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Is there a role for governments in emerging countries to accelerate economic development by intervening in product and factor markets? To address this question, we study optimal dynamic Ramsey policies in a standard growth model with financial frictions. The optimal policy intervention involves pro-business policies like suppressed wages in early stages of the transition, resulting in higher entrepreneurial profits and faster wealth accumulation. This, in turn, relaxes borrowing constraints in the future, leading to higher labor productivity and wages. In the long run, optimal policy reverses sign and becomes pro-worker. In a multi-sector extension, optimal policy subsidizes sectors with a latent comparative advantage and, under certain circumstances, involves a depreciated real exchange rate. Our results provide an efficiency rationale, but also identify caveats, for many of the development policies actively pursued by dynamic emerging economies.

We show how to construct optimal policy projections in Ramses, the Riksbank's open-economy medium-sized dynamic stochastic general equilibrium model for forecasting and policy analysis. Bayesian estimation of the parameters of the model indicates that they are relatively invariant to alternative policy assumptions and supports our view that the model parameters may be regarded as unaffected by the monetary policy specification.We discuss how monetary policy, and in particular the choice of output gap measure, affects the transmission of shocks. Finally, we use the model to assess the recent Great Recession in the world economy and how its impact on the economic development in Sweden depends on the conduct of monetary policy. This provides an illustration on how Rames incoporates large international spillover effects.

We determine optimal monetary policy under commitment in a forward-looking New Keynesian model when nominal interest rates are bounded below by zero. The lower bound represents an occasionally binding constraint that causes the model and optimal policy to be nonlinear. A calibration to the U.S. economy suggests that policy should reduce nominal interest rates more aggressively than suggested by a model without lower bound. Rational agents anticipate the possibility of reaching the lower bound in the future and this amplifies the effects of adverse shocks well before the bound is reached. While the empirical magnitude of U.S. mark-up shocks seems too small to entail zero nominal interest rates, shocks affecting the natural real interest rate plausibly lead to a binding lower bound. Under optimal policy, however, this occurs quite infrequently and does not imply positive average inflation rates in equilibrium. Interestingly, the presence of binding real rate shocks alters the policy response to (non-binding) markup shocks.

How should monetary authorities react to an oil price shock? This paper shows that in a noncompetitive economy, policies that perfectly stabilize prices entail large welfare costs, hence explaining the reluctance of policymakers to enforce them. The policy trade-off is nontrivial because oil (energy) is an input to both production and consumption. As welfaremaximizing policies are hard to implement and communicate, I derive a simple interest rate rule that depends only on observables but mimics the optimal plan in all dimensions. The optimal rule is hard on core inflation but accommodates oil price changes.

Manufacturers make production decisions and carry inventory to satisfy uncertain demand. When holding and shortage costs are high, carrying inventory could be even more expensive for a capacitated production system. Recent developments in information technology and sales strategies enabled firms to acquire, collect, or induce advance demand information. We address a periodic-review, stochastic, capacitated, finite and infinite horizon production system faced by a manufacturer who has the ability to obtain advance demand information. We establish optimal policies and characterize their behavior with respect to capacity, fixed costs, advance demand information, and the planning horizon. With a numerical study, we quantify the value of advance demand information and additional capacity for specific problem instances. We illustrate how advance demand information can be a substitute for capacity and inventory.

This paper deals with denumerable continuous-time Markov decision processes (MDP) with constraints. The optimality criterion to be minimized is expected discounted loss, while several constraints of the same type are imposed. The transition rates may be unbounded, the loss rates are allowed to be unbounded as well (from above and from below), and the policies may be history-dependent and randomized. Based on Kolmogorov's forward equation and Dynkin's formula, we remind the reader about the Bellman equation, introduce and study occupation measures, reformulate the optimization problem as a (primary) linear program, provide the form of optimal policies for a constrained optimization problem here, and establish the duality between the convex analytic approach and dynamic programming. Finally, a series of examples is given to illustrate all of our results.

This paper considers repositioning empty containers between multi-ports over multi-periods with stochastic demand and lost sales. The objective is to minimize the total operating cost including container-holding cost, stockout cost, importing cost and exporting cost. First, we formulate the single-port case as an inventory problem over a finite horizon with stochastic import and export of empty containers. The optimal policy for period n is characterized by a pair of critical points (A n , S n ), that is, importing empty containers up to A n when the number of empty containers in the port is fewer than A n ; exporting empty containers down to S n when the number of empty containers in the port is more than S n ; and doing nothing, otherwise. A polynomial-time algorithm is developed to determine the two thresholds, that is, A n and S n , for each period. Next, we formulate the multi-port problem and determine a tight lower bound on the cost function. On the basis of the two-threshold optimal policy for a single port, a polynomial-time algorithm is developed to find an approximate repositioning policy for multi-ports. Simulation results show that the proposed approximate repositioning algorithm performs very effectively and efficiently.

This paper is devoted to studying constrained continuous-time Markov decision processes (MDPs) in the class of randomized policies depending on state histories. The transition rates may be unbounded, the reward and costs are admitted to be unbounded from above and from below, and the state and action spaces are Polish spaces. The optimality criterion to be maximized is the expected discounted rewards, and the constraints can be imposed on the expected discounted costs. First, we give conditions for the nonexplosion of underlying processes and the finiteness of the expected discounted rewards/costs. Second, using a technique of occupation measures, we prove that the constrained optimality of continuous-time MDPs can be transformed to an equivalent (optimality) problem over a class of probability measures. Based on the equivalent problem and a so-called w̄-weak convergence of probability measures developed in this paper, we show the existence of a constrained optimal policy. Third, by providing a linear programming formulation of the equivalent problem, we show the solvability of constrained optimal policies. Finally, we use two computable examples to illustrate our main results.

We analyze a dynamic stochastic general-equilibrium (DSGE) model with an externality—through climate change—from using fossil energy. Our central result is a simple formula for the marginal externality damage of emissions (or, equivalently, for the optimal carbon tax). This formula, which holds under quite plausible assumptions, reveals that the damage is proportional to current GDP, with the proportion depending only on three factors: (i) discounting, (ii) the expected damage elasticity (how many percent of the output flow is lost from an extra unit of carbon in the atmosphere), and (iii) the structure of carbon depreciation in the atmosphere. Thus, the stochastic values of future output, consumption, and the atmospheric CO 2 concentration, as well as the paths of technology (whether endogenous or exogenous) and population, and so on, all disappear from the formula. We find that the optimal tax should be a bit higher than the median, or most well-known, estimates in the literature. We also formulate a parsimonious yet comprehensive and easily solved model allowing us to compute the optimal and market paths for the use of different sources of energy and the corresponding climate change. We find coal—rather than oil—to be the main threat to economic welfare, largely due to its abundance. We also find that the costs of inaction are particularly sensitive to the assumptions regarding the substitutability of different energy sources and technological progress.

The design and analysis of optimal monetary policy is usually guided by the paradigm of homogeneous rational expectations. Instead, we examine the dynamic consequences of design and implementation strategies, when the actual economy features expectational heterogeneity. Agents have either rational or adaptive expectations. Consequently, the central bank's ability to achieve price stability under heterogeneous expectations depends on its objective and implementation strategy. An expectations-based reaction function, which appropriately conditions on private sector expectations, performs exceptionally well. However, once the objective introduces policy inertia, popular strategies have similar determinacy properties, but they are less operational. This finding calls for new implementation strategies under interest rate stabilization.