THE RELATIONSHIP BETWEEN EFFICIENCY WAGES AND PRICE INDEXATION IN A NOMINAL WAGE CONTRACTING MODEL

This paper examines how a link between efficiency wages and price indexation can arise in a simple nominal wage contracting model. We show that, the more elastic the worker¡¯s effort is with respect to real wages, the looser the optimal linkage of nominal wages is to the price level. This is simply because, as the worker¡¯s effort becomes more sensitive to real wages, the output-stabilizing indexing scheme has to make nominal wages less dependent on the price level, thereby delegating firms more flexibility for adjusting the output level. As long as efficiency wages as an incentive device work well in the economy, our result may help explain the recent decline in the share of U.S. union contracts adjusting to a cost of living index.Efficiency Wages, Wage Indexation, Nominal Wage Contracting

Epidemiological Prediction using Deep Learning

Department of Mathematical SciencesAccurate and real-time epidemic disease prediction plays a significant role in the health system and is of great importance for policy making, vaccine distribution and disease control. From the SIR model by Mckendrick and Kermack in the early 1900s, researchers have developed a various mathematical model to forecast the spread of disease. With all attempt, however, the epidemic prediction has always been an ongoing scientific issue due to the limitation that the current model lacks flexibility or shows poor performance. Owing to the temporal and spatial aspect of epidemiological data, the problem fits into the category of time-series forecasting. To capture both aspects of the data, this paper proposes a combination of recent Deep Leaning models and applies the model to ILI (influenza like illness) data in the United States. Specifically, the graph convolutional network (GCN) model is used to capture the geographical feature of the U.S. regions and the gated recurrent unit (GRU) model is used to capture the temporal dynamics of ILI. The result was compared with the Deep Learning model proposed by other researchers, demonstrating the proposed model outperforms the previous methods.clos

On radial and conical Fourier multipliers

We investigate connections between radial Fourier multipliers on $R^d$ and certain conical Fourier multipliers on $R^{d+1}$. As an application we obtain a new weak type endpoint bound for the Bochner-Riesz multipliers associated to the light cone in $R^{d+1}$, where $d\ge 4$, and results on characterizations of $L^p\to L^{p,\nu}$ inequalities for convolutions with radial kernels