Exogeneity, weak identification and instrument selection in econometrics
The last decade shows growing interest for the so-called weak instruments problems in the econometric literature, i.e. situations where instruments are poorly correlated with endogenous explanatory variables. More generally, these can be viewed as situations where model parameters are not identified or nearly so (see Dufour and Hsiao, 2008). It is well known that when instruments are weak, the limiting distributions of standard test statistics - like Student, Wald, likelihood ratio and Lagrange multiplier criteria in structural models - have non-standard distributions and often depend heavily on nuisance parameters. Several empirical studies including the estimation of returns to education [Angrist and Krueger (1991, 1995), Angrist et al. (1999), Bound et al. (1995), Dufour and Taamouti (2007)] and asset pricing model (C-CAPM) [Hansen and Singleton (1982, 1983), Stock and Wright (2000)], have showed that the above procedures are unreliable in presence of weak identification. As a result, identification-robust tests [Anderson and Rubin (1949), Moreira (2003), Kleibergen (2002), Dufour and Taamouti (2007)] are often used to make reliable inference. However, little is known about the quality of these procedures when the instruments are invalid or both weak and invalid. This raises the following question: what happens to inference procedures when some instruments are endogenous or both weak and endogenous? In particular, what happens if an invalid instrument is added to a set of valid instruments? How robust are these inference procedures to instrument endogeneity? Do alternative inference procedures behave differently? If instrument endogeneity makes statistical inference unreliable, can we propose the procedures for selecting "good instruments" (i.e. strong and valid instruments)? Can we propose instrument selection procedure which will be valid even in presence of weak identification? This thesis focuses on structural models and answers these questions through four chapiters. The first chapter is published in Journal of Statistical Planning and Inference 138 (2008) 2649 – 2661. In this chapter, we analyze the effects of instrument endogeneity on two identificationrobust procedures: Anderson and Rubin (1949, AR) and Kleibergen (2002, K) test statistics, with or without weak instruments. First, when the level of instrument endogeneity is fixed (does not depend on the sample size), we show that all these procedures are in general consistent against the presence of invalid instruments (hence asymptotically invalid for the hypothesis of interest), whether the instruments are "strong" or "weak". We also describe situations where this consistency may not hold, but the asymptotic distribution is modified in a way that would lead to size distortions in large samples. These include, in particular, cases where 2SLS estimator remains consistent, but the tests are asymptotically invalid. Second, when the instruments are locally exogenous (the level of instrument endogeneity approaches zero as the sample size increases), we find asymptotic noncentral chi-square distributions with or without weak instruments, and describe situations where the non-centrality parameter is zero and the asymptotic distribution remains the same as in the case of valid instruments (despite the presence of invalid instruments). The second chapter analyzes the effects of weak identification on Durbin-Wu-Hausman (DWH) specification tests an Revankar-Harttley exogeneity test. We propose a finite-and large-sample analysis of the distribution of DWH tests under the null hypothesis (level) and the alternative hypothesis (power), including when identification is deficient or weak (weak instruments). Our finite-sample analysis provides several new insights and extensions of earlier procedures. The characterization of the finite-sample distribution of the test-statistics allows the construction of exact identificationrobust exogeneity tests even with non-Gaussian errors (Monte Carlos tests) and shows that such tests are typically robust to weak instruments (level is controlled). Furthermore, we provide a characterization of the power of the tests, which clearly exhibits factors which determine power. We show that DWH-tests have no power when all instruments are weak [similar to Guggenberger(2008)]. However, power does exist as soon as we have one strong instruments. The conclusions of Guggenberger (2008) focus on the case where all instruments are weak (a case of little practical interest). Our asymptotic distributional theory under weaker assumptions confirms the finite-sample theory. Moreover, we present simulation evidence indicating: (1) over a wide range cases, including weak IV and moderate endogeneity, OLS performs better than 2SLS [finding similar to Kiviet and Niemczyk (2007)]; (2) pretest-estimators based on exogeneity tests have an excellent overall performance compared with usual IV estimator. We illustrate our theoretical results through simulation experiment and two empirical applications: the relation between trade and economic growth and the widely studied problem of returns to education. In the third chapter, we extend the generalized Wald partial exogeneity test [Dufour (1987)] to non-gaussian errors. Testing whether a subset of explanatory variables is exogenous is an important challenge in econometrics. This problem occurs in many applied works. For example, in the well know wage model, one should like to assess if mother’s education is exogenous without imposing additional assumptions on ability and schooling. In the growth model, the exogeneity of the constructed instrument on the basis of geographical characteristics for the trade share is often questioned and needs to be tested without constraining trade share and the other variables. Standard exogeneity tests of the type proposed by Durbin-Wu-Hausman and Revankar-Hartley cannot solve such problems. A potential cure for dealing with partial exogeneity is the use of the generalized linear Wald (GW) method (Dufour, 1987). The GW-procedure however assumes the normality of model errors and it is not clear how robust is this test to non-gaussian errors. We develop in this chapter, a modified version of earlier procedure which is valid even when model errors are not normally distributed. We present simulation evidence indicating that when identification is strong, the standard GW-test is size distorted in presence of non-gaussian errors. Furthermore, our analysis of the performance of different pretest-estimators based on GW-tests allow us to propose two new pretest-estimators of the structural parameter. The Monte Carlo simulations indicate that these pretest-estimators have a better performance over a wide range cases compared with 2SLS. Therefore, this can be viewed as a procedure for selecting variable where a GW-test is used in the first stage to decide which variables should be instruments and which ones are valid instruments. We illustrate our theoretical results through two empirical applications: the well known wage equation and the returns to scale in electricity supply. The results show that the GW-tests cannot reject the exogeneity of mother’s education, i.e. mother’s education may constitute a valid IV for schooling. However, the output in cost equation is endogenous and the price of fuel is a valid IV for estimating the returns to scale. The fourth chapter develops identification-robust inference for the covariances between errors and regressors of an IV regression. The results are then applied to develop partial exogeneity tests and partial IV pretest-estimators which are more efficient than usual IV estimator. When more than one stochastic explanatory variables are involved in the model, it is often necessary to determine which ones are independent of the disturbances. This problem arises in many empirical applications. For example, in the New Keynesian Phillips Curve, one should like to assess whether the interest rate is exogenous without imposing additional assumptions on inflation rate and the other variables. Standard Wu-Durbin-Hausman (DWH) tests which are commonly used in applied work are inappropriate to deal with such a problem. The generalized Wald (GW) procedure (Dufour, 1987) which typically allows the construction of confidence sets as well as testing linear restrictions on covariances assumes that the available instruments are strong. When the instruments are weak, the GW-test is in general size distorted. As a result, its application in models where instruments are possibly weak–returns to education, trade and economic growth, life cycle labor supply, New Keynesian Phillips Curve, pregnancy and the demand for cigarettes–may be misleading. To answer this problem, we develop a finite-and large-sample valid procedure for building confidence sets for covariances allowing for the presence of weak instruments. We provide analytic forms of the confidence sets and characterize necessary and sufficient conditions under which they are bounded. Moreover, we propose two new pretest-estimators of structural parameters based on our above procedure. Both estimators combine 2SLS and partial IV-estimators. The Monte Carlo experiment shows that: (1) partial IV-estimators outperform 2SLS when the instruments are weak; (2) pretestestimators have an excellent overall performance–bias and MSE– compared with 2SLS. Therefore, this can be viewed as a variable selection method where the projection-based techniques is used to decide which variables should be instrumented and which ones are valid instruments. We illustrate our results through two empirical applications: the relation between trade and economic growth and the widely studied problem of returns to education. The results show unbounded confidence sets, suggesting that the IV are relatively poor in these models, as questioned in the literature [Bound (1995)].
Structural models Weak instruments Invalid instruments Identification-robust tests Exact Monte Carlo exogeneity tests Confidence sets Instrument selection partial exogeneity tests partial IV estimators