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Low frequency and weighted likelihood solutions for mixed frequency dynamic factor models

Blasques, Francisco; Koopman, Siem Jan; Mallee, Max I. P. - 2014

The multivariate analysis of a panel of economic and financial time series with mixed frequencies is a challenging problem. The standard solution is to analyze the mix of monthly and quarterly time series jointly by means of a multivariate dynamic model with a monthly time index: artificial...

Persistent link: https://www.econbiz.de/10010391543

Nowcasting and forecasting economic growth in the Euro area using principal components

Hindrayanto, Irma; Koopman, Siem Jan; Winter, Jasper de - 2014

short-term forecasting models. These empirical findings have been established for different macroeconomic data sets and … specification is most effective in its forecasting performance. Furthermore, the forecast performances of the different … extended empirical out-of-sample forecasting competition for quarterly growth of gross domestic product in the euro area and …

Persistent link: https://www.econbiz.de/10010395082

Spillover dynamics for systemic risk measurement using spatial financial time series models

Blasques, Francisco; Koopman, Siem Jan; Lucas, André; … - 2014

We introduce a new model for time-varying spatial dependence. The model extends the well-known static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the...

Persistent link: https://www.econbiz.de/10010391531

Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time Series Models

Blasques, Francisco - 2014

Persistent link: https://www.econbiz.de/10013049149

The information in systemic risk rankings

Nucera, Federico; Schwaab, Bernd; Koopman, Siem Jan; … - 2015

We propose to pool alternative systemic risk rankings for financial institutions using the method of principal components. The resulting overall ranking is less affected by estimation uncertainty and model risk. We apply our methodology to disentangle the common signal and the idiosyncratic...

Persistent link: https://www.econbiz.de/10010532581

Measuring financial cycles in a model-based analysis : empirical evidence for the United States and the euro area

Galati, Gabriele; Hindrayanto, Irma; Koopman, Siem Jan; … - 2016

We adopt an unobserved components time series model to extract financial cycles for the United States and the five largest euro area countries over the period 1970 to 2014. We find that credit, the credit-to-GDP ratio and house prices have medium-term cycles which share a few common statistical...

Persistent link: https://www.econbiz.de/10011456728

Measuring Financial Cycles with a Model-Based Filter : Empirical Evidence for the United States and the Euro Area

Galati, Gabriele - 2016

The financial cycle captures systematic patterns in the financial system and is closely related to the concept of procyclicality of systemic risk. This paper investigates the characteristics of financial cycles using a multivariate model-based filter. We extract cycles using an unobserved...

Persistent link: https://www.econbiz.de/10013000400

Modeling the Business and Financial Cycle in a Multivariate Structural Time Series Model

de Winter, Jasper - 2017

We consider a multivariate unobserved component time series model to disentangle the short-term and medium-term cycle for the G7 countries and the Netherlands using four key macroeconomic and financial time series. The novel aspect of our approach is that we simultaneously decompose the...

Persistent link: https://www.econbiz.de/10012945122

Likelihood-based analysis for dynamic factor models

Jungbacker, Borus; Koopman, Siem Jan - 2008

We present new results for the likelihood-based analysis of the dynamic factor model that possibly includes intercepts and explanatory variables. The latent factors are modelled by stochastic processes. The idiosyncratic disturbances are specified as autoregressive processes with mutually...

Persistent link: https://www.econbiz.de/10011373811

The stochastic volatility in mean model

Koopman, Siem Jan; Hol Uspensky, Eugenie - 2000

In this paper we present an exact maximum likelihood treatment forthe estimation of a Stochastic Volatility in Mean(SVM) model based on Monte Carlo simulation methods. The SVM modelincorporates the unobserved volatility as anexplanatory variable in the mean equation. The same extension...

Persistent link: https://www.econbiz.de/10011303314