This paper introduces nonlinear dynamic factor models for various applications related to risk analysis. Traditional factor models represent the dynamics of processes driven by movements of latent variables, called the factors. Our approach extends this setup by introducing factors defined as...

The paper discusses a range of modern time series methods that have become popular in the past 20 years and considers their usefulness for cliometrics research both in theory and via a range of applications. Issues such as, spurious regression, unit roots, cointegration, persistence, causality,...

The paper deals with the statistical treatment of macroeconomic data for short-run economic analysis, monitoring and control. The main applications are short-term forecasting and unobserved components estimation, including trend and cycle estimation, and, most often, seasonal adjustment. The...

This survey covers the major methods used to forecast interest rates. The theoretical underpinings are presented and discussed in the perspective of forecast accuracy using results published in the literature. A basic review of interest rate modeling is also provided. <P> Ce survol couvre les...</p>

In order to obtain exact distributional results without imposing restrictive parametric assumptions, various rank counterparts of the Dickey-Fuller statistic are considered. In particular, a rank counterpart of the score statistic is suggested which appears to have attractive theoretical...

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.

In the analysis of time series, it is frequent to classify perturbations as Additive Outliers (AO), Innovative Outliers (IO), Level Shift (LS) outliers or Transitory Change (TC) outliers. In this paper, a new outlier type, the Seasonal Level Shift (SLS), is introduced in order to complete the...

The paper contains some implications for applied econometric research. Two important ones are, first, that invertible models, such as AR or VAR models, cannot in general be used to model seasonally adjusted or detrended data. The second one is that to look at the business cycle in detrended...