khakieconomics

data {
  int N; // number of rows
  int T; // number of inidvidual-choice sets/task combinations
  int I; // number of Individuals
  int P; // number of covariates
  
  vector<lower = 0, upper = 1>[N] choice; // binary indicator for choice
  matrix[N, P] X; // product attributes
  
  int task[T]; // index for tasks

khakieconomics

data {
  int N; // number of rows
  int T; // number of inidvidual-choice sets/task combinations
  int I; // number of Individuals
  int P; // number of covariates
  
  vector<lower = 0, upper = 1>[N] choice; // binary indicator for choice
  matrix[N, P] X; // product attributes
  
  int task[T]; // index for tasks

khakieconomics

data {
  int N;
  int PX; // dimension of exogenous covariates
  int PN; // dimension of endogenous covariates
  int PZ; // dimension of instruments
  matrix[N, PX] X_exog; // exogenous covariates
  matrix[N, PN] X_endog; // engogenous covariates
  matrix[N, PZ] Z; // instruments
  vector[N] Y_outcome; // outcome variable
  int<lower=0,upper=1> run_estimation; // simulate (0) or estimate (1)

khakieconomics

library(rstan); library(dplyr); library(ggplot2); library(reshape2)
options(mc.cores = parallel::detectCores())

set.seed(49)
# Observations
N <- 2000

# Draw errors
rho <- 0.5
Sigma <- matrix(c(1, rho, rho, 1), 2, 2)

khakieconomics

functions {
    real binormal_cdf(real z1, real z2, real rho) {
    if (z1 != 0 || z2 != 0) {
      real denom = fabs(rho) < 1.0 ? sqrt((1 + rho) * (1 - rho)) : not_a_number();
      real a1 = (z2 / z1 - rho) / denom;
      real a2 = (z1 / z2 - rho) / denom;
      real product = z1 * z2;
      real delta = product < 0 || (product == 0 && (z1 + z2) < 0);
      return 0.5 * (Phi(z1) + Phi(z2) - delta) - owens_t(z1, a1) - owens_t(z2, a2);
    }

khakieconomics

functions {
  // lower bound is a, upper bound is b, rv is x, mean is mu, sd is sigma
  vector xi(vector x, real mu, real sigma) {
    return((x - mu)./sigma);
  }
  real alpha(real a, real mu, real sigma) {
    real out;
    out = (a==negative_infinity())? negative_infinity(): (a - mu)/sigma;
    return(out);
  }

khakieconomics

library(ggplot2); library(dplyr)

aa <- data_frame(a = rnorm(30, 2, .1), b = rnorm(30, .5, .05), sigma = rnorm(30, 1, .1)) 
g <- ggplot(data.frame(x = c(-1, 5.5)), aes(x))
for(i in 1:nrow(aa)) {
 g <- g +
  stat_function(fun = dnorm, args = list(mean = c(aa$a[i] + aa$b[i]), sd = aa$sigma[i]), alpha = 0.3)  
}
 
g +

khakieconomics

data {
  int N; // number of observations
  matrix[N, 6] Y; // a matrix of observations for each measure. I've encoded missing values as -9
  vector[6] inits; // a vector of centers for the initial values of the data
}
parameters {
  // the state
  matrix[N, 6] X;
  // cholesky factor of the correlation matrix of the innovations to the state
  cholesky_factor_corr[6] L_omega;

khakieconomics

# load the Stan library and set it up to use all cores
library(rstan)
options(mc.cores = parallel::detectCores())

# Create some data 

softmax <- function(x) exp(x)/sum(exp(x))
N <- 50
P <- 5
theta <- rnorm(P)

khakieconomics

library(tidyr); library(ggplot2)
library(rvest); library(dplyr); library(ggthemes)

session <- html_session("http://visadoor.com/")
form <- html_form(session)[[1]]

# Add job titles to the character list below
jobs <- c("data scientist", "economist", "actuary", "consultant", "management consultant", "statistician")