The effects of real and nominal uncertainty on inflation and output growth: some garch-m evidence

Grier, Kevin; Perry, Mark

doi:10.1002/(sici)1099-1255(200001/02)15:1<45::aid-jae542>3.0.co;2-k

Cited by 255 publications

(255 citation statements)

References 20 publications

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“…They find that for low inflation countries such as Canada, Germany, Italy, Japan and USA, there appear to be no relationship between the conditional mean and variance of inflation whereas for high inflation countries such as Argentina, Israel, Brazil, and UK, there is strong evidence of joint feedback between the two series. Grier and Perry (2000) use GARCH-M methods to test the effects of real and nominal uncertainty on average inflation and output growth in the United States from 1948:07 to 1996:12. They find evidence in support of the Friedman hypothesis but find no evidence in support of the Cukierman-Meltzer hypothesis.…”

Section: Literature Reviewsupporting

confidence: 89%

Causality between inflation and inflation uncertainty in South Africa: Evidence from a Markov-switching vector autoregressive model

Nasr

Balcılar

Ajmi

et al. 2015

Emerging Markets Review

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This study investigates the asymmetric and time-varying causality between inflation and inflation uncertainty in South Africa within a conditional Gaussian Markov switching vector autoregressive (MS-VAR) model framework. The MS-VAR model is capable of determining both the sign and direction of causality. We account for the nonlinear, long memory and seasonal features of inflation series simultaneously by measuring inflation uncertainty as the conditional variance of inflation generated by recursive estimation of a Seasonal Fractionally Integrated Smooth Transition Autoregressive Asymmetric Power GARCH (SEA-FISTAR-APGARCH) model using monthly data for the period 1921:01 to 2012:12. The recursive, rather than a full-sample, estimation allows us to obtain a time-varying measure of uncertainty and better mimics the real-time scenario faced by economic agents and/or policy makers. The inferred probabilities from the four-state MS-VAR model show evidence of a time-varying relationship. The conditional (i.e. lead-lag) and regime-prediction Granger causality provide evidence in favour of Friedman's hypothesis. This implies that past information on inflation can help improve the one-step-ahead prediction of inflation uncertainty but not vice versa. Our results have some important policy implications.

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Section: Literature Reviewsupporting

confidence: 89%

Causality between inflation and inflation uncertainty in South Africa: Evidence from a Markov-switching vector autoregressive model

Nasr

Balcılar

Ajmi

et al. 2015

Emerging Markets Review

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“…Macri and Sinha (2000) use quarterly real GDP and IP data, although the real GDP data do not exhibit an ARCH effect. Caporale and McKiernan (1996), Speight (1999), and Grier and Perry (2000) use monthly IP to examine the effect of output growth volatility on its growth. The data frequency may provide another avenue for differences in findings.…”

Section: Relationship Between Output Volatility and Economic Growthmentioning

confidence: 82%

Modeling the volatility of real GDP growth: The case of Japan revisited

Fang

Miller

2009

Japan and the World Economy

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Previous studies (e.g., Hamori, 2000;Ho and Tsui, 2003;Fountas et al., 2004) find high volatility persistence of economic growth rates using generalized autoregressive conditional heteroskedasticity (GARCH) specifications. This paper reexamines the Japanese case, using the same approach and showing that this finding of high volatility persistence reflects the Great Moderation, which features a sharp decline in the variance as well as two falls in the mean of the growth rates identified by Perrons (1998, 2003) multiple structural change test. Our empirical results provide new evidence. First, excess kurtosis drops substantially or disappears in the GARCH or exponential GARCH model that corrects for an additive outlier. Second, using the outlier-corrected data, the integrated GARCH effect or high volatility persistence remains in the specification once we introduce intercept-shift dummies into the mean equation. Third, the time-varying variance falls sharply, only when we incorporate the break in the variance equation. Fourth, the ARCH in mean model finds no effects of our more correct measure of output volatility on output growth or of output growth on its volatility. Journal of Economic Literature Classification: C32, E32, O40

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“…For example, Baillie et al (1996) and employ univariate GARCH models that allow for simultaneous feedback between the conditional mean and variance of inflation and growth respectively. Other recent studies use bivariate GARCH-in-mean models -either the CCC (Grier and Perry, 2000) or the BEKK specification (Grier et In the first step, the estimated models are used to generate conditional variances of inflation and output growth as proxies of nominal and real uncertainty and, in the second step, Granger-causality tests are performed. Table 10 presents a summary of the aforementioned studies and their findings.…”

Section: Comparison With Other Workmentioning

confidence: 99%

“…We look forward to sorting this out in future work. Grier and Perry (2000) in their bivariate formulation include the 6-month commercial paper-3-month treasury bill spread as a predictor of growth. In addition, it would be instructive to examine the relation between inflation, growth and their variabilities in a trivariate framework that accommodates the interactions of interest in an internally consistent fashion (see, for example, Elder, 2004).…”

Section: Multivariate Extensionsmentioning

confidence: 99%

Modeling the Link between US Inflation and Output: The Importance of the Uncertainty Channel

Conrad

Karanasos

2010

SSRN Journal

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This paper employs an augmented version of the UECCC GARCH specification proposed in Conrad and Karanasos (2010) which allows for lagged in-mean effects, level effects as well as asymmetries in the conditional variances. In this unified framework we examine the twelve potential intertemporal relationships between inflation, growth and their respective uncertainties using US data. We find that high inflation is detrimental to output growth both directly and indirectly via the nominal uncertainty. Output growth boosts inflation but mainly indirectly through a reduction in real uncertainty. Our findings highlight that macroeconomic performance affects nominal and real uncertainty in many ways and that the bidirectional relation between inflation and growth works to a large extend indirectly via the uncertainty channel.Keywords: Bivariate GARCH process, volatility feedback, inflation uncertainty, output variability.JEL Classification: C32, C51, E31. * We would like to thank James Davidson, Marika Karanassou and Ruey Tsay for their valuable suggestions. We have also benefited from the comments received from the participants of the Annual Meeting of the Verein für Socialpolitik (Kiel, 2010), the Annual Meeting of the Society for Nonlinear Dynamics and Econometrics (Novara, 2010)

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The effects of real and nominal uncertainty on inflation and output growth: some garch-m evidence

Cited by 255 publications

References 20 publications

Causality between inflation and inflation uncertainty in South Africa: Evidence from a Markov-switching vector autoregressive model

Causality between inflation and inflation uncertainty in South Africa: Evidence from a Markov-switching vector autoregressive model

Modeling the volatility of real GDP growth: The case of Japan revisited

Modeling the Link between US Inflation and Output: The Importance of the Uncertainty Channel

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