In this paper we describe a solution to the problem of controlling the size of homoskedasticity tests in linear regression contexts.

A new simple formula is found to correct the underestimation of the standard deviation for total lead time demand when using simple exponential smoothing. This new formula allows one to see readily the significant size of the underestimation of the traditional formula and can easily be...

Procedures for estimating a linear single-equation model by means of panel data with errors-in-variables are considered. To eliminate fixed individual heterogeneity, the equation is differenced across one or more than one periods. The differenced equations can be estimated by using as...

This paper takes a computationnaly simple LS approach to develop a more efficient estimation procedure, which we call Residual Augmented Least Square (RALS), than OLS when the errors are not normally distributed. The efficiency gain is from manipulating the higher moment conditions implied by...

Let X1,X2,...Xn be i.i.d. N-dimensional random variables having an unknown support of probability density denoted G; we suppose that G belongs to a functional class "g" of compact sets with smooth upper surface called boundary fragments. The problem consists in testing the hypotheses G=Go...

Many current seasonally adjusted level data are based on Census-X-11-type moving average filters applied to past and forecasted log-transformed observations, which is usually called the Census-X-11 ARIMA method. The forecasts are often generated from seasonal ARIMA models for the log-transformed...

In this paper we test for (Generalized) AutoRegressive Conditional Heteroskedasticity [(G) ARCH] in daily and weekly data on 22 exchange rates and 13 stock market indices using the standard Lagrange Multiplier [LM] test for GARCH and a new LM test that is resistant to additive outliers. The data...

In this paper we propose a sequential testing procedure to determine the order of differencing in seasonally observed time series processes, which builds existing approaches developed for nonseasonal series. We allow for the possible presence of multiple unit roots at both zero and seasonal...

This paper presents a complete framework for testing procedure based on statistical theory of Markov chains.

In this paper, new noncausality tests built on a general nonlinear framework are proposed and their performance studied by a Monte Carlo experiment and a variety of nonlinear artificial series. Two of these test are based on a Taylor expansion of the nonlinear model around a given point in a...