search for: gamma_2

I want to estimate the following fixed effect model: y_i,t = alpha_i + beta_1 x1_t + beta_2 x2_i,tx2_i,t = gamma_i + gamma_1 x1_t + gamma_2 Z1_i + gamma_3 Z2_i I can use ivreg from AER to do the iv regression. fm <- ivreg(y_i,t ~ x1_t + x2_i,t | x1_t + Z1_i + Z2_i, data = DataSet) But, I'm not sure how can I add the fixed effects. Thanks! [[alternative HTML version deleted]]

...riable N_i. gamma_i is, for example, (0, 0.03012048, 0.05000000, 0.19200000, 0.44000000, 0.62566845) N_i (N_1 or N_2) is between 0 and 1 so that N_1+N_2=1, so if N_1=(0,.166,.180,.250,.325,.374), then N_2=(1.000, 0.834, 0.820, 0.750, 0.675, 0.626) a_i (a_1 or a_2) So, for gamma_i (in this case gamma_2), N_i (N_2), and a_i (a_2) first the following a_i = ln(gamma_i)/(1-N_i)^2 then, ln(gamma_1) = -a_2*N_1*N*2 - integration (from N_1=1, to N_1) a_2 dN_1 I hope that makes sense...

...ary, since you need to calculate recursive sequence. Is there a faster way to do this in R, without using loops? The model is such: r_t = \mu + \alpha_2 r_{t-2} + a_t a_t = \sigma_t\varepsilon_t \sigma_t^2 = \beta_1a_{t-1}^2+\beta_2\sigma_{t-1}^2+ 1_{\{a_{t-1}>0\}}(\gamma_0+ \gamma_1a_{t-1}^2+\gamma_2\sigma^2_{t-1}) It is asummed that \varepsilon_t are iid and normal with zero mean and variance one. The data given is r_t, and you have to estimate variables, \mu, \alpha, \beta and \gamma. Since \varepsilon_t=\frac{a_t}{\sqrt{sigma_t}} using the equations we calculate a_t and \sigma_t and estim...