This function generates a 3D array giving (Xn-X) in the notation of
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.
vector of data containing the dependent variable.
vector of data for all regressor variables in a regression or
true values of regressor coefficients for simulation. If
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
Use this only when lagged dependent variable is absent.
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
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
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 =
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
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.
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