A Monte Carlo study of a factor analytical method for fixed-effects dynamic panel models

In a recent article Bai (2013a) proposes a new factor analytical method (FAM) for the estimation of fixed-effects dynamic panel data models, which has the unique and very useful property that it is asymptotically bias free. In this paper we provide Monte Carlo evidence of the good small-sample performance of FAM, that complement Bai’s theoretical study.     

Two decades of XBRL: a science mapping of research trends and future research agenda

eXtensible Business Reporting Language (XBRL) is a software standard of significant importance in the current shift in financial reporting. This new standard is representative of the dramatic developments in how financial information is presented and reported. Over the past 20 years (2000–2020), we have witnessed how information systems and technology advancement have continued to shape current practices in accounting, which has resulted in an increasing trend toward Internet reporting, and thus the XBRL adoption. This study investigates the current trends and various characteristics of worldwide research on XBRL using bibliometric analysis, subsequently presenting a plausible future research direction. The Scopus database was searched for articles indexed under the terms “XBRL*” or “Digital Financial Reporting*” or “Internet Financial Reporting*” or “Web financial reporting*”, which yielded a total of 661 documents from the years 2000–2020. We analysed a total of 621 documents as our final sample after excluding those not related to the interest of the study. Our analysis discovered six (6) main research clusters related to XBRL, which are financial reporting, development of XBRL, decision making, XBRL adoption, corporate governance and disclosure, and financial analytics. We also proposed a future research direction within XBRL-related research.

     

CCE in fixed‐T panels

The presence of unobserved heterogeneity and its likely detrimental effect on inference has recently motivated the use of factor‐augmented panel regression models. The workhorse of this literature is based on first estimating the unknown factors using the cross‐section averages of the observables, and then applying ordinary least squares conditional on the first‐step factor estimates. This is the common correlated effects (CCE) approach, the existing asymptotic theory for which is based on the requirement that both the number of time series observations, T, and the number of cross‐section units, N, tend to infinity. The obvious implication of this theory for empirical work is that both N and T should be large, which means that CCE is impossible for the typical micro panel where only N is large. In the current paper, we put the existing CCE theory and its implications to a test. This is done by developing a new theory that enables T to be fixed. The results show that many of the previously derived large‐T results hold even if T is fixed. In particular, the pooled CCE estimator is still consistent and asymptotically normal, which means that CCE is more applicable than previously thought. In fact, not only do we allow T to be fixed, but the conditions placed on the time series properties of the factors and idiosyncratic errors are also much more general than those considered previously.     

Developing and validating the qualitative labour productivity measurement in service industry

Quality & Quantity - Over the past two centuries, labour productivity (LP) measured has long been synonymous with productivity measured in manufacturing quantitative features reflecting to its...