Difficulties with evaluating Negative Bias Temperature Instability (NBTI) are linked to fast effects occurring at microsecond or possibly faster time scales. The wide distribution of time scales involved in NBTI relaxation suggests participation of some sort of dispersion in the underlying NBTI mechanism. A universal behavior of the relaxation is observed and used to benchmark several models incorporating dispersion. The impact of the boundary condition on the model based on dispersive transport is also briefly discussed. Furthermore, on-chip circuits are designed and fabricated to measure the effect of AC NBTI up to 2 GHz on individual devices. The results on both single pFET's and inverters indicate that AC NBTI is independent of the frequency in the entire 1 Hz - 2 GHz range. This suggests that any characteristic time scale of the NBTI mechanism must be below 1 ns.
<strong>T</strong>his paper is aimed at developing a business-cycle model for a small open emerging economy (SOEE). The model is parameterized, calibrated, and simulated to rationalize two important stylized facts in a SOEE. The first one is that when the international interest rate increases, the growth rate of a soee is reduced. Secondly, when industrialized countries are in recession, a soee suffers an even larger reduction in their growth rate. The obtained results show that if exports respond negatively to the international interest rate or exports are reduced due to an international recession, the aggregated consumption of the domestic economy is substantially more volatile than an economy where exports do not react. Moreover, this paper provides a possible explanation to the puzzle that developing countries suffer a recession not only when industrialized countries are in recession, but also when they are growing too fast.
This chapter shows a way to, using simulation analysis, assess the performance of some of the most popular unit root and change in persistence tests. The authors do this by means of Monte Carlo simulations. The findings suggest that these tests show a lower than expected performance when dealing with some of the processes commonly believed to be found in the economic and financial data. The output signals that extreme care should be taken when trying to support a theory using real data. As the results show, a blind practitioner could get misleading implications almost surely. As an empirical exercise, the authors show that the considered test finds evidence of a unit root process in the US house price index. Nonetheless, as the simulation analysis shows, extreme caution should be taken when analyzing these results.
We apply text analysis to Twitter messages in Spanish to build a sentiment- based risk index for the financial sector in Mexico. We classify a sample of tweets for the period 2006-2019 to identify messages in response to positive or negative shocks to the Mexican financial sector. We use a voting classifier to aggregate three different classifiers: one based on word polarities from a pre-defined dictionary; one based on a support vector machine; and one based on neural networks. Next, we compare our Twitter sentiment index with existing indicators of financial stress. We find that this novel index captures the impact of sources of financial stress not explicitly encompassed in quantitative risk measures. Finally, we show that a shock in our Twitter sentiment index correlates positively with an increase in financial market risk, stock market volatility, sovereign risk, and foreign exchange rate volatility
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