Short run

About: Short run is a research topic. Over the lifetime, 11819 publications have been published within this topic receiving 232153 citations.

Monopolistic competition with outside goods

Abstract: The Chamberlinian monopolistically competitive equilibrium has been explored and extended in a number of recent papers. These analyses have paid only cursory attention to the existence of an industry outside the Chamberlinian group. In this article I analyze a model of spatial competition in which a second commodity is explicitly treated. In this two-industry economy, a zero-profit equilibrium with symmetrically located firms may exhibit rather strange properties. First, demand curves are kinked, although firms make "Nash" conjectures. If equilibrium lies at the kink, the effects of parameter changes are perverse. In the short run, prices are rigid in the face of small cost changes. In the long run, increases in costs lower equilibrium prices. Increases in market size raise prices. The welfare properties are also perverse at a kinked equilibrium.

Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets

Abstract: We construct two models of the behavior of consumers in an environment where there is uncertainty about brand attributes. In our models, both usage experience and advertising exposure give consumers noisy signals about brand attributes. Consumers use these signals to update their expectations of brand attributes in a Bayesian manner. The two models are 1 a dynamic model with immediate utility maximization, and 2 a dynamic “forward-looking” model in which consumers maximize the expected present value of utility over a planning horizon. Given this theoretical framework, we derive from the Bayesian learning framework how brand choice probabilities depend on past usage experience and advertising exposures. We then form likelihood functions for the models and estimate them on Nielsen scanner data for detergent. We find that the functional forms for experience and advertising effects that we derive from the Bayesian learning framework fit the data very well relative to flexible ad hoc functional forms such as exponential smoothing, and also perform better at out-of-sample prediction. Another finding is that in the context of consumer learning of product attributes, although the forward-looking model fits the data statistically better at conventional significance levels, both models produce similar parameter estimates and policy implications. Our estimates indicate that consumers are risk-averse with respect to variation in brand attributes, which discourages them from buying unfamiliar brands. Using the estimated behavioral models, we perform various scenario evaluations to find how changes in marketing strategy affect brand choice both in the short and long run. A key finding obtained from the policy experiments is that advertising intensity has only weak short run effects, but a strong cumulative effect in the long run. The substantive content of the paper is potentially of interest to academics in marketing, economics and decision sciences, as well as product managers, marketing research managers and analysts interested in studying the effectiveness of marketing mix strategies. Our paper will be of particular interest to those interested in the long run effects of advertising. Note that our estimation strategy requires us to specify explicit behavioral models of consumer choice behavior, derive the implied relationships among choice probabilities, past purchases and marketing mix variables, and then estimate the behavioral parameters of each model. Such an estimation strategy is referred to as “structural” estimation, and econometric models that are based explicitly on the consumer's maximization problem and whose parameters are parameters of the consumers' utility functions or of their constraints are referred to as “structural” models. A key benefit of the structural approach is its potential usefulness for policy evaluation. The parameters of structural models are invariant to policy, that is, they do not change due to a change in the policy. In contrast, the parameters of reduced form brand choice models are, in general, functions of marketing strategy variables e.g., consumer response to price may depend on pricing policy. As a result, the predictions of reduced form models for the outcomes of policy experiments may be unreliable, because in making the prediction one must assume that the model parameters are unaffected by the policy change. Since the agents in our models choose among many alternative brands, their choice probabilities take the form of higher-order integrals. We employ Monte-Carlo methods to approximate these integrals and estimate our models using simulated maximum likelihood. Estimation of the dynamic forward-looking model also requires that a dynamic programming problem be solved in order to form the likelihood function. For this we use a new approximation method based on simulation and interpolation techniques. These estimation techniques may be of interest to researchers and policy makers in many fields where dynamic choice among discrete alternatives is important, such as marketing, decision sciences, labor and health economics, and industrial organization.

Should Central Banks Respond to Movements in Asset Prices

Abstract: In recent decades, asset booms and busts have been important factors in macroeconomic fluctuations in both industrial and developing countries. In light of this experience, how, if at all, should central bankers respond to asset price volatility? We have addressed this issue in previous work (Bernanke and Gertler, 1999). The context of our earlier study was the relatively new, but increasingly popular, monetary-policy framework known as inflation-targeting (see e.g., Bernanke and Frederic Mishkin, 1997). In an inflation-targeting framework, publicly announced medium-term inflation targets provide a nominal anchor for monetary policy, while allowing the central bank some flexibility to help stabilize the real economy in the short run. The inflation-targeting approach gives a specific answer to the question of how central bankers should respond to asset prices: Changes in asset prices should affect monetary policy only to the extent that they affect the central bank’s forecast of inflation. To a first approximation, once the predictive content of asset prices for inflation has been accounted for, there should be no additional response of monetary policy to assetprice fluctuations. In use now for about a decade, inflationtargeting has generally performed well in practice. However, so far this approach has not often been stress-tested by large swings in asset prices. Our earlier research employed simulations of a small, calibrated macroeconomic model to examine how an inflation-targeting policy (defined as one in which the central bank’s instrument interest rate responds primarily to changes in expected inflation) might fare in the face of a boom-and-bust cycle in asset prices. We found that an aggressive inflationtargeting policy rule (in our simulations, one in which the coefficient relating the instrument interest rate to expected inflation is 2.0) substantially stabilizes both output and inflation in scenarios in which a bubble in stock prices develops and then collapses, as well as in scenarios in which technology shocks drive stock prices. Intuitively, inflation-targeting central banks automatically accommodate productivity gains that lift stock prices, while offsetting purely speculative increases or decreases in stock values whose primary effects are through aggregate demand. Conditional on a strong policy response to expected inflation, we found little if any additional gains from allowing an independent response of central-bank policy to the level of asset prices. In our view, there are good reasons, outside of our formal model, to worry about attempts by central banks to influence asset prices, including the fact that (as history has shown) the effects of such attempts on market psychology are dangerously unpredictable. Hence, we concluded that inflationtargeting central banks need not respond to asset prices, except insofar as they affect the inflation forecast. In the spirit of recent work on robust control, the exercises in our earlier paper analyzed the performance of policy rules in worst-case † Discussants: Robert Shiller, Yale University; Glenn Rudebusch, Federal Reserve Bank of San Francisco; Kenneth Rogoff, Harvard University.

Theory of production

Abstract: This chapter focuses on the motivations, objectives, and decisions of the producer. A producer's primary aim is to produce and sell the goods, services, and make profits. In economics terminology, the final or finished product is the output, while the resources used for production are the inputs. The production method, or a combination of methods and processes, is called technology. The production function is a technical relationship between the output and the inputs under a specified set of conditions. More complex functions relate many different forms of output to several variables. The inputs may be classified as being either fixed or variable, depending on the circumstances. Those inputs that may be varied are usually called variable inputs, while those whose quantities are invariant are referred to as fixed inputs. Production theory is discussed in terms of either the short run or the long run. These “runs” do not necessarily imply different lengths of time, but rather the circumstances surrounding production.