School of Economics and Finance

No. 534: Cluster Analysis of Panel Choosing the Optimal Set of Instruments from Large Instrument Setsusing Non-Standard Optimisation of Information Criteria

George Kapetanios , Queen Mary, University of London

May 1, 2005

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It is well known that instrumental variables (IV) estimation is sensitive to the choice of instruments both in small samples and asymptotically. Recently, Donald and Newey (2001) suggested a simple method for choosing the instrument set. The method involves minimising the approximate mean square error (MSE) of a given IV estimator where the MSE is obtained using refined asymptotic theory. An issue with the work of Donald and Newey (2001) is the fact that when considering large sets of valid instruments, it is not clear how to order the instruments in order to choose which ones ought to be included in the estimation. The present paper provides a possible solution to the problem using nonstandard optimisation algorithms. The properties of the algorithms are discussed. A Monte Carlo study illustrates the potential of the new method.

J.E.L classification codes: C12, C15, C23

Keywords:Instrumental Variables, MSE, Simulated Annealing, Genetic Algorithms