Rank Condition For Identification Research Articles

Nonparametric estimation of triangular simultaneous equations models under weak identification

This paper analyzes the problem of weak instruments on identification, estimation, and inference in a simple nonparametric model of a triangular system. The paper derives a necessary and sufficient rank condition for identification, based on which weak identification is established. Then nonparametric weak instruments are defined as a sequence of reduced‐form functions where the associated rank shrinks to zero. The problem of weak instruments is characterized as concurvity, which motivates the introduction of a regularization scheme. The paper proposes a penalized series estimation method to alleviate the effects of weak instruments and shows that it achieves desirable asymptotic properties. A data‐driven procedure is proposed for the choice of the penalization parameter. The findings of this paper provide useful implications for empirical work. To illustrate them, Monte Carlo results are presented and an empirical example is given in which the effect of class size on test scores is estimated nonparametrically. Triangular models nonparametric identification weak identification weak instruments series estimation regularization concurvity C13 C14 C36

NONPARAMETRIC IDENTIFICATION OF ACCELERATED FAILURE TIME COMPETING RISKS MODELS

We provide new conditions for identification of accelerated failure time competing risks models. These include Roy models and some auction models. In our setup, unknown regression functions and the joint survivor function of latent disturbance terms are all nonparametric. We show that this model is identified given covariates that are independent of latent errors, provided that a certain rank condition is satisfied. We present a simple example in which our rank condition for identification is verified. Our identification strategy does not depend on identification at infinity or near zero, and it does not require exclusion assumptions. Given our identification, we show estimation can be accomplished using sieves.

INCREMENTAL EXPENDITURE OF BIOLOGIC DISEASE MODIFYING ANTIRHEUMATIC TREATMENT USING INSTRUMENTAL VARIABLES IN PANEL DATA

In health care, decision makers are generally interested in simultaneous comparisons among multiple treatments or interventions available as treatment choices in real-world clinical setting. The lack of random assignment to treatment in real-world clinical settings leads to selection-bias issues when evaluating the marginal benefits of treatment. The application of instrumental variables (IV) estimation to mitigate selection bias has traditionally been limited to comparing only two treatments/interventions concurrently. Using the case of biologic treatment in rheumatoid arthritis, we describe a generalized method of moments (GMM)-based panel data IV (IV-GMM) framework, to simultaneously estimate multiple treatment effects in the presence of time-varying selection bias and time-invariant heterogeneity. To satisfy the order and rank conditions for identification with multiple endogeneity, we propose lagged values of each treatment as excluded instruments. We evaluate the validity of the IV estimation assumptions on instrument relevance and exogeneity. Results indicate that the IV-GMM model offers enhanced control over selection bias and heterogeneity, and more importantly the panel data framework can provide valid excluded instruments that satisfy the order and rank conditions for identification when dealing with multiple endogenous variables. The approach outlined in this article has broad application for comparative effectiveness and health technology assessment involving multiple treatments/interventions using real-world nonexperimental data.

The monetary model in the presence of I(2) components: long-run relationships, short-run dynamics and forecasting of the Greek drachma

This paper re-examines the long-run properties of the monetary exchange rate model using data for the drachma–dollar and drachma–mark exchange rates under the hypothesis that the system contains variables that are I(2). Using the recent I(2) test by Paruolo (On the determination of integration indices in I(2) systems. J. Economet. 72 (1996) 313–356) to examine the presence of I(2) and I(1) components in a multivariate context we find that the system contains two I(2) variables in both cases and this finding is reconfirmed by the estimated roots of the companion matrix (Do purchasing power parity and uncovered interest rate parity hold in the long-run? An example of likelihood inference in a multivariate time-series model. Juselius, J. Economet. 69 (1995) 211–240). The I(2) component led to the transformation of the estimated model by imposing long-run but not short-run proportionality between domestic and foreign money. Two statistically significant cointegrating vectors were found and, by imposing linear restrictions on each vector as suggested by Johansen and Juselius (Identification of the long-run and the short-run structure: an applicaion to the ISLM model. J. Economet. 63 (1994) 7–36) and Johansen (Identifying restrictions of linear equations with applications to simultaneous equations and cointegration. J. Economet. 69 (1995b) 111–132), the order and rank conditions for identification are satisfied, but the test for overidentifying restrictions was not significant only for the case of the drachma/mark rate. The main findings suggest that we reject the forward-looking version of the monetary model for the drachma/dollar case but not when the drachma/mark rate is used, a result that is attributed to the monetary and exchange rate policy followed by the Greek authorities since Greece's joining of the European Union. Furthermore, we test for parameter stability using the tests developed by Hansen and Johansen (Recursive estimation in cointegrated VAR-models. Working paper (1993) University of Copenhagen) and it is shown that the dimension of the cointegration rank is sample independent while the estimated coefficients do not exhibit instabilities in recursive estimations. Finally, it is shown that the monetary model outperforms the random walk model in an out-of-sample forecasting contest.

The monetary approach to the exchange rate: Long-run relationships, identification and temporal stability

This paper re-examines the monetary model of exchange rate determination from a long-run perspective using data for three key U.S. dollar bilateral exchange rates. A novel feature of the analysis is the implementation of the testing procedure suggested by Paruolo (1996) to examine for the presence of I(2) and I(1) components in a multivariate context. Furthermore, we consider the cointegration rank utilizing the maximum likelihood cointegration technique proposed by Johansen, 1988, Johansen, 1991 as well as the estimated roots of the companion matrix (Juselius 1995). Two statistically significant cointegrating vectors were identified and by imposing linear restrictions on each vector as suggested by Johansen and Juselius (1994) the order and rank conditions for identification are satisfied, but all tests for overidentifying restrictions were significant. The main findings suggest that we reject the forward-looking version of the monetary model but the unrestricted monetary model is a valid framework for explaining the long-run movements of these exchange rates. Finally, we test for parameter stability using the tests developed by Hansen and Johansen (1993) and it is shown that the dimension of the cointegration rank may be sample dependent while the estimated coefficients do not exhibit instabilities in recursive estimations.