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...

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.

We develop regression-based tests of hypotheses about out of sample prediction errors. Representative tests include ones for zero mean and zero correlation between a prediction error and a vector of predictors. The relevant environments are ones in which predictions depend on estimated...

A well known result is that the Gaussian log-likelihood can be expressed as the sum over different frequency components. This implies that the likelihood ratio statistic has a similar linear decomposition. We exploit these observations to devise diagnostic methods that are useful for...

In this paper I describe and apply the methods of Symbolic Time Series Analysis (STSA) to an experimental framework. The idea behing STSA is simple: the values of a given time series data are transformed into a finite set of symbols obtaining a finite string.

This paper investigates the stability of households' consumption behaviour in France through a prcedure of discriminating between feedback and feedforward models connected to the notions of encompassing in invariance initially proposed by Hendry (1988).

This paper derives exact expressions for the statistical curvature and related geometric quantities in the first order autoregressive models.