Statistical estimation and moment evaluation of a stochastic growth model with asset market restrictions

This paper estimates the parameters of a stochastic growth model with asset market and contrasts the model's moments with moments of the actual data. We solve the model through log-linearization along the line of Campbell (19 94) [Journal of Monetary Economics 33(3), 463] and estimate the model witho ut and with asset pricing restrictions. As asset pricing restrictions we em ploy the riskfree interest rate and the Sharpe-ratio. To estimate the param eters we employ, as in Semmler and Gong (1996a) [Journal of Economics Behav ior and Organization 30, 301], a ML estimation. The estimation is conducted through the simulated annealing. We introduce a diagnostic procedure which is closely related to Watson (1993) [Journal of Political Economy 101(6), 1011] and Diebold et al. (1995) [Technical Working Paper No. 174, National Burea of Economic Research] to test whether the second moments of the actua l macroeconomic time series data are matched by the model's time series. Se veral models are explored. The overall results are that sensible parameter estimates may be obtained when the actual and computed riskfree rate is inc luded in the moments to be matched. The attempt, however, to include the Sh arpe-ratio as restriction in the estimation does not produce sensible estim ates. The paper thus shows, by employing statistical estimation techniques, that the baseline real business cycle (RBC) model is not likely to give co rrect predictions on asset market pricing when parameters are estimated fro m actual time series data. (C) 2001 Elsevier Science B.V. All rights reserv ed. JEL classification: C13; C15; C61; E32; G1; G12.