In this paper we develop a structural equation model with latent variables in an ordinal setting which allows us to test broker-dealer predictive ability of financial market movements. We use a multivariate logit model in a latent factor framework, develop a tractable estimator based on a...

This paper outlines a method to forecast FX spot rates. The data set consists of the Bloomberg FX spot rates for emerging markets as defined by Bloomberg. The in-sample data set consisted of weekly FX spot rates for ten Emerging markets, from August 2013 to March 2019. The out sample spanned...

This paper introduces a parsimonious and yet flexible semiparametric model to forecast financial volatility. The new model extends the linear nonnegative autoregressive model of Barndorff-Nielsen and Shephard (2001) and Nielsen and Shephard (2003) by way of a power transformation. It is...

This paper details efforts at developing and estimating a Vector Autoregressive (VAR) econometric model representative of the financial statements of a firm. Although the model can be generalized to represent the financial statements of any firm, this work was carried out as a case study, where...

This paper offers a framework for judging when the discrepancy embodied in current-account forecasts is large. The first step in implementing this framework involves developing an econometric model explaining the components of the aggregate discrepancy, estimating the associated parameters, and...

In recent years, the international community has been increasing its efforts to reduce the human footprint on air pollution and global warming. Total CO2 emissions are a key component of global emissions, and as such, they are closely monitored by national and supranational entities. This study...

This study applies a model averaging approach to conditionally forecast housing investment in the largest euro area countries and the euro area. To account for substantial modelling uncertainty, it estimates many vector error correction models (VECMs) using a wide set of short and long-run...

We propose a new machine learning-based framework for long-term mortality forecasting. Based on ideas of neighbouring prediction, model ensembling, and tree boosting, this framework can significantly improve the prediction accuracy of long-term mortality. In addition, the proposed framework...

This study applies a model averaging approach to conditionally forecast housing investment in the largest euro area countries and the euro area. To account for substantial modelling uncertainty, it estimates many vector error correction models (VECMs) using a wide set of short and long-run...

Electricity price forecasting has become an area of increasing relevance in recent years. Despite the growing interest in predictive algorithms, the challenges are difficult to overcome given the restricted access to relevant data series and the lack of accurate metrics. Multiple models have...