In this paper, we empirically examine whether the assumptions and predictions of the Hotelling model are consistent with patterns observed in data. We consider nonlinear functional forms for the extraction cost and resource demand to develop an empirical Hotelling model with technological...

This paper introduces a factor-augmented forecasting regression model in the presence of threshold effects. We consider least squares estimation of the regression parameters, and establish asymptotic theories for estimators of both slope coefficients and the threshold parameter. Prediction...

This chapter surveys recent econometric methodologies for inference in large dimensional conditional factor models in finance. Changes in the business cycle and asset characteristics induce time variation in factor loadings and risk premia to be accounted for. The growing trend in the use of...

We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no arbitrage restrictions by regularizing appropriate groups of coefficients. The second...

We propose a method for forecasting individual outcomes and estimating random effects in linear panel data models and value-added models when the panel has a short time dimension. The method is robust, trivial to implement and requires minimal assumptions. The idea is to take a weighted average...

This paper presents one of the inflation forecasting models used by the Magyar Nemzeti Bank in its recent inflation forecasts. The model attempts to integrate all the properties of the former models considered by the author as being advantageous and desirable into a unified framework. Thus, this...

In the context of the multivariate Normal regression model, a mean squared error of prediction is developed for making the choice of subset of explanatory variables for predicting the response variable in future samples

A general statistical modeling problem is that given a class of competing models and new data, how one can improve the overall model performance. In general, there exist two solutions for this problem, namely model selection and model combination. Model selection is to select a single best model...