A Practitioner's Guide and MATLAB Toolbox for Mixed Frequency State Space Models

The use of mixed frequency data is now common in many applications, ranging from the analysis of high frequency financial time series to large cross-sections of macroeconomic time series. In this article, we show how state space methods can easily facilitate both estimation and inference in these settings. After presenting a unified treatment of the state space approach to mixed frequency data modeling, we provide a series of applications to demonstrate how our MATLAB toolbox can make the estimation and post-processing of these models straightforward

Engineering HIV-Resistant Human CD4+ T Cells with CXCR4-Specific Zinc-Finger Nucleases

HIV-1 entry requires the cell surface expression of CD4 and either the CCR5 or CXCR4 coreceptors on host cells. Individuals homozygous for the ccr5Δ32 polymorphism do not express CCR5 and are protected from infection by CCR5-tropic (R5) virus strains. As an approach to inactivating CCR5, we introduced CCR5-specific zinc-finger nucleases into human CD4+ T cells prior to adoptive transfer, but the need to protect cells from virus strains that use CXCR4 (X4) in place of or in addition to CCR5 (R5X4) remains. Here we describe engineering a pair of zinc finger nucleases that, when introduced into human T cells, efficiently disrupt cxcr4 by cleavage and error-prone non-homologous DNA end-joining. The resulting cells proliferated normally and were resistant to infection by X4-tropic HIV-1 strains. CXCR4 could also be inactivated in ccr5Δ32 CD4+ T cells, and we show that such cells were resistant to all strains of HIV-1 tested. Loss of CXCR4 also provided protection from X4 HIV-1 in a humanized mouse model, though this protection was lost over time due to the emergence of R5-tropic viral mutants. These data suggest that CXCR4-specific ZFNs may prove useful in establishing resistance to CXCR4-tropic HIV for autologous transplant in HIV-infected individuals

Competition Within a Cartel: Correction

The model of league conduct formulated by Ferguson, Jones, and Stewart (2000) contains an algebraic error. This note provides the relevant correction and shows that the empirical results given in their article are robust to it.