We develop an estimator for latent factors in a large-dimensional panel of financial data that can explain expected excess returns. Statistical factor analysis based on Principal Component Analysis (PCA) has problems identifying factors with a small variance that are important for asset pricing....

We propose a new approach of latent factor analysis that, in addition to the main panel of interest, introduces other relevant data that serve as instruments for dynamic factor loadings.The method, called IPCA, provides a parsimonious means of incorporating vast conditioning information into...

This paper proposes a constrained principal components (CnPC) estimator for efficient estimation of large-dimensional factor models when errors are crosssectionally correlated and the number of cross-sections (N) may be larger than the number of observations (T). Although principal components...

Understanding the irrational sentiments of the market participants is necessary for making good investment decisions. Despite the recent academic effort to examine the role of investors’ sentiments in market dynamics, there is a lack of consensus in delineating the structural aspect of market...

This paper puts forward a new instrumental variables (IV) approach for linear panel datamodels with interactive effects in the error term and regressors. The instruments are transformed regressors and so it is not necessary to search for external instruments. The proposed method asymptotically...

The main contribution of this work consist on studying sales behaviour and their relationship with local market conditions like labor market indicators through a time series principal component analysis. We study the correlation structure of a large database on prices and found that all product...

The studies concerning commonality in liquidity on emerging markets in Central and Eastern Europe are scarce and, in particular, they do not utilize the Principal Component Analysis (PCA) to identify latent factors in liquidity. Therefore, the main aim of this research is to assess commonality...