In the literature technical change is mostly assumed to be exogenous and specified as a function of time. However, some exogenous external factors other than time can also affect technical change. In this paper we model technical change via time trend (purely external non-economic) as well as...

In the literature technical change is mostly assumed to be exogenous and specified as a function of time. However, some exogenous external factors other than time can also affect technical change. In this paper we model technical change via time trend (purely external non-economic) as well as...

We employ a two-step approach in investigating the dynamic transmission channels under which globalization factors foster technical efficiency by combining a dynamic efficiency analysis in the stochastic frontier framework, and a time series approach based on VAR and spectral analysis. Using the...

Risk management is now present in many economic sectors. This paper investigates the role of risk management in creating value for financial institutions by analyzing U.S. property-liability insurers. Property-liability insurers are financial intermediaries whose primary role in the economy is...

In this paper we investigate whether accounting for non-pervasive shocks improves the forecast of a factor model. We compare four models on a large panel of US quarterly data: factor models, factor models estimated on selected variables, Bayesian shrinkage, and factor models together with...

In this paper we propose to exploit the heterogeneity of forecasts produced by different model specifications to measure forecast uncertainty. Our approach is simple and intuitive.It consists in selecting all the models that outperform some benchmark model, and then to construct an empirical...

I develop methods that produce consistent estimates of the Vasicek-Basel IRB (VAIRB) credit risk model parameters. I apply these methods to Moody's data on corporate defaults over the period 1920–2008 and assess the model fit and construct hypothesis tests using bootstrap methods. The results...

In this paper, we exploit the heterogeneity in the forecasts obtained by estimating different factor models to measure forecast uncertainty. Our approach is simple and intuitive. It consists first in selecting all the models that outperform some benchmark model, and then in constructing an...

Measuring bias is important as it helps identify flaws in quantitative forecasting methods or judgmental forecasts. It can, therefore, potentially help improve forecasts. Despite this, bias tends to be under represented in the literature: many studies focus solely on measuring accuracy. Methods...

This chapter presents a unified set of estimation methods for fitting a rich array of models describing dynamic relationships within a longitudinal data setting. The discussion surveys approaches for characterizing the micro dynamics of continuous dependent variables both over time and across...