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Does Capacity Utilization Predict Inflation? A Wavelet Based Evidence from United States

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Abstract

This paper aims to test a causal nexus between capacity utilization and inflation in the United States for the period from January 1969 to June 2017. Given the non-validity of the constant-parameter linear model (i.e., standard linear Granger causality) in attendance of nonlinearities and structural breaks, we use wavelets to provide a more general picture of the link between the U.S. capacity utilization and U.S. inflation in both time and frequency domains. The findings indicate a positive co-movement between the variables, mainly at high frequencies (shorter term). In addition, we do find evidence of a significant bi-causal relationship between capacity utilization rate and inflation per different frequency, whereas standard linear Granger causality detects a unidirectional link from inflation to capacity utilization. In general, our findings suggest a notable implication for policy makers that are in contradiction to the view of recent scholars regarding deterioration in the inflation–utilisation nexus.

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Source: Lodder (2009)

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Notes

  1. One might apply a nonparametric causality test with the ability to capture the nonlinearities and changes in regimes; however, the problem arises from a restriction to only the time domain. Hence, this approach cannot discern whether the causality between variables exists in the short-, medium- or long-run. Moreover, nonparametric tests are much more restricted than the wavelet, because the former is not time-varying, unlike phase differences in the latter.

  2. A traditional method that examines periodicities in the frequency domain and implicitly assumes that the underlying processes are stationary in time; for details, see Aguiar-Conraria et al. (2008).

  3. The self-similarity property implies a long-term dependence between the series (i.e., They have a similar shape, like cycle).

  4. Here, \(L^2({\mathbb {R}})\) shows a space of finite energy functions. For more details, see Aguiar-Conraria et al. (2008, p. 2868).

  5. If \(|s|<1\), then the mother wavelet \(\psi (t)\) is compressed, whereas \(|s|>1\) exposes that the mother wavelet \(\psi (t)\) is stretched across frequencies.

  6. As is clear from the equation, \(C_{\psi }\) is a constant for each given mother wavelet and is, therefore, called the “admissibility constant”.

  7. Because the Fourier frequency is defined by \(f(s) = \frac{\omega _0}{2\pi s} \), we can obtain the best conversion from the wavelet scale to the Fourier frequency f.

  8. Some studies such as Finn (1996) have shown that fuel prices have a negative impact on capacity utilisation, whereas the core CPI excludes the more volatile categories of food and energy prices.

  9. Available at https://fred.stlouisfed.org.

  10. A log transformation has been made for the capacity utilisation rate to correct the potential heteroscedasticity and dimensional differences between the series.

  11. According to the reasoning of Cavaliere (2005) and Cavaliere and Xu (2014), conventional unit root–critical values are not appropriate for bounded series. Moreover, they argue that conventional unit root critical values are inappropriate for series influenced by a control policy. Capacity utilisation is influenced by both of these rationales to some extent, such that it is bounded by construction between 0 and 100 and indirectly targeted by policy makers as an analogue of directly targeted labour unemployment, which consequently binds capacity utilisation even more than the simple 0 and 100 values. Hence, following Ahmed and Cassou (2017), we conduct the Cavaliere and Xu (2014) simulation-based ADF unit root test for the log of capacity utilisation rate. Under the constant term scenario, our simulated critical value at 5% level is \(-\,3.125\), where adopting this new adjusted critical value results in non-rejection of the null of a unit root for capacity utilisation.

  12. The standard Granger test is based on asymptotic distribution theory; however, in the presence of the nonstationary variables of the VAR model, results do not follow the formal asymptotic chi-squared distribution under the null hypothesis (Granger and Newbold 1974). Transforming the variables by differencing can be a remedy for this problem, but this will lead to loss of long-run information. For details, see Hacker and Hatemi-J (2006).

  13. For brevity, we do not report the LM test for serial independence. The results are available upon request from the authors.

  14. Our results agree with the recent calls made by economists in the popular press, which proposed the desirability of inflation inducement by the Federal Reserve Bank.

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  1. Department of Economics, Queen’s University, Kingston, ON, K7L 3N6, Canada

    Pejman Bahramian

  2. Department of Economics, Near East University, Via Mersin 10, Nicosia, Northern Cyprus, Turkey

    Andisheh Saliminezhad

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Bahramian, P., Saliminezhad, A. Does Capacity Utilization Predict Inflation? A Wavelet Based Evidence from United States. Comput Econ 58, 1103–1125 (2021). https://doi.org/10.1007/s10614-020-09990-4

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