Palgrave Handbook of Econometrics: Applied Econometrics

772 Microeconometrics: Methods and Developments Kloek, T. and H.K. van Dijk (1978) Bayesian estimates of equation system paramet ...

A. Colin Cameron 773 McCulloch, R.E., N.G. Polson and P.E. Rossi (2000) A Bayesian analysis of the multinomial probit model with ...

774 Microeconometrics: Methods and Developments Rubin, D.B. (1976) Inference and missing data.Biometrika 63 , 581–92. Rubin, D.B ...

15 Computational Considerations in Empirical Microeconometrics: Selected Examples David T. Jacho-Chávez and Pravin K. Trivedi Ab ...

776 Computational Considerations in Microeconometrics 15.4.2 Semiparametric estimation 793 15.4.2.1 Example: efficient estimatio ...

David T. Jacho-Chávez and Pravin K. Trivedi 777 brings out heterogeneity of individuals, firms, and organizations. Modeling such ...

778 Computational Considerations in Microeconometrics (see, e.g., Chib, 2004; Geweke, 2005). The preliminary issues are tackled ...

David T. Jacho-Chávez and Pravin K. Trivedi 779 15.2.1.1 Characteristics Researchers are often faced with the dilemma of what pr ...

780 Computational Considerations in Microeconometrics dynamically loadedby the original program to speed up overall execution. F ...

David T. Jacho-Chávez and Pravin K. Trivedi 781 Fourth, computer-intensive resampling methods, such as the bootstrap and jack- k ...

782 Computational Considerations in Microeconometrics methods are used in theL 1 norm case, quantile regression (QR) being a lea ...

David T. Jacho-Chávez and Pravin K. Trivedi 783 whereτis small andej =[0,...,0,1,0,...,0]$is aK-vector with unity in the jth row ...

784 Computational Considerations in Microeconometrics function subject to linear equality constraints: min c$z c$z, subject toAz ...

David T. Jacho-Chávez and Pravin K. Trivedi 785 where the∼denotes normalization relative to the first alternative. Whenm>3, t ...

786 Computational Considerations in Microeconometrics available. An example is the method that uses quasi-random draws based on ...

David T. Jacho-Chávez and Pravin K. Trivedi 787 Algorithm 15.3.4.0.1 Bootstrap variance estimation – implementation Given data( ...

788 Computational Considerations in Microeconometrics The main components of the DP model are as follows. State variables are de ...

David T. Jacho-Chávez and Pravin K. Trivedi 789 called a kernel, provides the necessary weights, and a smoothing parameter, know ...

790 Computational Considerations in Microeconometrics Algorithm 15.4.1.1.1 Conditional density estimation – implementation Sele ...

David T. Jacho-Chávez and Pravin K. Trivedi 791 0.0 0.2 0.4 0.6 Density 30–32 years old 19–26 years old 67891011 log (Average An ...