20 research outputs found

    State Space Methods in Ox/SsfPack

    Get PDF
    The use of state space models and their inference is illustrated using the package SsfPack for Ox. After a rather long introduction that explains the use of SsfPack and many of its functions, four case-studies illustrate the practical implementation of the software to real world problems through short sample programs. The first case consists in the analysis of the well-known (at least to time series analysis experts) Nile data with a local level model. The other case-studies deal with ARIMA and RegARIMA models applied to the (also well-known) Airline time series, structural time series models applied to the Italian industrial production index and stochastic volatility models applied to the FTSE100 index. In all applications inference on the model (hyper-) parameters is carried out by maximum likelihood, but in one case (stochastic volatility) also an MCMC-based approach is illustrated. Cubic splines are covered in a very short example as well.

    Business cycle and sector cycles

    Get PDF
    A methodology based on the multivariate generalized Butterwoth filter for extracting the business cycles of the whole economy and of its productive sectors is developed. The method is then illustrated through an application to the Italian gross value added time series of the main economic sectors.Business cycle, Butterworth filter, Unobserved components, Kalman Filter

    Time Series Modeling with Duration Dependent Markov-Switching Vector Autoregressions: MCMC Inference, Software and Applications

    Get PDF
    Duration dependent Markov-switching VAR (DDMS-VAR) models are time series models with data generating process consisting in a mixture of two VAR processes, which switches according to a two-state Markov chain with transition probabilities depending on how long the process has been in a state. In the present paper I propose a MCMC-based methodology to carry out inference on the model's parameters and introduce DDMSVAR for Ox, a software written by the author for the analysis of time series by means of DDMS-VAR models. An application of the methodology to the U.S. business cycle concludes the article.Markov-switching, Business cycle, Gibbs sampling, Duration dependence, Vector autoregression

    Dynamic Conditional Correlation with Elliptical Distributions

    Get PDF
    The Dynamic Conditional Correlation model of Engle has made the estimation of multivariate GARCH models feasible for reasonably big vectors of securities’ returns. In the present paper we show how Engle’s twosteps estimate of the model can be easily extended to elliptical conditional distributions and apply different leptokurtic DCC models to some stocks listed at the Milan Stock Exchange. A free software written by the authors to carry out all the required computations is presented as well.Multivariate GARCH, Dynamic conditional correlation, Generalized method of moments

    State Space Methods in Ox/SsfPack

    Get PDF
    The use of state space models and their inference is illustrated using the package SsfPack for Ox. After a rather long introduction that explains the use of SsfPack and many of its functions, four case-studies illustrate the practical implementation of the software to real world problems through short sample programs. The first case consists in the analysis of the well-known (at least to time series analysis experts) Nile data with a local level model. The other case-studies deal with ARIMA and RegARIMA models applied to the (also well-known) Airline time series, structural time series models applied to the Italian industrial production index and stochastic volatility models applied to the FTSE100 index. In all applications inference on the model (hyper-) parameters is carried out by maximum likelihood, but in one case (stochastic volatility) also an MCMC-based approach is illustrated. Cubic splines are covered in a very short example as well

    Modelling Good and Bad Volatility

    Full text link

    Time series modelling with unobserved components

    No full text

    The Industrial Cycle of Milan as an Accurate Leading Indicator for the Italian Business Cycle

    No full text
    A coincident business cycle indicator for the Milan area is built on the basis of a monthly industrial survey carried out by Assolombarda, the largest territorial entrepreneurial association in Italy. The indicator is extracted from three time series concerning the production level and the domestic and foreign order book as declared by some 250 Assolombarda associates.
    corecore