checkConv: Check Convergence

Description Usage Arguments Details Value References See Also


This function generates a 3D array giving (Xn-X) in the notation of the ConvergenceConcepts package by Lafaye de Micheaux and Liquet for sample paths with dimensions = n999 as first dimension, nover = range of n values as second dimension and number of items in key as the third dimension. It is intended to be used for checking convergence of meboot in the context of a specific real world time series regression problem.


checkConv (y, bigx, trueb = 1, n999 = 999, nover = 5, 
  seed1 = 294, key = 0, trace = FALSE) 



vector of data containing the dependent variable.


vector of data for all regressor variables in a regression or ts object. bigx should not include column of ones for the intercept.


true values of regressor coefficients for simulation. If trueb=0 then use OLS coefficient values rounded to 2 digits as true values of beta for simulation purposes, to be close to but not exactly equal to OLS.


number of replicates to generate in a simulation.


number of values of n over which convergence calculated.


seed for the random number generator.


the subset of key regression coefficient whose convergence is studied if key=0 all coefficients are studied for convergence.


logical. If TRUE, tracing information on the process is printed.


Use this only when lagged dependent variable is absent.

Warning: key=0 might use up too much memory for large regression problems.

The algorithm first creates data on the dependent variable for a simulation using known true values denoted by trueb. It proceeds to create n999 regression problems using the seven-step algorithm in meboot creating n999 time series for all variable in the simulated regression. It then creates sample paths over a range of n values for coefficients of interest denoted as key (usually a subset of original coefficients). For each key coefficient there are n999 paths as n increases. If meboot algorithm is converging to true values, the value of (Xn-X) based criteria for "convergence in probability" and "almost sure convergence" in the notation of the ConvergenceConcepts package should decline. The decline can be plotted and/or tested to check if it is statistically significant as sample size increases. This function permits the user of meboot working with a short time series to see if the meboot algorithm is working in his or her particular situation.


A 3 dimensional array giving (Xn-X) for sample paths with dimensions = n999 as first dimension, nover = range of n values as second dimension and number of items in key as the third dimension ready for use in ConvergenceConcepts package.


Lafaye de Micheaux, P. and Liquet, B. (2009), Understanding Convergence Concepts: a Visual-Minded and Graphical Simulation-Based Approach, The American Statistician, 63(2) pp. 173-178.

Vinod, H.D. (2006), Maximum Entropy Ensembles for Time Series Inference in Economics, Journal of Asian Economics, 17(6), pp. 955-978

Vinod, H.D. (2004), Ranking mutual funds using unconventional utility theory and stochastic dominance, Journal of Empirical Finance, 11(3), pp. 353-377.

See Also

meboot, criterion.

meboot documentation built on May 2, 2019, 9:13 a.m.