Monetary Policy Rules and Transmission Mechanisms Under Inflation Targeting in Israel

This paper analyzes Israel's recent inflation targeting policies and their role in the disinflation process in the 1990s. Special features of Israel underlying inflation targeting are: a high-inflation history; lack of consensus about the benefits from reducing inflation and thereby lack of full credibility of monetary policy; the existence of monetary policy overburdening in its attempts to meet the inflation targets; the coexistence of an exchange rate band together with the inflation targets. A key finding of the econometric analysis is that there is a time-varying passthrough from exchange rates to prices, which depends on the state of the business cycle and the size of exchange rate fluctuations. In the present empirical specifications, monetary conditions are shown to have played a key role in accounting for the various turning points along the disinflation process. Estimates of an analogue of a 'Taylor rule' indicate that in contrast with the monetary accommodation that prevailed in the past, monetary policy in the more recent years has acted as an inflation stabilizer.

Generalized Assignment via Submodular Optimization with Reserved Capacity

We study a variant of the generalized assignment problem (GAP) with group constraints. An instance of (Group GAP) is a set I of items, partitioned into L groups, and a set of m uniform (unit-sized) bins. Each item i in I has a size s_i >0, and a profit p_{i,j} >= 0 if packed in bin j. A group of items is satisfied if all of its items are packed. The goal is to find a feasible packing of a subset of the items in the bins such that the total profit from satisfied groups is maximized. We point to central applications of Group GAP in Video-on-Demand services, mobile Device-to-Device network caching and base station cooperation in 5G networks. Our main result is a 1/6-approximation algorithm for Group GAP instances where the total size of each group is at most m/2. At the heart of our algorithm lies an interesting derivation of a submodular function from the classic LP formulation of GAP, which facilitates the construction of a high profit solution utilizing at most half the total bin capacity, while the other half is reserved for later use. In particular, we give an algorithm for submodular maximization subject to a knapsack constraint, which finds a solution of profit at least 1/3 of the optimum, using at most half the knapsack capacity, under mild restrictions on element sizes. Our novel approach of submodular optimization subject to a knapsack with reserved capacity constraint may find applications in solving other group assignment problems