The paper describes the specification, estimation, and testing of an unrestricted structural econometric model design to explain and forecast individual returns of securities listed on the Brazilian stock market. The model's explanatory variables include macroeconomic, fundamental and...

This paper investigates the relationship linking investment (capital stock) and structural policies. Using a panel of 32 OECD countries from 1985 to 2013, we show that more stringent product and labour market regulations are associated with less investment (lower capital stock). The paper also...

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...

We build a simple diagnostic criterion for approximate factor structure in large panel datasets. Given observable factors, the criterion checks whether the errors are weakly cross-sectionally correlated or share at least one unobservable common factor (interactive effects). A general version...

In this paper, we consider a partially linear panel data model with cross-sectional dependence and non-stationarity. Meanwhile, we allow fixed effects to be correlated with the regressors to capture unobservable heterogeneity. Under a general spatial error dependence structure, we then establish...

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...

The GMM estimator that is usually employed in the panel data literature, has an unbounded influence function. This means that the estimator is easily influenced by outliers in the data. This paper develops a variant of the GMM estimator that is less sensitive to anomalous observations....

Model selection and model combination is a general problem in many areas. Especially, when we have several different candidate models and also have gathered a new data set, we want to construct a more accurate and precise model in order to help predict future events. In this paper, we propose a...

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...

The presence of discontinuities in the January rounds of Philippine Unemployment Data from 1981-2006 is investigated by way of modeling these data as a noisy Compound Gauss-Markov Random Fields (CGMRF). The likelihood and prior hyper-parameters are respectively estimated with wavelet shrinkage...