### Table 5 Stochastic Volatility Models

"... In PAGE 26: ...etween 5.7 and 14.4 percent in our sample. Our choice of a linear drift and a square-root di#0Busion function for the dynamics of the exchange rate volatility is admittedly ad-hoc. Therefore, Table5 tests this speci#0Ccation #28Model A#29 against a number of alternatives. Model B has a linear drift and a constant elasticity of variance #28CEV#29 di#0Busion function p #0C 2 v #0C 3 .... ..."

### Table 2. General dynamic models with behavioral relations

"... In PAGE 13: ... The early models were designed in the United States while the Europeans caught up in the 1980s and now seem to dominate the work with static behavioral models. Table2 lists general dynamic models with behavioral relations. General here means two things.... ..."

### Table 4 GMM Estimation of Stochastic Volatility Model Parameter DM/$ Rate Yen/$ Rate Nikkei 225

"... In PAGE 16: ... Even though the GMM omnibus test only rejects the model for the DM/$ exchange rate, the one-factor model is obviously an oversimpli cation of the true dynamic dependencies for all three markets. However, from an overall perspective, the estimation results in Table4 are generally in line with the simulation evidence reported in the previous section, and clearly suggest that the new estimation procedure could e ectively be employed in the empirical estimation of more complicated continuous time di usions. 5 Concluding Remarks Exploiting closed form analytic expressions for the conditional moments of integrated volatil- ity coupled with highly accurate empirical quadratic variation measures constructed from high-frequency data, we proposed a new class of GMM-type estimators for stochastic volatil- ity di usions.... ..."

### TABLE I DYNAMIC AND EQUILIBRIUM PROPERTIES

2005

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### Table 1: Parameter values and functions included in the stochastic dynamic programming model Parameter Value or formula Reference

### Table 1. Countries, Regions, and Sectors in the General Equilibrium Model Country or Region Sectors

"... In PAGE 8: ... The effects of human emissions on tropospheric ozone, and the influence of the various carbon aerosols, also are not considered in the analysis, as these substances are not included in the Kyoto framework. Non-energy activities are aggregated to three sectors, as shown in Table1 . The energy sector, which contributes to emissions of several of the non-CO2 gases as well as to CO2 itself, is modeled in more detail.... In PAGE 8: ... All electricity generation technologies produce perfectly substitutable electricity except for the Solar amp;Wind technology, which is modeled as producing an imperfect substitute, reflecting its intermittent output. The regional and sectoral disaggregation also is shown in Table1 . The disaggregation of Annex B into six nations or multi-nation groups is seen in the analysis below.... ..."

### Table 3. Dynamic models

"... In PAGE 4: ...0000) (1.0000) Table3 . The in nominal signi cant.... In PAGE 6: ... We then proceeded to the estimation of the dynamic model taking a general-to-speci c approach. The parsimonious model reported in Table3 has dynamic terms in nominal wages, in ation and productivity, and it passes several diagnostic tests. There is no evidence of structural 668 G.... In PAGE 6: ... 6. France, C S MSQ test an ECM and dynamic terms in prices and unemployment (see Table3 ). Serial correlation, normality and hetero- scedasticity tests are all passed, and so are predictive failure and Chow tests.... ..."

### Table 1: Performance comparison of the queuing with the Nagel-Schreckenberg model for Wup- pertal and NRW. It is a naturally arising question whether this algorithm for the route choice will converge. Due to the stochasticity of the underlying route choice model only a stochastic equilibrium, where the 4

2001

"... In PAGE 4: ... Due to its intrinsic properties the queuing model is the faster the longer the average link lengths in the network are. For example, applied to the network of Wuppertal (see Table1 ) with an average link length of 0.3 km it is faster by approximately one order of magnitude, but almost two orders of magnitude for the freeway network of the German Bundesland Nordrhein-Westfalen (NRW) with an average link length of 2.... ..."

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