### 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 Non-time varying independent variables to predict churn behavior

2005

"... In PAGE 14: ...14 3.3 Non-time Varying Independent Variables Besides the sequential dimension, several non-time varying covariates are created (see Table3 ). Two blocks of independent variables can be distinguished.... In PAGE 24: ...3 Defining the Best Subset of non-time varying Independent Variables Before we compare the predictive performance of the LogSeq model with that of the LogNonseq model, we first define a best subset of non-time varying independent variables to include in the logistic- regression models besides the sequential dimension relbalance. Employing the leap and bound algorithm [11] on the non-time varying independent variables in Table3 , we compared the best subsets having size 1 until 20 on their sums of squares. As expected the increase in the performance criterion is inversely proportional to the number of independent variables added.... ..."

### 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 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 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 2: Percentage of execution time for varying percentages of most relevant predicates

2003

"... In PAGE 31: ... Whether without evidence construction (Figure 4(a) or with evidence construction (Figure 4(b)), the graphs indi- cate that the timings of the programs with mode are consistently better than those without mode declaration. Additionally, we compare the running space performance between the programs with and with- out mode declaration in Table2 . For benchmarks without evidence construction, our experiments... In PAGE 32: ...39 305.19 Table2 : Running space comparison (Megabytes) indicate that with mode declaration, space requirement is 1.4 to 15.... In PAGE 54: ...564 1.577 Table2 : Normalized execution times. conj disj sh 65 gr 65 sh 130 gr 130 sh 5gr5sh10 gr 10 comp 11.... In PAGE 54: ...775 Table 3: Normalized compilation times. The results in Table2 , 3, and 4 are normalized with respect to the control flow compilation case, namely with respect to conj sh 65 for the conj benchmarks and with respect to disj sh 5forthe disj benchmarks. Table 2 shows the normalized execution time of a query when it is executed using compile amp; run (comp), meta-call (call), embedded meta-call (emc), and control flow compiled code (cfcomp).... In PAGE 54: ... The results in Table 2, 3, and 4 are normalized with respect to the control flow compilation case, namely with respect to conj sh 65 for the conj benchmarks and with respect to disj sh 5forthe disj benchmarks. Table2 shows the normalized execution time of a query when it is executed using compile amp; run (comp), meta-call (call), embedded meta-call (emc), and control flow compiled code (cfcomp). Embedding the meta-call results in a substantial improvement over normal meta-call, which is of course due to the massive instruction compression.... In PAGE 65: ...3. Table2 shows differences of about 3% in time both ways. That hardly seems meaningful, but the meta qsort and queens are very backtracking intensive.... In PAGE 66: ...6 12402 12111 +2.3 Table2 : Time (msecs) and space (machine words) performance: one stack against two stack On the whole, the one stack model is favorable to backtracking intensive programs. Note that the space figures in Table 2 include the setup for the benchmarks5.... In PAGE 66: ...3 Table 2: Time (msecs) and space (machine words) performance: one stack against two stack On the whole, the one stack model is favorable to backtracking intensive programs. Note that the space figures in Table2 include the setup for the benchmarks5. The one stack model has more chance to win space wise when the life times of choice points and environments overlap: this seems not true in more realistic programs like comp.... In PAGE 83: ... Further than that only marginal improvements would be achieved, or the code growth could even introduce some slow-downs due to caching problems. Notice that the results from Table2 show an increasing trend as the programs become larger. Considering the last 3 programs which have more than 40 predicates, the percentage of the execution time on 20% of the predicates is on average 83.... In PAGE 93: ... The degree of complexity of the low-level code is similar to the one proposed in the BAM [25]. Table2 summarizes the instructions. The Type argument which appears in several of them is intended to reflect the type of the instruction arguments: for example, in the instruction bind, Type used to specify if the arguments contain a a more complete discussion of this issue).... In PAGE 94: ... CallerImp and CalleeImp mark how caller and callee are compiled. Control ijump(X) Jump to the address stored in X jump(Label) Jump to Label cjump(Cond, Label) Jump to Label if Cond is true switch on type(X, Var, Str, List, Cons) Jump to the label that matches the type of X switch on functor(X, Table, Else) switch on cons(X, Table, Else) Conditions not(Cond) Negate the Cond condition test(Type, X) True if X matches Type equal(X, Y) True if X and Y are equal erroneous(X) True if X hasanerroneousvalue Table2 : Control and data instructions. variable (and, if this is known, whether it lives in the heap, in the stack, etc.... In PAGE 111: ... Table2 contains the timings for the benchmarks for each bb heapwb system. The maximal size of the remembered sets (number of entries) is also included.... In PAGE 111: ...3. wam heap bb heapwb 2Mb bb heapwb 4Mb bb heapwb 8Mb bb heapwb 16Mb ttot ttot mremset ttot mremset ttot mremset ttot mremset browsegc 5319 6404 1042695 6079 782547 5620 0 5668 0 boyergc 9074 9949 1115 9837 606 9769 316 9716 176 dnamatchgc 2414 2598 217 2588 120 2578 50 2571 11 takgc 1380 1465 0 1462 0 1454 0 1434 0 serialgc 7725 9114 22891761 9132 22891060 9031 22890701 9054 22395214 Table2 : Overhead of the write barrier and remembered sets browsegc boyergc dnamatchgc takgc serialgc 1.0 1.... ..."

### 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.... ..."

Cited by 1

### TABLE 2. Model Time-Varying Inputs

2007

"... In PAGE 33: ...TABLE2 . Model Time-Varying Inputs (cont.... ..."

### Table A2: Selected Results for Unemployment Regression Models with Time-Varying Institutions

2001

Cited by 6