Kiviet, Jan
(2019):
*Instrument-free inference under confined regressor endogeneity; derivations and applications.*

Preview |
PDF
MPRA_paper_96839.pdf Download (667kB) | Preview |

## Abstract

A fully-fledged alternative to Two-Stage Least-Squares (TSLS) inference is developed for general linear models with endogenous regressors. This alternative approach does not require the adoption of external instrumental variables. It generalizes earlier results which basically assumed all variables in the model to be normally distributed and their observational units to be stochastically independent. Now the chosen underlying framework corresponds completely to that of most empirical cross-section or time-series studies using TSLS. This enables revealing empirically relevant replication studies, also because the new technique allows testing the earlier untestable exclusion restrictions adopted when applying TSLS. For three illustrative case studies a new perspective on their empirical findings results. The new technique is computationally not very demanding. It involves scanning least-squares-based results over all compatible values of the nuisance parameters established by the correlations between regressors and disturbances.

Item Type: | MPRA Paper |
---|---|

Original Title: | Instrument-free inference under confined regressor endogeneity; derivations and applications |

Language: | English |

Keywords: | endogeneity robust inference, instrument validity tests, replication studies, sensitivity analysis, two-stage least-squares. |

Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C12 - Hypothesis Testing: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C21 - Cross-Sectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C26 - Instrumental Variables (IV) Estimation |

Item ID: | 96839 |

Depositing User: | Professor Jan Kiviet |

Date Deposited: | 16 Nov 2019 09:12 |

Last Modified: | 16 Nov 2019 09:12 |

References: | Andrews, D.W.K., Marmer, V., Yu, Z., 2019. On optimal inference in the linear IV model. Quantitative Economics 10, 457--485. Andrews, I., Stock, J., Sun, L., 2019. Weak instruments in IV regression: Theory and practice. Annual Review of Economics. Forthcoming. Angrist, J.D., Graddy, K., Imbens, G.W., 2000. The interpretation of Instrumental Variables estimators in simultaneous equations models with an application to the demand for fish. Review of Economic Studies 76, 499-527. Bound, J., Jaeger, D., Baker, R., 1995. Problems with Instrumental Variables estimation when the correlation between the instruments and the endogenous explanatory variables is weak. Journal of the American Statistical Association 90, 443-450. Epstein, R.J., 1989. The fall of OLS in structural estimation. Oxford Economic Papers 41, 94-107. Fisher, F.M., 1959. Generalisation of the rank and order conditions for identifiability. Econometrica 27, 431-447. Fisher, F.M., 1963. Uncorrelated disturbances and identifiability criteria. International Economic Review 4, 134-152. Frankel, J., Romer, D., 1999. Does trade cause growth? American Economic Review 89, 379-399. Graddy, K., 1995. Testing for imperfect competition at the Fulton fish market. RAND Journal of Economics 26, 75-92. Graddy, K., 2006. Markets: The Fulton fish market. Journal of Economic Perspectives 20, 207-220. Graddy, K., Kennedy, P., 2010. When are supply and demand determined recursively rather than simultaneously? Eastern Economic Journal 36, 188-197. Guggenberger, P., Kleibergen, F., Mavroeidis, S., 2019. A more powerful subvector Anderson Rubin test in linear instrumental variables regression. Quantitative Economics 10, 487-526. Hendry, D.F., Nielsen, B., 2006. Econometric Modeling; A Likelihood Approach. Princeton University Press, Princeton, USA. Imbens, G.W., 2014. Instrumental Variables: An econometrician's perspective. Statistical Science 29, 323-358. Kiviet, J.F., 2013. Identification and inference in a simultaneous equation under alternative information sets and sampling schemes. The Econometrics Journal 16, S24-S59. Kiviet, J.F., 2016. When is it really justifiable to ignore explanatory variable endogeneity in a regression model? Economics Letters 145, 192-195. Kiviet, J.F., 2019. Testing the impossible: identifying exclusion restrictions. To appear in The Journal of Econometrics. Kiviet, J.F., Niemczyk, J., 2012. The asymptotic and finite sample (un)conditional distributions of OLS and simple IV in simultaneous equations. Journal of Computational Statistics and Data Analysis 56, 3567-3586. Kiviet, J.F., Phillips, G.D.A., 2012. Higher-order asymptotic expansions of the least-squares estimation bias in first-order dynamic regression models. Journal of Computational Statistics & Data Analysis 56, 3705-3729. Koopmans, T. C., Rubin, H., Leipnik, R. B., 1950. Measuring the equation systems of dynamic economics. Chapter II in Statistical Inference in Dynamic Economic Models (T.C. Koopmans, ed.), New York, John Wiley and Sons: Cowles Commission Monograph No. 10. Lewbel, A., 2012. Using heteroscedasticity to identify and estimate mismeasured and endogenous regressor models. Journal of Business & Economic Statistics 30, 67-80. Murray, M.P., 2006. Avoiding invalid instruments and coping with weak instruments. Journal of Economic Perspectives 20, 111-132. Murray, M.P., 2017. Linear model IV estimation when instruments are many or weak. Journal of Econometric Methods 6, 1-22. Phillips, G.D.A., Liu-Evans, G., 2016. Approximating and reducing bias in 2SLS estimation of dynamic simultaneous equation models. Journal of Computational Statistics & Data Analysis 100, 734-762. Tanaka, T., Camerer, C.F., Nguyen, Q., 2010. Risk and time preferences: Linking experimental and household survey data from Vietnam. American Economic Review 100, 557-571. Wegge, L.L., 1965. Identifiability criteria for a system of equations as a whole. Australian Journal of Statistics 7, 67-77. Young, A., 2019. Consistency without inference: Instrumental Variables in practical application. Unpublished manuscript. |

URI: | https://mpra.ub.uni-muenchen.de/id/eprint/96839 |