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About this book

This volume consists of six essays that develop and/or apply "rational expectations equilibrium inventory models" to study the time series behavior of production, sales, prices, and inventories at the industry level. By "rational expectations equilibrium inventory model" I mean the extension of the inventory model of Holt, Modigliani, Muth, and Simon (1960) to account for: (i) discounting, (ii) infinite horizon planning, (iii) observed and unobserved by the "econometrician" stochastic shocks in the production, factor adjustment, storage, and backorders management processes of firms, as well as in the demand they face for their products; and (iv) rational expectations. As is well known according to the Holt et al. model firms hold inventories in order to: (a) smooth production, (b) smooth production changes, and (c) avoid stockouts. Following the work of Zabel (1972), Maccini (1976), Reagan (1982), and Reagan and Weitzman (1982), Blinder (1982) laid the foundations of the rational expectations equilibrium inventory model. To the three reasons for holding inventories in the model of Holt et al. was added (d) optimal pricing. Moreover, the popular "accelerator" or "partial adjustment" inventory behavior equation of Lovell (1961) received its microfoundations and thus overcame the "Lucas critique of econometric modelling.

Table of Contents

Frontmatter

Chapter I. The Linear Rational Expectations Equilibrium Inventory Model: An Introduction

Abstract
This paper intends to serve as an introduction to the other papers in this volume. It develops a linear rational expectations equilibrium version of the Holt et al (1960) inventory model. The necessary conditions and the sufficient conditions for a solution are derived and the stability, cycling, and comparative dynamics properties of the solution are investigated.
Tryphon Kollintzas

Chapter II. Inventories and Price Fluctuations under Perfect Competition and Monopol

Abstract
This paper examines the impact of speculative inventories on the rational expectations competitive equilibrium laws of motion of prices, output and sales when firms are confronted with stochastic production and input choices are made prior to the realization of these shocks. The case of a monopolist producer-speculator is also considered. It is shown that a nonnegativity constraint on inventories will, occasionally be binding under both perfect competition and monopoly. Under these circumstances, there exists, for both industry structures, an inventory reservation price which has the property that inventories will be positive when the market price is less than the reservation price. When this is not the case, inventories will be zero. This result is established under general convex production and inventory holding costs.
An example is also provided for calculating the competitive and monopoly equilibria, when corners are taken into account and the shocks to production are independently and identically distributed. The comparative dynamics as well as the impact of industry structure on the means and variances of prices, inventories, production and consumption are analyzed.
S. Rao Aiyagari, Zvi Eckstein, Martin Eichenbaum

Chapter III. Temporal Aggregation and the Stock Adjustment Model of Inventories

Abstract
This paper examines the quantitative importance of temporal aggregation bias in distorting parameter estimates and hypothesis tests in the production smoothing/buffer stock model of inventories. In particular, our results are consistent with the Mundlak-Zellner hypothesis that temporal aggregation bias can account for the slow speeds of adjustment typically obtained in such a model.
Lawrence J. Christiano, Martin Eichenbaum

Chapter IV. A Linear Rational Expectations Equilibrium Model of the American Petroleum Industry

Abstract
This paper develops and estimates a model of the American petroleum industry. The model accounts for the storable and exhaustible nature of petroleum as well as the strategic interaction of agents operating in the stochastic environment of the markets for crude and refined petroleum products. The linear rational expectations modelling framework is adopted. The formulation and the econometric specification of the model are motivated by a statistical and vector autoregression analysis of the annual data for the post World War II period. The parameter estimates of the model over that period conform to the model’s restrictions. In addition, the overall fit of the model judged from the usual diagnostic statistics seems to be relatively good. Important findings of this empirical test are: a not too inelastic domestic demand for refined petroleum products; a marginal cost of domestic crude petroleum production that is an increasing function of cumulative production (exhaustibility), evidence of production smoothing inventory behavior; fast inventory adjustment to desired inventory levels; and finally, upward sloping foreign supplies of crude and refined petroleum products.
Sophia P. Dimelis, Tryphon Kollintzas

Chapter V. Seasonality, Cost Shocks, and the Production Smoothing Model of Inventories

Abstract
A great deal of research on the empirical behavior of inventories examines some variant of the production smoothing model of finished goods inventories. The overall assessment of this model that exists in the literature is quite negative: there is little evidence that manufacturers hold inventories of finished goods in order to smooth production patterns.
This paper examines whether this negative assessment of the model is due to one or both of two features: cost shocks and seasonal fluctuations. The reason for considering cost shocks is that, if firms are buffeted more by cost shocks than demand shocks, production should optimally be more variable than sales. The reasons for considering seasonal fluctuations are that seasonal fluctuations account for a major portion of the variance in production and sales, that seasonal fluctuations are precisely the kinds of fluctuations that producers should most easily smooth, and that seasonally adjusted data are likely to produce spurious rejections of the production smoothing model even when it is correct.
We integrate cost shocks and seasonal fluctuations into the analysis of the production smoothing model in three steps. First, we present a general production smoothing model of inventory investment that is consistent with both seasonal and non-seasonal fluctuations in production, sales, and inventories. The model allows for both observable and unobservable changes in marginal costs. Second, we estimate this model using both seasonally adjusted and seasonally unadjusted data plus seasonal dummies. The goal here is to determine whether the incorrect use of seasonally adjusted data has been responsible for the rejections of the production smoothing model reported in previous studies. The third part of our approach is to explicitly examine the seasonal movements in the data. We test whether the residual from an Euler equation is uncorrelated with the seasonal component of contemporaneous sales. Even if unobservable seasonal cost shocks make the seasonal variation in output greater than that in sales, the timing of the resulting seasonal movements in output should not necessarily match that of sales.
The results of our empirical work provide a strong negative report on the production smoothing model, even when it includes cost shocks and seasonal fluctuations. At both seasonal and non-seasonal frequencies, there appears to be little evidence that firms hold inventories in order to smooth production. A striking piece of evidence is that in most industries the seasonal in production closely matches the seasonal in shipments, even after accounting for the movements in interest rates, input prices, and the weather.
Jeffrey A. Miron, Stephen P. Zeldes

Chapter VI. Order Backlogs and Production Smoothing

Abstract
Empirical examination of some aggregate manufacturing data suggests that order backlogs may help explain two puzzling facts: (1) the variability of production appears to be greater than that of demand, and (2) inventories appear to be drawn down when demand is low, built up when demand is high.
Kenneth D. West

Backmatter

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