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Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors

Bennedsen, Mikkel; Hillebrand, Eric; Koopman, Siem Jan - In: Energy economics 96 (2021), pp. 1-17

Persistent link: https://www.econbiz.de/10012818590

Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment

Koopman, Siem Jan; Ooms, Marius; Hindrayanto, Irma - Tinbergen Institute - 2006

This paper discusses identification, specification, estimation and forecasting for a general class of periodic … formulations are introduced for exact maximum likelihood estimation, component estimation and forecasting. Identification issues …

Persistent link: https://www.econbiz.de/10005137026

Likelihood-based Analysis for Dynamic Factor Models

Jungbacker, Borus; Koopman, Siem Jan - Tinbergen Institute - 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/10005137376

An Alternative Bayesian Approach to Structural Breaks in Time Series Models

Hauwe, Sjoerd van den; Paap, Richard; Dijk, Dick J.C. van - Tinbergen Institute - 2011

We propose a new approach to deal with structural breaks in time series models. The key contribution is an alternative dynamic stochastic specification for the model parameters which describes potential breaks. After a break new parameter values are generated from a so-called baseline prior...

Persistent link: https://www.econbiz.de/10008838634

The Stochastic Volatility in Mean Model

Koopman, Siem Jan; Uspensky, Eugenie Hol - Tinbergen Institute - 2000

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

Persistent link: https://www.econbiz.de/10005281817

Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility

Hol, Eugenie; Koopman, Siem Jan - Tinbergen Institute - 2000

daily squared returns and intradaily squared returns for forecasting horizons ranging from 1 to 10 days. For the daily …

Persistent link: https://www.econbiz.de/10005281987

Nowcasting and Forecasting Economic Growth in the Euro Area using Principal Components

Hindrayanto, Irma; Koopman, Siem Jan; Winter, Jasper de - Tinbergen Instituut - 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/10011257503

An Alternative Bayesian Approach to Structural Breaks in Time Series Models

Hauwe, Sjoerd van den; Paap, Richard; Dijk, Dick J.C. van - Tinbergen Instituut - 2011

Persistent link: https://www.econbiz.de/10011257521

Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility

Hol, Eugenie; Koopman, Siem Jan - Tinbergen Instituut - 2000

returns and intradaily squared returns for forecasting horizons rangingfrom 1 to 10 days. For the daily squared returns we …

Persistent link: https://www.econbiz.de/10011255461

Low Frequency and Weighted Likelihood Solutions for Mixed Frequency Dynamic Factor Models

Blasques, Francisco; Koopman, Siem Jan; Mallee, Max - Tinbergen Instituut - 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/10011256800