no distribution in the posteriors
Posted: Wed Mar 26, 2014 5:05 pm
Hi everybody,
I'm estimating a new-keynesian model with a production sector of monopolistically competitive firms that combine raw materials, capital, and labor to produce output. I also have importers of consumption and investment goods, and importers of raw materials, all of which have market power. At the same time, households offer differentiated labour in a monopolistically competitive labour market, and thus choose the nominal wage. I have paremeters of calvo that indicate the percent of firms/households that can't adjust their prices/wages at each moment t.
When I try to estimate these parameters, I get the following posteriors (see attached file). Epsq and epsw, the parameters of price stickiness associated to output prices and wages, do not have a posterior distribution, but only a value, which coincides with the initial value I give to them in the estimated_params block. I have beta_pdf distributions for these parameters, but when I try to use an uniform between 0 and 1, the estimation fails. (I'm using the Metropolis-Hastings algortithm)
Any ideas on how to procede or what might be deriving these results?
Thank you very much,
Rafael
I'm estimating a new-keynesian model with a production sector of monopolistically competitive firms that combine raw materials, capital, and labor to produce output. I also have importers of consumption and investment goods, and importers of raw materials, all of which have market power. At the same time, households offer differentiated labour in a monopolistically competitive labour market, and thus choose the nominal wage. I have paremeters of calvo that indicate the percent of firms/households that can't adjust their prices/wages at each moment t.
When I try to estimate these parameters, I get the following posteriors (see attached file). Epsq and epsw, the parameters of price stickiness associated to output prices and wages, do not have a posterior distribution, but only a value, which coincides with the initial value I give to them in the estimated_params block. I have beta_pdf distributions for these parameters, but when I try to use an uniform between 0 and 1, the estimation fails. (I'm using the Metropolis-Hastings algortithm)
Any ideas on how to procede or what might be deriving these results?
Thank you very much,
Rafael