search for: gamma_1

Hello, I'm very new to using R, but I was told it could do what I want. I'm not sure how best to enter the information but here goes... I'm trying to transfer the following integral into R to solve for ln(gamma_1), on the left, for multiple instances of gamma_i and variable 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...

...Demand Error Correction Model for some German federal states. As I expect the labour markets to be correlated I used a Seemingly Unrelated Regression using systemfit in R. My Model is: d(emp)_it = c + alpha*ln(emp)_i,t-1 + beta_1*ln(gdp)_i,t-1 + + beta_2*ln(wage)_i,t-1 + + beta_1*ln(i)_i,t-1 + gamma_1*d(gdp)_it + gamma_2*d(wage)_it with emp_it being the employment in state i at time t, i stands for the real interest rate, ln() is the logarithmed data, while d() stands for the difference operator. I would like to test now for weak exogeneity and I am not quite sure what kind of regression to ru...

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]]

I would like to fit a hierarchical regression model from Witte et al. (1994; see reference below). It's a logistic regression of a health outcome on quntities of food intake; the linear predictor has the form, X*beta + W*gamma, where X is a matrix of consumption of 82 foods (i.e., the rows of X represent people in the study, the columns represent different foods, and X_ij is the amount of

Hola a todos. Estoy utilizando la función lmer del paquete lme4 para ajustar un modelo mixto. Tengo varias variables en mi data.frame, unas son a nivel individual y otras a nivel de comarcas. Listo algunas. ingre_6 : Ingresos (nivel individual) iscs_a : un indicador sintético resumen de otras variables, calculado mediante componentes principales. sau_com : superficie agraria útil de cada una