G. (2004) Does price matter? Price and non-price competition in the airline industry. Working Paper, Kansas State University. Goolsbee, A. and Petrin, A. (2004) The consumer gains from direct broadcast satellites and the competition with cable TV. Econometrica, 72 (2), 351-381. Granados, N., Gupta, A. and Kauffman, R.J. (2012) Online and offline demand and price elasticities: Evidence from the air travel industry. Information Systems Research. INFORMS. 23 (1), 164-181. Greene, W.H. (2003) Econometric Analysis ed Rod Banister, 5th ed. Prentice Hall, Upper Saddle River, New Jersey. Guevara, C.A. and Ben-Akiva, M. (2006) Endogeneity in residential location choice models. Transportation Research Record: Journal of the Transportation Research Board, 1977, 60-66. Guevara, C.A. and Ben-Akiva, M. (2009) Addressing endogeneity in discrete choice models: Assessing control-function and latent-variable methods. Working Paper Series, Paper No. TSI-SOTUR-09-03, Massachusetts Institute of Technology, Portugal Program. Guevara-Cue, C.A. (2010) Endogeneity and sampling of alternatives in spatial choice models. Dissertation for Doctor of Philosophy, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology. Hausman, J.A. (1996) Valuation of new goods under perfect and imperfect competition. The Economics of New Goods eds Robert J. Gordon and Timothy F. Bresnahan, 207–248. University of Chicago Press, Chicago. Hausman, J., Leonard, G. and Zona, J.D. (1994) Competitive analysis with differentiated products. Annals of Economics and Statistics, No. 34, 159–180. Hsiao, C-.Y. (2008) Passenger demand for air transportation in a hub-and-spoke network. Dissertation for Doctor of Philosophy, Civil and Environmental Engineering, University of California, Berkeley. Nevo, A. (2000a) A practitioner’s guide to estimation of random-coefficients logit models of demand. Journal of Economics & Management Strategy, 9(4), 513-548. Nevo, A. (2000b) Mergers with differentiated products: The case of the ready-to-eat cereal industry. The RAND Journal of Economics, 31 (3), 395–421. Nevo, A. (2001) Measuring market power in the ready-to-eat cereal industry. Econometrica, 69 (2), 307–342. Pekgün, P., Griffin, P.M. and Keskinocak, P. (2013) An empirical study for estimating price elasticities in the travel industry. Working Paper, University of South Carolina. Petrin, A. and Train, K. (2010) A control function approach to endogeneity in consumer choice models. Journal of Marketing Research, 47 (1), 3-13. Rivers, D. and Vuong, Q.H. (1988) Limited information estimators and exogeneity tests for simultaneous probit models. Journal of Econometrics, 39, 347-366. Stock, J.H. and Trebbi, F. (2003) Retrospectives: Who invented instrumental variable regression? Journal of Economic Perspectives. 17 (3), 177–194. Stern, S. (1996). Market definition and the returns to innovation: Substitution patterns in pharmaceutical markets. Working paper, Sloan School of Management, Massachusetts Institute of Technology. Train, K. (2009) Endogeneity, Chapter 13. Discrete Choice Methods with Simulation, 315-346. Cambridge University Press, New York, NY. Train, K.E. and Winston, C. (2007) Vehicle choice behavior and the declining market share of U.S. automakers. International Economic Review, 48 (4), 1469-1496. Villas-Boas, J.M. and Winer, R.S. (1999) Endogeneity in brand choice models. Management Science, 45 (10), 1324-1338. Directory: bitstream -> handle -> 1853 handle -> The Economics of Technology Sharing: Open Source and Beyond Josh Lerner and Jean Tirole handle -> The Rise of Digital Curation and Cyberinfrastructure: From Experimentation to Implementation, and Maybe Integration handle -> Mobile augmented reality applications for library services handle -> Libraries, Archives, and Museums as Epistemic Infrastructure1 Download 1.47 Mb. Share with your friends:
|