### Table 1. Discrete-time simulation results for quadratic friction case.

"... In PAGE 7: ...0. The results in Table1 show that although the pure discrete wheel model simulation executes at a faster rate, its accuracy leaves much to be desired. Figure 7.... ..."

### Table 1. Discrete-time simulation results for quadratic friction case.

"... In PAGE 7: ...0. The results in Table1 show that although the pure discrete wheel model simulation executes at a faster rate, its accuracy leaves much to be desired. Figure 7.... ..."

### 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: 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 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 2: Parameters of the Discrete-Time Model

2003

### 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 7. Estimates of Unobserved Components Models: Discrete Time and Continuous Time

"... In PAGE 28: ... The results are contained in Tables 7 and 8. Table7 contains the results of estimating the discrete time and continuous time trend- plus-cycle models. The discrete time estimates are taken directly from Harvey (1989, p.... In PAGE 30: ...2843 Figures in parentheses are standard errors. misspeci ed in some way, and Harvey (1989) does indeed nd that the discrete time model in Table7 is inferior to a cyclical trend model in which t also depends on t 1 and yt = t+ t. Further investigations with continuous time cyclical trend models may be fruitful, but are beyond the scope of this simple illustration.... ..."

### Table 2: Free and xed elements in the discrete time parameter matrices of the EDM

"... In PAGE 16: ... Model speci cation While different models were speci ed, all had the same measurement equation part, which will be addressed rst: yti = Ctixti + dti + vti with cov(vti) = Rti : (20) The parameter matrices for successive observation time points t0; t1; t2; t3 are shown in Table 2. By xing the factor loading of the One-Minute-Test Form A at value 1 and its measurement origin at value 0 (in the rst row of Ct0 and dt0 in Table2 ) we equalled the variance and mean of the latent Decoding Skill (DS) variable at the initial time point t0 to the true variance (total variance minus measurement error variance) and mean of the One-Minute-Test Form A at that time point. In the same manner, the true variance and mean of Cito Reading Comprehension Test 2 de ned the variance and mean of the latent Reading Comprehension (RC) variable at t0.... In PAGE 18: ....H.L. Oud The parameter matrices of the discrete time state equation xt = A xt t + + b + wt t with cov(wt t) = Q (21) are also shown in Table2 . As they contain 21 unknown parameters, the total number of pa- rameters to be estimated is 38.... In PAGE 18: ... The stochastic differential equation dx(t) dt = Ax(t) + + b + GdW(t) dt ; (22) describes the development of the latent variables in continuous time, containing in particular con- tinuously contributing traits and constants b. The EDM relates the continuous time parameter matrices in Equation (22) as follows to the discrete time parameter matrices in Table2 (Oud amp; Jansen, 2000): A = eA t ; b = A 1[A I] b ; Q = irow[(A I + I A) 1(A A I I) row(GG0)] ; = A 1[A I] [A0 I]A0 1 ; ;xt0 = A 1[A I] ;xt0 : (23) Here, is the Kronecker product, row is the rowvec operation, putting the elements of a matrix rowwise in a column vector, irow the inverse operation. Because the time intervals between the measurements were approximately half a year, we started by xing t for the intervals t1 t0; t2 t1; t3 t2 at = 0:50.... ..."