讲解Econometrics、讲解bandwidth留学生、辅导Python，Java/c++编程设计
- 首页 >> 其他 Financial Econometrics: Assignment One
March 11, 2019
Due: 2018-10-11
1 KERNEL DENSITY ESTIMATOR
Step 1: Generate random numbers from standard Normal distribution with sample size T =1000.1 Let the generated sample be {x1,...,xT }.
Step 2: Let k(·) be the Epanechnikov kernel, i.e.,
For a fixed x0, the kernel density estimator is given b
where h is a bandwidth. In practice, kernel estimation is sensitive to the choice of h. In Step 2,
you are required to choose h based on the method called likelihood cross-validation, which
chooses h to maximize the log likelihood function given by
L =1Remember to set a seed before the random number generation.
1is the leave-one-out kernel estimator of f (xt).
Step 3: Using the optimal bandwidth h obtained in Step 2, estimate the density function
evaluated at each sample point xt
, t = 1,...,T . Compare your result with standard normal
distribution.
2
March 11, 2019
Due: 2018-10-11
1 KERNEL DENSITY ESTIMATOR
Step 1: Generate random numbers from standard Normal distribution with sample size T =1000.1 Let the generated sample be {x1,...,xT }.
Step 2: Let k(·) be the Epanechnikov kernel, i.e.,
For a fixed x0, the kernel density estimator is given b
where h is a bandwidth. In practice, kernel estimation is sensitive to the choice of h. In Step 2,
you are required to choose h based on the method called likelihood cross-validation, which
chooses h to maximize the log likelihood function given by
L =1Remember to set a seed before the random number generation.
1is the leave-one-out kernel estimator of f (xt).
Step 3: Using the optimal bandwidth h obtained in Step 2, estimate the density function
evaluated at each sample point xt
, t = 1,...,T . Compare your result with standard normal
distribution.
2