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This paper investigates the movement of manufacturing inventories and production over the business cycle to arrive at an asymmetric structural equilibrium-correction model. Initially crosscorrelation coefficients and deepness and steepness tests for skewness are utilised to present desriptive statistics of the time series. Cointegration analysis is performed on each disaggregate inventory by stage of production (finished goods, work in progress and raw materials) with a set of explantory variables. Restricted cointegration vectors are added to the symmetric and non-symmetric specifications of the structural equilibrium-correction model. These are compared by parameter constancy tests and one-step ahead forecasts.

this article by briefly presenting what was considered, at least through the early 1980s, the standard model of inventory behavior---the production smoothing model. Next, I discuss some of this model's empirical predictions and review some facts about inventories that are at odds with the simplest version of the model. I then provide an overview of how economists have responded to the discrepancy between theory and data and examine how the interaction between theory development and data has continued to evolve.

The bullwhip effect, or demand information distortion, has been a subject of both theoretical and empirical studies in the operations management literature. Empirical studies have shown large magnitudes of the bullwhip effect at the individual product level, but the effect does not always exist at the macro level. The majority of studies focusing on the macro level have used monthly data due to its availability. In practice, however, companies often order more frequently than monthly, such as at daily or weekly intervals. In this paper, we examine how data aggregation can affect the observation of bullwhip effect. Specifically, we show how aggregating data over relatively long time periods can mask the magnitude of the bullwhip effect. In addition, we show that similar impacts occur when data is aggregated across products, and how the existence of correlated demand, seasonality, batch order, and finite capacity all can affect the measurement of the bullwhip effect. Finally, we discuss the cost implications associated with the measured magnitude of the bullwhip effect.

remaining errors and omissions are our own responsibility. The opinions expressed in this paper do not necessarily re ect views of the Federal Reserve Bank of New York, Board of Governors, nor the We examine inventory adjustment in the U.S. manufacturing sector using quarterly rmlevel data over the period 1978{97. Our evidence indicates that the inventory investment process is nonlinear and asymmetric, results consistent with a nonconvex adjustment cost structure. The inventory adjustment process di ers over the business cycle: for a given level of excess inventories, rms disinvest more in recessions than they do in expansions. The inventory adjustment process has changed little between the 1980s and 1990s, suggesting that recent advances in inventory control have had little e ect on adjustment costs. Nevertheless, the optimal inventory-sales ratio in the durable goods sector has declined signi cantly during our sample period. JEL Classi cation: D24, E22, E37

The views expressed in this paper are those of the author. No responsibility for them should be attributed to the Bank of Canada. iii

investment dynamics appear to dominate the economy’s historical movement around its long-run path. Blinder and Maccini (1991) show that the average movement in inventory investment during post-war recessions account for 87 percent of the peak-to-trough movement in Gross National Product (GNP). 1 Because inventory fluctuations have historically played such a major role in business cycles (and possibly seasonal fluctuations), it is important to understand the theoretical motivation for inventory holdings and the implied dynamics. The received view, established by Holt, Modigliani, Muth and Simon (1960), is that

I introduce inside money and serially correlated supply shocks to the Uncertain and Sequential Trading (UST) monetary model and test its implications using a vector auto regression impulse response analysis on post-war US data. I find that (a) The importance of money in predicting output is substantially reduced once the stock of inventories is added to the VAR system and (b) Shocks to inventories have a negative persistent effect on output and prices. These findings are broadly consistent with the predictions of the UST model but other findings about the timing of the maximal effects are not.