khakieconomics

library(KFAS)
library(rstan)
data(GlobalTemp)

model_dlm1a <- stan_model("../stan/dlm1a.stan")

y <- as.matrix(GlobalTemp)
data <-
    within(list(),
       {

khakieconomics

library(readr); library(dplyr); library(lubridate)

dat_2 <- read_csv("Datathon WC Data Games 11-20.csv")
glimpse(dat_2)



dat_3 <- read_csv("Datathon WC Data Games 21-30.csv")

as_datetime <- function(x){

khakieconomics

# Some fake data

library(dplyr); library(rstan)

# Write out the data generation with known parameters

# Set the number of individuals
n_ind <- 50

khakieconomics

# Generate fake data

dgp <- "data {
  int<lower=1> N;
  real x[N];
  real rho;
  real sigma;
}
transformed data {
  vector[N] mu;

khakieconomics

library(MASS); library(rstan)
# Sample size
N <- 5000

# Generate two uncorrelated covariates (means 1 and 5, both with standard deviations of 1)
X <- mvrnorm(N, c(1, 5), matrix(c(1, 0, 0, 1), 2, 2))
plot(X)
# Loadings matrix
Gamma <- matrix(c(0.5, 1,

khakieconomics

# In this demonstration, I estimate a discrete-time NGARCH model. 
# I first simulate the model with known parameters, then try to
# recover the parameters from simulated data. Then I apply the 
# model to weekly returns for Google. 

# The model seems to be able to recover most of the parameters
# fairly well, with inference for alpha, beta fairly week. gamma
# does not seem to be well identified. 

# jim savage, james@lendable.io

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")

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

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

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 +