### Table A6: Fixed Effects Specifications with Time-Varying Covariates

### 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 1: Time-varying parameters of the model in simula-

"... In PAGE 2: ... 1955, Fant 1960, Kent 1972, Kent et al. 1972n29 suggests that the signin0ccant model pa- rameters are those listed in Table1 , and that their temporal variation may take the form of a sigmoidal function.... ..."

### Table 1. Summary of different simulation or animation scenarios and the CPU time taken to complete the calculation

2005

"... In PAGE 4: ... The morphs generated by MovieMaker and the Yale Molecular Motions server appear to be essentially identical for these hinge motion movements. Table1 lists the approximate CPU time (2.0 GHz processor with 512 MB RAM) taken for each of the seven types of motion supported by MovieMaker.... ..."

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### Table 3. FFT Co-Processor Computation Time at Varying Clock Rates

2004

"... In PAGE 4: ...$4.95 * (13.6 / 9.06)). Table3 shows the average throughput achieved when running the EMIF and co-processor and different clock rates with pipelining. Table 3.... In PAGE 4: ... Table3 indicates that running the EMIF at higher clock rates (133 MHz rather than 100 MHz) or at a higher bandwidth (64-bit synchronous rather than 32-bit synchronous) could potentially increase the performance of the FPGA co-processor assuming that the performance bottleneck is caused by the latency in data transfer. The use of FIFO buffers in the transmit-and-receive paths enables the FFT (or any other co-processing function) to run at higher clock speeds from the EMIF.... ..."

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### Table 3: Cycle time varying the number of sections

"... In PAGE 5: ... The cycle time has been computed for two trains run- ning on circuits of 5; 6;8;10; 11;12 sections and in a circuit of 11 sections the cycle time of 1; 2;3;4;5 trains. The results of varying the number of sections are in Table3 graphically represented in Fig. 5, while the cycle time of varying the number of trains is re- ported in Table 4 and Fig.... ..."

### 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 A1: Mean Values for Time-Varying Institutions Non-US US

2001

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