State Space Models and MIDAS Regressions

We examine the relationship between MIDAS regressions and Kalman filter state space models applied to mixed frequency data. In general, the latter involves a system of equations, whereas in contrast MIDAS regressions involve a (reduced form) single equation. As a consequence, MIDAS regressions might be less efficient, but also less prone to specification errors. First we examine how MIDAS regressions and Kalman filters match up under ideal circumstances, that is in population, and in cases where all the stochastic processes - low and high frequency - are correctly specified by a linear state space model. We characterize cases where the MIDAS regression exactly replicates the steady state Kalman filter weights. In cases where the MIDAS regression is only an approximation, we compute the approximation error and find it to be small (using two different metrics). We also study how MIDAS regressions perform in comparison to the Kalman filter when the latter is subject to specification errors. Our findings favor MIDAS regressions, as their approximation errors are typically small in comparison to the model specification errors of the Kalman filter. The paper concludes with an empirical application comparing MIDAS and Kalman filtering to predict future GDP growth, using monthly macroeconomic series

Synthesis, Characterisation, and Preliminary Anti-Cancer Photodynamic Therapeutic \u3ci\u3eIn Vitro\u3c/i\u3e Studies of Mixed-Metal Binuclear Ruthenium(II)-Vanadium(IV) Complexes

We report the synthesis and characterisation of mixed-metal binuclear ruthenium(II)-vanadium(IV) complexes, which were used as potential photodynamic therapeutic agents for melanoma cell growth inhibition. The novel complexes, [Ru(pbt)2(phen2DTT)](PF6)2•1.5H2O 1 (where phen2DTT = 1,4-bis(1,10-phenanthrolin-5-ylsulfanyl)butane-2,3-diol and pbt = 2-(2\u27-pyridyl)benzothiazole) and [Ru(pbt)2(tpphz)](PF6)2•3H2O 2 (where tpphz = tetrapyrido[3,2-a:2′,3′-c:3″,2″-h:2‴,3‴-j]phenazine) were synthesised and characterised. Compound 1 was reacted with [VO(sal-L-tryp)(H2O)] (where sal-L-tryp = N-salicylidene-L-tryptophanate) to produce [Ru(pbt)2(phen2DTT)VO(sal-L-tryp)](PF6)2•5H2O 4; while [VO(sal-L-tryp)(H2O)] was reacted with compound 2 to produce [Ru(pbt)2(tpphz)VO(sal-L-tryp)](PF6)2•6H2O 3. All complexes were characterised by elemental analysis, HRMS, ESI MS, UV-visible absorption, ESR spectroscopy, and cyclic voltammetry, where appropriate. In vitro cell toxicity studies (with the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) colorimetric assay) via dark and light reaction conditions were carried out with sodium diaqua-4,4\u27,4”,4”\u27tetrasulfophthalocyaninecobaltate(II) (Na4[Co(tspc)(H2O)2]), [VO(sal-L-tryp)(phen)]•H2O, and the chloride salts of complexes 3 and 4. Such studies involved A431, human epidermoid carcinoma cells; human amelanotic malignant melanoma cells; and HFF, non-cancerous human skin fibroblast cells. Both chloride salts of complexes 3 and 4 were found to be more toxic to melanoma cells than to non-cancerous fibroblast cells, and preferentially led to apoptosis of the melanoma cells over non-cancerous skin cells. The anti-cancer property of the chloride salts of complexes 3 and 4 was further enhanced when treated cells were exposed to light, while no such effect was observed on non-cancerous skin fibroblast cells. ESR and 51V NMR spectroscopic studies were also used to assess the stability of the chloride salts of complexes 3 and 4 in aqueous media at pH 7.19. This research illustrates the potential for using mixed-metal binuclear ruthenium(II)-vanadium(IV) complexes fighting skin cancer

Equity premium predictions with adaptive macro indexes

Fundamental economic conditions are crucial determinants of equity premia. However, commonly used predictors do not adequately capture the changing nature of economic conditions and hence have limited power in forecasting equity returns. To address the inadequacy, this paper constructs macro indexes from large data sets and adaptively chooses optimal indexes to predict stock returns. I find that adaptive macro indexes explain a substantial fraction of the short-term variation in future stock returns and have more forecasting power than both the historical average of stock returns and commonly used predictors. The forecasting power exhibits a strong cyclical pattern, implying the ability of adaptive macro indexes to capture time-varying economic conditions. This finding highlights the importance of using dynamically measured economic conditions to investigate empirical linkages between the equity premium and macroeconomic fundamentals.Stocks - Rate of return ; Forecasting ; Macroeconomics ; Economic indicators