This package computes and plots impulse responses and confidence intervals for a structural Vector Autoregression (VAR). The impulse responses can be obtained through four different implementations of the standard Choleski decomposition. A sample file is attached with the common example of a trivariate VAR including industrial production, inflation and a 3-month rate for the U.S. economy.
Using pyramid decomposition and iterative refinement, OF is calculated. Includes a demo and a paper that thoroughly explains the methodology.
The solution of the nearest correlation matrix applies the hypershpere or spectral decomposition methods as outlined in Monte Carlo methods in Finance by Peter Jackel, Chapter 6.
Use CorrelationExample.m that applies a simple example...
MVG is a multivariate Gaussian (normal) random number generator. A user can generate a vector from the multivariate normal distribution of any dimension by specifying a mean vector and symmetric positive-definite covariance matrix. A linear...
Finds the weighting coefficients of the linear combination of a set of Legendre polynomials up to order N.
Three methods are available (actually just for fun): 'inv' (default) inverts the normal equation matrix directly, while 'chol'...
Lingo is an automatic indexing system for text files. It supports lemmatising, decomposition, multiword recognition and synonyms for recognized words. Lingo is flexibly configurable and easily extendable with new custom functions.
toobox_signal - image processing related functions.
This toolbox contains functions related to image processing, including
* images loading and generation. * filtering and blurring functions. * anisotropic and...
Using the Cholesky decomposition, it generates n iterations of multivariate Gaussian random variables for a given mean vector (mu) and variance-covariance matrix (sigma).
mvgrnd(mu,sigma,n)
[A , c] = MinVolEllipse(P, tolerance)
Finds the minimum volume enclosing ellipsoid (MVEE) of a set of data points stored in matrix P. The following optimization problem is solved:
minimize log(det(A)) s.t. (P_i -...
This function plots the result of wavedec2 matlab function in two different modes. The first one called 'tree' displays all approximations and details coefficients (horizontal, vertical, diagonal), the second one called 'square' displays the...
This scrip determines the column vector 'x', given the LU decomposition of matrix 'A'. It performs the forward substitution, finding 'y=L*z', then, by backward substitution: 'z=U*x', determines the values of 'x'.
This is a templated library of numerical base classes which implement basic data structures like complex numbers, dynamic vectors, static vectors, different types of matrices like full matrices, band matrices, sparse matrices, etc. and also...
LDPC codes BER simulation under AWGN channel. MacKay-Neal based LDPC matrix. Message encoding uses sparse LU decomposition. There are 4 choices of decoder: hard-decision/bit-flip decoder, probability-domain SPA decoder, log-domain SPA decoder, and...
In this programe a highly scattered enviroment is considered. The Capacity of a MIMO channel with nt transmit antenna and nr recieve antenna is analyzed. The power in parallel channel (after SVD decomposition) is distributed as water-filling...
B3MSV Bidirectional Branch and Bound(B3) subset selection using the the Minimum Singular Value (MSV) as the criterion.
Consider the following subset selection problem:
Given a tall (m x n, m>n) matrix, A, to find n rows of...
This function searches the interior of a signal in an attempt to find a segment that may be used in order to make the signal quasi-periodic. This can be used to reduce the influence of end effects on analyses such as the Hilbert Transform, the...
This is the 2-D Fast DOST Decomposition. The computational complexity is O(NlnN)
Split the signal S(SPACE,TIME) into its downward/upward space propagating and stationnary components via a 2D Fourier decomposition. S is a (SPACE,TIME) matrix. We eventually proceed to a space and/or time filtering. DT,DX are...
Carpark adds LISP-style car, cdr, and endless combinations of them to the standard Ruby Array, allowing for terse and powerful decomposition of deeply nested arrays: [1, [2, 3, [4, 5], [[[6]]]]].caaaadddadr => 6
It generates a stochastic field on a squared space. Samples are created by Latin hypercube sampling and the spatial correlation is performed by an algorithm based on Cholesky decomposition. Example inside. |