The contributions of error distributions have been ignored while modeling stock market volatility in Nigeria and studies have shown that the application of appropriate error distribution in volatility model enhances efficiency of the model. Using Nigeria All Share Index from January 2, 2008 to...

Since the influential paper of Stock and Watson (2002), the dynamic factor model (DFM) has been widely used for forecasting macroeconomic key variables such as GDP. However, the DFM has some weaknesses. For nowcasting, the dynamic factor model is modified by using the mixed data sampling...

We assess the effectiveness of the forward guidance undertaken by European Central Bank using a standard medium-scale DSGE model à la Smets and Wouters (2007). Exploiting data on expectations from surveys, we show that incorporating expectations should be crucial in performance evaluation of...

We test two questions: (i) Is the Bayesian Information Criterion (BIC) more parsimonious than Akaike Information Criterion (AIC)?, and (ii) Is BIC better than AIC for forecasting purposes? By using simulated data, we provide statistical inference of both hypotheses individually and then jointly...

In this paper we undertake an out-of-sample evaluation of the ability of a model to forecast the Swedish Krona's real and nominal effective exchange rate, using a cointegrating relation between the real exchange rate, relative output, terms of trade and net foreign assets (or alternatively the...

We present an approach to forecast customer orders of ready-to-launch new products that are similar to past products. The approach fits product life cycle (PLC) curves to historical customer order data, clusters the curves of similar products, and uses the representative curve of the new...

In this paper, we propose and evaluate a shrinkage based methodology that is designed to improve the accuracy of forecasts of daily integrated volatility. Our approach is based on a two-step shrinkage procedure designed to extract latent common volatility factors from a large dimensional and...

We introduce a new class of stochastic volatility models with autoregressive moving average (ARMA) innovations. The conditional mean process has a flexible form that can accommodate both a state space representation and a conventional dynamic regression. The ARMA component introduces serial...