|Contributions||Toronto, Ont. University.|
|The Physical Object|
|Pagination||272 leaves in various pagings :|
|Number of Pages||272|
An Instrumental variable consistent estimation procedure to overcome the problem of endogenous variables in multilevel models Neil H Spencer1, Antony Fielding2 1University of Hertfordshire, 2University of Birmingham e-mail: [email protected] Introduction It is not unusual for a multilevel model to contain fixed effect explanatory. The use of an instrumental variable approach to overcoming the estimation problems associated with endogenous variables (Spencer and Fielding, , Spencer, , Spencer and Fielding, Empirical applications of instrumental variables estimation often give imprecise results. Using many valid instrumental variables can improve precision. For example, as we show, using all instruments in the Angrist and Krueger () schooling application gives tighter correct conﬁdence intervals than using 3 instruments. An important. AN INSTRUMENTAL VARIABLE APPROACH FOR IDENTIFICATION AND ESTIMATION WITH NONIGNORABLE NONRESPONSE Sheng Wang1, Jun Shao2;3 and Jae Kwang Kim4 1Mathematica Policy Research, 2East China Normal University, 3University of Wisconsin and 4Iowa State University Abstract: Estimation based on data with nonignorable nonresponse is considered.
INSTRUMENTAL VARIABLES 35 Instrumental Variables Amajorcomplicationthatisemphasizedinmicroeconometricsisthepossibilityof inconsistent parameter estimation due to endogenous regressors. Then regression estimates measure only the magnitude of association, rather than the magnitude and direction of causation which is needed for File Size: KB. Instrumental Variables and the Search for Identiﬁcation: From Supply and Demand to Natural Experiments Joshua D. Angrist and Alan B. Krueger T he method of instrumental variables is a signature technique in the econometrics toolkit. The canonical example, and earliest applications, of instrumental variables involved attempts to estimate. In statistics, econometrics, epidemiology and related disciplines, the method of instrumental variables (IV) is used to estimate causal relationships when controlled experiments are not feasible or when a treatment is not successfully delivered to every unit in a randomized experiment. When we have more instrumental variables than endogenous variables, we say the endogenous variables are over-identified. 2 we need to use “two stage least squares” (2SLS) estimation. We will come back to 2SLS later. 1 1 1, 1 1 1 n n n nk k k n n n nk k File Size: KB.
This IVARMA method is based on a modification of a previous algorithm and utilizes the Simplified Refined Instrumental Variable (SRIV) algorithm to estimate the ARMA model from the results of initial, high order, AutoRegressive (AR) model estimation. Using Monte Carlo simulation, the new algorithm is compared with the maximum likelihood method of ARMA estimation, using the well known PEM algorithm, and shown to produce parameter estimates Cited by: equation which includes the instrumental variables regressions in the estimation of the GLM. The usual variance estimate of the coeﬃcient vector from the GLM does not take into account the estimation of the instrumental variables regressions. We must derive a variance estimate that takes into account these regressions as well as the GLM es. A recursive instrumental variable algorithm is derived for the overdetermined case, in which the number of instruments is greater than the number of estimated parameters. In some applications, the algorithm provides improved parameter estimation accuracy, compared to the standard recursive instrumental variable by: use the IV estimator, making use of the gen-erated instrument ^y2. The IV estimator we developed above can be shown, algebraically, to be a 2SLS estimator; but although the IV estimator becomes non-unique in the presence of multiple instruments, the 2SLS estimation technique will always yield a unique set of pa-rameter values for a given instrument Size: 82KB.