======================================================================= Version: 22 February, 2008 ----------------------------------------------------------------------- Ley, E. and M.F.J. Steel (2008) "On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression" Journal of Applied Econometrics ======================================================================= 0. Files ls6bmalps.f 140Kb f77 code ls6bma.par 4Kb parameter file grk41t72.dat 20Kb data file from FLS grk54t93.dat 68Kb data file from MP grk67t88.dat 80Kb data file from SDM 1. Code In order to reproduce the results in the paper, youÕll need to compile the f77 file ls6bmalps.f and generate an executable, say, bma.exe. Then youÕll need to place a data file (*.dat) and a parameter file (ls6bma.par) in the same directory. The file flsbma.par controls some options as explained below. The successful execution will always produce an output file *.out, and (if the option to draw inference subsamples is set to TRUE) then it will also produce additional output files. 2. Parameter File The flsbma.par file controls some of the execution time parameters. [1] The first line sets the name of the different output files (the LPS *.dat files will have Ô 1.datÕ and Ô 9.datÕ appended to this name. (These dat files were later processed with R to produce the graphs in Figures 6 and 7 in the paper.) [2] The second line must contain the exact name of the data file. [3] The rest of the parameters are fairly self-explanatory. If in doubt, check the routine setup in flsbmalps.f to see what the parameters do. For a description of prediction issues and the G&M convergence estimate refer to FLS, and random theta priors are discussed in LS6. Jointness issues are described in LS5. An example of a flsbma.par file follows: k41RthetaMH OUT namefile, change at wish grk41t72.dat DAT namefile -212166542 random number seed, enter any negative integer 9 integer (1-9) specifiying prior, see routine computefj 100 warmup draws in thousands (use 1 for initial testing) 500 chain draws (use 5 for initial testing) F standardise Xs?? F Loop to compute LPS? 100 If qbove yes, then # LPSloop? 0.85d0 real taking care of sample split F wrpost? F dogm---do G&M stuff? convergence F dojoint---do jointness stuff? F thetafixed: T for fixed theta, F for random theta 20.5 emodsize---expected model size 3. Data There are 3 datasets (plain text files): grk41t72.dat [FLS] grk54t93.dat [MP] grk67t88.dat [SDM] The 3 *.dat files are plain ascii files with the following structure: Line 1..................int T: the number of observations Line 2..................int K: the total number of possible regressors in Z Lines 3 to (K+2)........char*8: the varnames for the K regressors Lines (K+3) to (K+2+T)..each line contains an obs of (y,Z) in free format (i.e., K+1 numbers, y and the K vars in Z) 4. References [FLS] Fernandez, C., E. Ley and M.F.J. Steel (2001) "Model Uncertainty in Cross-Country Growth Regressions" Journal of Applied Econometrics, 16:53-76 [LS5] Ley, E. and M.F.J. Steel (2007) "Jointness in Bayesian Variable Selection with Applications to Growth Regression" Journal of Macroeconomics 29(3): 476-493 [LS6] Ley, E. and M.F.J. Steel (2008) "On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression" Journal of Applied Econometrics [MP] Masanjala, W. and C. Papageorgiou (2005) "Initial Conditions, European Colonialism and AfricaÕs Growth," unpublished (Baton Rouge: Department of Economics, Louisiana State University) [SDM] Sala-i-Martin, X.X., G. Doppelhofer and R.I. Miller (2004) "Determinants of long-term growth: A Bayesian averaging of classical estimates (BACE) approach" American Economic Review 94: 813-83 ==========================================================================