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Studies in Nonlinear Dynamics & Econometrics

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Volume 17, Issue 3


Inventory investment and the business cycle: the usual suspect

Frédérique Bec
  • Corresponding author
  • THEMA, University Cergy-Pontoise and CREST, 15 bd Gabriel Peri, F-92245 Malakoff Cedex, France
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  • Other articles by this author:
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/ Melika Ben Salem
Published Online: 2013-04-02 | DOI: https://doi.org/10.1515/snde-2012-0041


From quarterly postwar US and French data, this paper provides evidence of a bounce-back effect in inventory investment but not in final sales data. Actually, from a bounce-back augmented threshold model, it appears that i) the null hypothesis of no bounce-back effect is strongly rejected by the inventory investment data and ii) the one-step ahead forecasting performances of the models accounting for this bounce-back effect are well improved compared to linear or standard threshold autoregressions. This supports the conventional wisdom that inventory investment exacerbates aggregate fluctuations, in line with the recent theoretical models by, e.g., Wang and Wen (Wang, P., and Y. Wen. 2009. “Inventory Accelerator in General Equilibrium.” Working Paper 010, Federal Reserve Bank of St. Louis) and Wang, Wen and Xu (Wang, P., Y. Wen, and Z. Xu. 2011. “When do Inventories Destabilize the Economy? An Analytical Approach to (s,s) Policies.” Working Paper 014, Federal Reserve Bank of St. Louis) which clearly predict a destabilizing role of inventory investment over the business cycle. By contrast, our empirical findings cast doubt on models based on the stockouts avoidance motive for holding inventories.

This article offers supplementary material which is provided at the end of the article.

Keywords: inventory investment; threshold models; bounce-back effects; asymmetric business cycles


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About the article

Corresponding author: Frédérique Bec, THEMA, University Cergy-Pontoise and CREST, 15 bd Gabriel Peri, F-92245 Malakoff Cedex, France

Published Online: 2013-04-02

Published in Print: 2013-05-01

See Blinder (1986) for a detailed presentation of the production smoothing model and how it may be amended to somehow reconcile its implications to the stylized facts.

See also Blinder (1981), Blinder and Maccini (1991), Kahn and Thomas (2007). In the model proposed by the latter, inventories arise as a result of non-convex delivery costs. To economize on such costs, firms hold stocks, making active adjustments only when these stocks are sufficiently far from a target. This behavior grounds the so-called (S, s) rule.

Eichenbaum (1989) has first developed this motive in a partial equilibrium setup.

See Kahn (1987), Bils and Kahn (2000), Kryvtsov and Midrigan (2009, 2010), Wen (2011).

The idea that firm-level (S, s) policy can spread throughout sectors and/or the whole economy was further explored by Cooper and Haltiwanger (1992) who consider an economy consisting in a retailer for final goods and two manufacturers who produce intermediate goods. They show that a high cost to hold inventories for the manufacturers will imply a production bunching in the manufacturers sector even though it has rising marginal costs: this stems from the bunching of orders by the retail sector as in the (S, s) model.

See Kim, Morley, and Piger (2005) or Morley and Piger (2012) for an extension of the Markov-Switching model which allows bounce-back effects.

See Bec, Bouabdallah, and Ferrara (2011, 2013) for a detailed description of these functions.

The series ID number is P54. Inventory investment is measured by the INSEE as the difference between the national sources and uses other than inventories, namely intermediate consumption, final consumption, gross fixed capital formation and exports.

According to Bec, Bouabdallah, and Ferrara (2011), four recessions occurred in France over the sample under study: 1974Q4–1975Q2, 1980Q2–1980Q4, 1992Q4–1993Q2, 2008Q2–2009Q3. For the US, we use the NBER recession dates.

Notice that the threshold parameter is estimated from a grid search leaving at least 5% of the observations in the lower regime. Hence, this constraint is not binding.

We have deliberately chosen not to present Diebold and Mariano (1995) type of tests for the statistical comparison of the predictive accuracy of the different models. First, there are two traditional arguments against their use: i) classical testing with implausible null implies a sizeable small-sample bias in favor of this null and ii) the original forecast comparison, based, e.g., on Mean squared Errors, is a strong model selection tool on its own grounds [see amongst others Wei (1992), Inoue and Kilian (2006) or Ing (2007) on this point]. Then, as shown in Costantini and Kunst (2011), the small sample bias toward the Diebold-Mariano like null and toward simplicity is especially true when the true DGP is a Threshold Auto-Regression process.

Citation Information: Studies in Nonlinear Dynamics and Econometrics, Volume 17, Issue 3, Pages 335–343, ISSN (Online) 1558-3708, ISSN (Print) 1081-1826, DOI: https://doi.org/10.1515/snde-2012-0041.

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