The choice and estimation of joint probability distribution function are key steps in portfolio optimization problem. As such distribution functions pair-copula constructions (PCC), or vine-copulae, on arbitrary R-vines are used. For the investor with exponential utility criterion the NYSE oil...

We propose a new approach to predictive density modeling that allows for MI- DAS e¤ects in both the ?rst and second moments of the outcome and develop Gibbs sampling methods for Bayesian estimation in the presence of stochastic volatility dy- namics. When applied to quarterly U.S. GDP growth...

Our proposed local vector autoregressive (LVAR) model has time-varying parameters that allow it to be safely used in both stationary and non-stationary situations. The estimation is conducted over an interval of local homogeneity where the parameters are approximately constant. The local...

We propose an Adaptive Dynamic Nelson-Siegel (ADNS) model to adaptively forecast the yield curve. The model has a simple yet flexible structure and can be safely applied to both stationary and nonstationary situations with different sources of change. For the 3- to 12-months ahead out-of-sample...

This paper addresses the issue of forecasting the term structure. We provide a unified state-space modelling framework that encompasses different existing discrete-time yield curve models. Within such framework we analyze the impact of two modelling choices, namely the imposition of no-arbitrage...

We reassess the predictability of U.S. recessions at horizons from three months to two years ahead for a large number of previously proposed leading-indicator variables. We employ an efficient probit estimator for partially missing data and assess relative model performance based on the receiver...

This paper proposes a new approach for nowcasting as yet unavailable GDP growth by estimating monthly GDP growth with a large dataset. The model consists of two parts: (i) a few indicators that explain a large part of the variation in GDP growth, and (ii) principal components, which are...

Aylık sanayi üretim endeksi, hem politika yapıcılar hem de akademisyenler tarafından ekonometrik tahminlerde oldukça sık kullanılan bir zaman serisidir. Bu makalede, düzey, logaritmik, mevsimsellikten arındırılmış düzey ve mevsimsellikten arındırılmış logaritmik Türkiye...

This study estimates a composite leading business cycle indicator for the Uruguayan economy following the methodology of The Conference Board. Prediction is based on the analysis of multiple series that have a leading relationship to the Industrial Production Index, which is used as the...

This study develops a simple forecasting model using Japanese prefectural data. The Markov chain, known as a stochastic model, corresponds to a first-order vector auto-regressive (VAR) model. If the transition probability matrix can be appropriately estimated, a forecasting model using the...