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not in the same manner. For Sweden we find a negative impact in accordance with the Holland hypothesis, whereas for Germany and the Netherlands we find the opposite in support of the Cukierman–Meltzer hypothesis. In a sensitivity analysis we show that an arbitrary choice of the heteroscedasticity parameter influences this relationship significantly. JEL classification: C22, E31. Keywords: GARCH-in-mean; inflation; level effect; nominal uncertainty; power transformation. 1. INTRODUCTION The issue of the welfare costs of inflation has been one of the most researched

Dişbudak, C., 2014. Modelling Inflation Uncertainty with Structural Breaks Case of Turkey (1994–2013). Mathematical Problems in Engineering . 31. Greenspan, A., 2004. Risk and uncertainty in monetary policy. American Economic Review, 33-40. 32. Grier, K.B. and Perry, M.J., 1998. On inflation and inflation uncertainty in the G7 countries. Journal of International Money and Finance , 17, 4, 671-689. 33. Grier, K.B. and Perry, M.J., 2000. The effects of real and nominal uncertainty on inflation and output growth: some garch-m evidence. Journal of Applied Econometrics

- sistence in inflation and its uncertainty using a dual long memory process. We also investigate the possible existence of heterogeneity in inflation dynamics across Euro area countries and examine the link between nominal uncertainty and macroeconomic performance measured by the inflation and output growth rates. Strong evidence is provided for the hypothesis that increased inflation raises nominal uncertainty in all countries. However, we find that uncertainty surrounding future inflation has a mixed impact on output growth. This result brings out an important asymmetry

0165-1765(02)00009-5 Fountas S. Karanasos M. Kim J. 2002 Inflation and Output Growth Uncertainty and Their Relationship with Inflation and Output Growth Economics Letters 75 293 301 Fountas, S., M. Karanasos, and J. Kim. 2006. “A Multivariate GARCH Approach of the Relationship Between Inflation, Output Growth, and Real and Nominal Uncertainty for the G7.” Oxford Bulletin of Economics and Statistics 68: 319–344. Fountas S. Karanasos M. Kim J. 2006 A Multivariate GARCH Approach of the Relationship Between Inflation, Output Growth, and Real and Nominal Uncertainty for

zincochromite] ranged from –1.3 to 1.5 GPa, without a deÞ nite trend, with an average of about 0.03 GPa and a standard deviation of 0.5 GPa. We decided to use a nominal uncertainty of 0.1 GPa on P to Þ t the experimental P-V curve, according to our previous experience with data collected at ID9A under similar conditions. This was motivated by the fact that the uncertainties on the elastic constants of ZnCr2O4 might skew the inferred K0 and Kʼ values, had we used a weighted scheme based on σ(P) = σ3 – σ1 in the EoS calculations. Note that σ(P) = 0.1 GPa is likely an

pressure-transmit- ting medium. The P-values have been measured by the ß uorescence line shift of ruby, excited by an Ar-laser and adopting the non-linear pressure scale of Mao et al. (1986). Equilibrium at a given pressure has been assumed when P-oscillations do not exceed 0.02–3 GPa, monitored every tenth minute. A nominal uncertainty of 0.1 GPa has been used based on our previous studies carried out with the same experimental set-up (Curetti et al. 2006). Non-hydrostaticity of P is a problem commonly affecting these kinds of experiments. Levy et al. (2003

and Pass-Through The Quarterly Journal of Economics 125 675 727 Grier, K. B., and M. J. Perry. 2000. “The Effects of Real and Nominal Uncertainty on Inflation and Output Growth: Some GARCH-M Evidence.” Journal of Applied Econometrics 15: 45–58. 10.1002/(SICI)1099-1255(200001/02)15:1<45::AID-JAE542>3.0.CO;2-K Grier K. B. Perry M. J. 2000 The Effects of Real and Nominal Uncertainty on Inflation and Output Growth: Some GARCH-M Evidence Journal of Applied Econometrics 15 45 58 Gust, C., S. Leduc, and R. Vigfusson. 2010. “Trade Integration, Competition, and the Decline

The combined data from FTIR (Table 2) and SIMS (Table 3) shows that the investigated samples contain a variety of defect types, with a large range of water contents that contribute in dif- ferent proportions to the total absorbance, enabling the different absorption coefficients k[Si], k[Mg], k[Ti], and k[triv] in Equation 2 to be determined by multiple linear regression. The initial regres- sion showed that k[Mg] was not resolvable given experimental uncertainties and was certainly <<0.1; accordingly, we assumed 0.03 (with a nominal uncertainty of ±0.03) from

Sveen 1974), and nickel (Meschter et al. 1981) with the temperature scaled to the magnetic ordering temperature (TC,N), and the entropy scaled to the value at the ordering temperature. FIGURE 11. The non-lattice heat capacity of fayalite (circles) compared with the computed contributions from the M1 and M2 sites (dashed) and their sum (bold solid line) considering magnetic and electronic contributions. The remainder (light solid line) is the critical contribution and is shown dashed when its value falls below nominal uncertainties in the phonon contribution [1

. Deposit items are free to all readers and found on the MSA web site, via the specific issue’s Table of Contents (go to http://www.minsocam.org/MSA/AmMin/TOC/2018/Dec2018_data/Dec2018_data.html) Note 5 and Table S6 for further details. Magmatic water contents were estimated using the plagioclase-melt hygrometers of Putirka (2005) and Waters and Lange (2015) , which have nominal uncertainties of 1.0 wt% and 0.35 wt% H 2 O, respectively. Both of these methods require as inputs magmatic temperature and pressure estimates as well as compositions of equilibrium