### Table 4 Time-Varying Market Price of Currency Risk

"... In PAGE 25: ... To the extent that #20 and #20 #03 are time-varying, or that the correlation #1A zz #03 is time-varying, the sign of the currency risk premium may also be time-varying. Table4 allows the market price of currency risk to depend on the level of the exchange rate #28#20 t = #20 0 + #20 1 e t , model B#29, the interest rate di#0Berential #28#20 t = #20 0 + #20 2 #28r t , r t #03 #29, model C#29, or the volatility of the exchange rate #28#20 t = #20 0 + #20 3 v t , model D#29. Each line in the table presents only estimates of #20 0 , #20 1 , and #20 2 , along with the resulting log likelihood of the model.... In PAGE 25: ... However, when we let the market price of currency risk depend on both the level and the volatility of the exchange rate #28model E#29, only the dependence on the volatility remains signi#0Ccant. Plot A of Figure 5 shows a decomposition of the exchange rate drift with a time-varying market price of currency risk #28model D in Table4 #29. The solid line is the interest rate di#0Berential, the dashed line is the currency risk premium, and the dotted line is the interest rate risk premium.... In PAGE 26: ... Studies by Baillie and Bollerslev #281989,1990#29, Bekaert and Hodrick #281993#29, and Domowitz and Hakkio #281985#29, #0Cnd only weak support for the inclusion of the conditional exchange rate volatility in the exchange rate drift. The evidence presented in Table4 and in Figure 5 is much stronger for two reasons. We impose an economic model, which implies a speci#0Cc functional form for the drift, and we observe the instantaneous volatility of the exchange rate, rather than infer it with error from observed changes in the exchange rate.... In PAGE 28: ... 4.3 Implications for Currency Markets With time-varying market price of currency risk #28model D in Table4 #29 and time-varying correlation between innovations to the log exchange rate and innovations to its volatility #28model B in Table 6#29, our estimated model is: dr t =0:240 , 0:034 , r t #01 dt +0:047 p r t dW t ; dr t #03 =1:069 , 0:070 , r t #03 #01 dt +0:093 p r t #03 dW t #03 ; #2842#29 de t = h , r t , r t #03 #01 + #10 , 4:063 , 29:817v t #01 + , , 0:230 #01, , 0:194 #01 p r t #11 v t , 1 2 v t 2 i dt + v t dX t ; dv t =4:073 , 0:102 , v t #01 dt +0:305 p v t dY t ; where Corr 2 6 6 6 6 4 dW t dW t #03 dX t dY t 3 7 7 7 7 5 = 2 6 6 6 6 4 1:000 ,0:205 1:000 ,0:230 0:056 1:000 0:059 ,0:006 #1A xy 1:000 3 7 7 7 7 5 #2843#29 and #1A xy =2 exp #08 1:573 , 3:217e t #09 1 + exp #08 1:573 , 3:217e t #09 , 1: #2844#29 This model has some interesting implications for the currency spot and options markets. 4.... ..."

### Table 3: Fixed Weight versus Time-Varying Weight Strategies

2004

"... In PAGE 16: ...Evaluating Trading Strategies 4.1 One-Period Portfolio Choice Table3 highlights the importance of conditioning information in the context of static portfolio choice. There is a substantial increase in the maximal Sharpe Ratio that one can obtain by using the predictive variables.... In PAGE 16: ...4% increment in annual return. The standard errors in Table3 , and standard errors and confidence bands in sub- sequent tables and figures, are generated in a parametric bootstrap. Returns and pre- dictive variables are modeled as a VAR(1) where the residuals are re-sampled.... ..."

### Table 3. Econometric estimates from time-varying advertising parameter models. Variable Parameter Fluid Milk Cheese

"... In PAGE 13: ...9 and BCGW and GCGW are the brand and generic cheese advertising goodwill variables, respectively. Estimation and Testing Results Estimation results are displayed in Table3 . Before discussing those results, we need to evaluate the heteroskedastic nature of the residuals.... In PAGE 14: ... Estimation results reveal both models demonstrate reasonable explanatory power with adjusted R-square values at or above 0.94 ( Table3 ). Wald tests were constructed to test the structural heterogeneity of the advertising parameters.... In PAGE 15: ... The shorter lag-distribution for cheese relative to fluid milk is consistent with the empirical results in Kaiser that applied five-quarter lags to generic fluid milk advertising and three-quarter lags to generic cheese advertising using a polynomial distributed lag structure. Demand Elasticities Given the nonlinear specification of the time-varying parameter models, the regression results of Table3 are most usefully evaluated in terms of calculated elasticities. Table 4 provides selected elasticities for the time-varying models evaluated at the sample means.... ..."

### Table 1 Panel A: Estimates of Time-Varying Expected Returns 1871-1997

"... In PAGE 9: ...8 In the robustness tests we introduce another instrument, the price earnings ratio in excess of the short term interest rate. Table1 reports the estimates of the system (4), (5) and (6) using Quasi-maximum like- 7 For international evidence of stock return predictability see, for example, Campbell (1987), Harvey (1991), Ferson and Harvey (1993), Solnik (1993), Bekaert and Harvey (1995), Hardouvelis et al (1996) and DeSantis and Gerard (1997). For evidence regarding international bond return predictablity, see, for example, Evans (1994), Fama and French (1989), Keim and Stanbaugh (1986) and Ilmanen (1995).... ..."

### Table 6 Time-Varying Correlation between dX and dY

"... In PAGE 27: ...he theoretical literature on option pricing under stochastic volatility #5Be.g. Hull and White #281987#29 or Stein and Stein #281991#29#5D. Table6 allows this correlation to depend on the log exchange rate #28model B#29, the interest rate di#0Berential #28model C#29, or the volatility of the exchange rate #28model D#29. Each line in the table presents only estimates of #1A 0 , #1A 1 , and #1A 2 of the speci#0Ccation #2838#29, along with the resulting log likelihood of the model.... In PAGE 28: ... 4.3 Implications for Currency Markets With time-varying market price of currency risk #28model D in Table 4#29 and time-varying correlation between innovations to the log exchange rate and innovations to its volatility #28model B in Table6 #29, our estimated model is: dr t =0:240 , 0:034 , r t #01 dt +0:047 p r t dW t ; dr t #03 =1:069 , 0:070 , r t #03 #01 dt +0:093 p r t #03 dW t #03 ; #2842#29 de t = h , r t , r t #03 #01 + #10 , 4:063 , 29:817v t #01 + , , 0:230 #01, , 0:194 #01 p r t #11 v t , 1 2 v t 2 i dt + v t dX t ; dv t =4:073 , 0:102 , v t #01 dt +0:305 p v t dY t ; where Corr 2 6 6 6 6 4 dW t dW t #03 dX t dY t 3 7 7 7 7 5 = 2 6 6 6 6 4 1:000 ,0:205 1:000 ,0:230 0:056 1:000 0:059 ,0:006 #1A xy 1:000 3 7 7 7 7 5 #2843#29 and #1A xy =2 exp #08 1:573 , 3:217e t #09 1 + exp #08 1:573 , 3:217e t #09 , 1: #2844#29 This model has some interesting implications for the currency spot and options markets. 4.... ..."

### Table 7: Time-Varying EM. 14-node topology. Bad prior.

"... In PAGE 7: ... We retested the EM method using a window size of 10, to take advantage of multiple measurement intervals. Table7 shows the results. The constant case is not shown because it is not affected by incorporating multi- ple measurement intervals.... ..."

### Table 7: Time-Varying EM. 14-node topology. Bad prior.

"... In PAGE 7: ... We retested the EM method using a window size of 10, to take advantage of multiple measurement intervals. Table7 shows the results. The constant case is not shown because it is not affected by incorporating multi- ple measurement intervals.... ..."

### Table 2 Performance measures for static and time-varying (TV) scalar fields for direct vol- ume rendering (DVR) and isosurfacing (Iso). Static measurements were taken with- out multithreading. Performance is reported for each dataset with object-space sort- ing (during rotations) and without object-space sorting (otherwise). Mesh Sort DVR Static DVR TV DVR TV Iso Static Iso TV Iso TV

"... In PAGE 16: ... Rendering time statistics were produced using a fixed number of viewpoints. In Table2 we show timing results for our experimental datasets. To compare the overhead of our system with the original HAVS system that handles only static data, we also measure the rendering rates for static scalar fields without multi- threading.... ..."

### Table 3. Parameter and Variable Estimates Time Invariant Time Varying (1994)

in Abstract

2007

"... In PAGE 31: ... We then use the demand equation, Equation (8), to solve for A. Table3 summarizes our parameters. 4.... ..."

### Table 4 Single link: time varying arrival rate

"... In PAGE 7: ...ink. The initial arrival rate is 500 calls per minute. The arrival rate is then increased linearly over a 5 min window. The resulting blocking rates over this window are shown in Table4 . Recall that the prescribed blocking probability is 0.... ..."