Econometric Analysis of Panel Data Models with Multifactor Error Structures

Economic panel data often exhibit cross-sectional dependence, even after conditioning on appropriate explanatory variables. Two approaches to modeling cross-sectional dependence in economic panel data are often used: the spatial dependence approach, which explains cross-sectional dependence in terms of distance among units, and the residual multifactor approach, which explains cross-sectional dependence by common factors that affect individuals to a different extent. This article reviews the theory on estimation and statistical inference for stationary and nonstationary panel data with cross-sectional dependence, particularly for models with a multifactor error structure. Tests and diagnostics for testing for unit roots, slope homogeneity, cointegration, and the number of factors are provided. We discuss issues such as estimating common factors, dealing with parameter plethora in practice, testing for structural stability and nonlinearity, and dealing with model and parameter uncertainty. Finally, we address issues related to the use of these economic panel models.

A Multifactor Approach to Mild Cognitive Impairment

Mild cognitive impairment (MCI) represents an intermediate stage between normal cognition and dementia. Individuals with MCI are at increased risk of conversion to dementia, and the rate of progression of MCI to dementia is dependent on age, gender, and education. MCI may be diagnosed using neuropsychological criteria using cut-offs representing decrements in cognition, or using criteria to assess for a decline in functional status. The ability to determine the status of dementia-related biomarkers has allowed for better staging and prognostication in different forms of MCI. MCI is now recognized as a significant target stage for future therapies. These future therapies aim to reduce the rate of conversion of individuals with MCI to dementia. In this article, we review different conceptions of MCI, the diagnosis and prognostication of MCI, and presently available management approaches for this condition.