### Table 1: Empirical size and power for T = 30 and N = 20 LL IPS UB LL IPS UB

2000

"... In PAGE 17: ...05. TABLE 1 ABOUT HERE Table1 presents the rejection frequencies for the di erent tests. For p gt; 0 the LL test turns out to be quite conservative.... ..."

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### Table 1: Bayesian Bioassay Design by Curve Fitting of Monte Carlo Experiments

1999

"... In PAGE 7: ... To avoid under tting or over tting, we use both graphical methods for model checking and ANOVA as suggested by Cleveland, Grosse, and Shyu (1991) to experiment with speci cation of the surface (linear, degree=1; or quadratic, degree=2) and the neighborhood size (span). Table1 lists the optimal design obtained by the Monte Carlo method and the choice for the loess tting for the = 1 case. For I = 1, the Monte Carlo results are obtained by a xed grid with 500 equally spaced grid points, where the corresponding utility (g) for each grid point is obtained by averaging the preposterior risks with 5 repeated samples.... In PAGE 7: ...5 independent of the sample size. Table1 shows that the Monte Carlo results for the case = 1 ( .496, for n = 1, and... ..."

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### Table 4 Monte Carlo Evidence

2005

"... In PAGE 14: ... The results of the six experiments are presented in Table 4. Table 4 Panel A of Table4 gives the actual quarterly IMRF where the underlying model is difference stationary and three FRFs derived from three alternative estimation strategies. For all three estimation strategies the resulting FRFs give large values of persistence that grow with the time horizon.... In PAGE 15: ... What do we make of these results? First, we may ask what these results imply about the estimation strategy employed by forecasters. From Table4 we see that the strategy of estimating low order ARMA models, omitting a time trend and leaving the largest root unconstrained yield very large and increasing FRFs. Because these estimates are much higher than any observed from the actual forecast revisions in Table 3, we may conclude that forecasters have not used this strategy.... In PAGE 15: ... Because these estimates are much higher than any observed from the actual forecast revisions in Table 3, we may conclude that forecasters have not used this strategy. Table4 indicates that the strategy of imposing a time trend yields moderate estimates of persistence when the underlying process is a unit root but very low estimates when the true process is trend stationary. The latter case is inconsistent with observed FRFs from Table 3.... In PAGE 15: ... Finally, the strategy of imposing a unit root on low order AR models yields FRFs somewhat higher than those actually observed in Table 3. More generally, the results in Table4 suggest that annual FRFs will tend to be substantially larger than underlying quarterly IMRFs. In that sense, annual FRFs provide very poor and upwardly biased estimates of the underlying quarterly IMRF.... In PAGE 15: ... If our interest is obtaining the underlying IMRF from quarterly data, we may conclude that the large estimates of persistence found in the previous section are merely artifacts of the estimation process and have little bearing on the question of whether shocks to output are persistent. An alternative interpretation of the results in Table4 is warranted. The FRFs represent how forecasters actually revise their forecasts in light of new information and ... In PAGE 16: ... In practice, forecasters, and presumably economic agents, do not have precise knowledge of the underlying model and will use new information to update their model specification. In that sense, the results of Table4 can be interpreted as implying that IMRFs provide a poor estimate of how shocks affect our forecasts of future levels of output and hence are poor measures of the persistence of shocks. ... ..."

### Table I. Monte Carlo Cu model parameters.

### Table 4: Prices computed by alternative methods under the 3-factor SV model

2000

"... In PAGE 15: ... For both methods however, increasing the number of strikes does not result in dramatic increases in the com- putational times. Table4 shows the spread option prices for di erent strikes under the three factor SV model. The Monte Carlo prices with a discretisation of 2000 time steps oscillate around those computed by the FFT method.... ..."

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### Table 1: Prices computed by alternative methods under the 2-factor GBM model

2000

"... In PAGE 13: ... 4.2 Computational Results Table1 documents the spread option prices across a range of strikes under the two factor Geo- metric Brownian motion model [22], computed by three di erent techniques: one-dimensional integration (analytic), the fast Fourier Transform and the Monte Carlo method. The values for the FFT methods shown are the \lower quot; prices, computed over , regions that approach the the true exercise region from below and are therefore all less than the analytic price in the rst column.... ..."

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### Table 2 Results of the Monte Carlo study

"... In PAGE 5: ... It identifies the true model 83.2% of the times ( Table2 ). The main problem associated with AIC is that it tends to overfit the data (20.... ..."

### Table 3: Monte Carlo results (based on 100 trials) for the logistic/Weibull mixture model: joint modeling with various changes in the conditions.

"... In PAGE 13: ... The coverage rates of the 95% posterior intervals are at least 92% in all cases, suggesting approximate validity of inferences in all of the situations. Comparison with Table 2 shows that the posterior standard deviations in Table3 are larger to varying degrees. This is to be expected under the changes that we consider (more missing data, smaller sample size, heavier censoring), since they imply losses of information compared to the situation considered in Section 4.... ..."

### Table 1 : A preliminary Monte Carlo study to investigate the power of

### Table 3: Monte Carlo estimates for the non-overlapping framework, 1973:04- 1994:12a

1998

"... In PAGE 8: ...e signi cant, i.e. there is cointegration based on our structural exchange rate models. We cannot con rm that when we use tabulated critical values for inference in speci cation (8), as we are in most cases unable to reject ~ k = 0 and therefore k = 0.4 - Table3 here - The Monte Carlo critical values are, as mentioned in subsection 3.1, an approximation of the true distribution of t~ k, as this distribution is positioned somewhere between a standard-normal and a Dickey-Fuller distribution.... ..."

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