The assumption we submit, because macroeconomic forcasts would be unperfect, is that behavioral equations doesn't enough describe economic behaviours through the capacity of reaction opposite to environment. Further, the forcaster belongs to his search-system, so that, may be, we must now...

In this paper, we show how to estimate the parameters of stochastic volatility models using Bayesian estimation and Markov chain Monte Carlo (MCMC) simulations through the approximation of the a-posteriori distribution of parameters. Simulated independent draws are made possible by using...

This report presents an application of a macro stress testing procedure on credit risk in the Romanian banking system. Macro stress testing, i.e. assessing the vulnerability of financial systems to exceptional but plausible macroeconomic scenarios, maintains a central role in macro-prudential...

Macroeconomic practitioners frequently work with multivariate time series models such as VARs, factor augmented VARs as well as time-varying parameter versions of these models (including variants with multivariate stochastic volatility). These models have a large number of parameters and, thus,...

In a recent article, Bai and Perron (2003, Journal of Applied Econometrics) present a comprehensive discussion of computational aspects of multiple structural change models along with several empirical examples. Here, we report on the results of a replication study using the R statistical...

TSMod is an interactive program which allows the user to estimate a broad range of univariate models. This review describes the possibilities of the package, from a user's perspective and with a secondary focus on the numerical accuracy of the program.

We provide a fast algorithm for calculating the fractional difference of a time series. In standard implementations, the calculation speed (complexity) is of order T 2, where T is the length of the time series. Our algorithm allows calculation speed of order T log2 T . For moderate and large...

Methods for detecting structural changes, or change points, in time series data are widely used in many fields of science and engineering. This chapter sketches some basic methods for the analysis of structural changes in time series data. The exposition is confined to retrospective methods for...

In seasonal adjustment a time series is considered as a juxtaposition of several components, the trend-cycle, and the seasonal and irregular components. The Bureau of the Census X-11 method, based on moving averages, correction of large errors and trading day adjustments, has long dominated....

Long memory and nonlinearity are two key features of some macroeconomic time series which are characterized by persistent shocks that seem to rise faster during recession than it falls during expansion. A variant of nonlinear time series model together with long memory are used to examine these...