### Table 1: Effect of Exchange Rate Movements on FDI Flows: Panel data Estimates

### Table 1. Empirical Results

"... In PAGE 11: ...INSERT FIGURE 5 (A) TO (F) Empirical Results We present the results of the estimation in Table1 , together with the results of a linear regression of the selected macroeconomic variables on the devaluation probability. The linear regression might also be seen as the estimation of the model presented earlier with Eq.... In PAGE 11: ... The expected signs are, therefore, opposite to those assigned in the case of Jeanne apos;s model. Table1 shows that, for the non-linear case, the level of international reserves is the only variable that is significant and has the expected sign. This points to the importance of this variable in the determination of the fundamental of the Brazilian economy.... In PAGE 12: ...he evolution of the estimated fundamental can be seen in Fig.7. Fig.9 shows the separate contribution of each macroeconomic variable in the composition of the fundamental. One can see clearly the importance of the level of international reserves in this composition, in accordance with the empirical results presented in Table1 . Observing Fig.... ..."

### Table 1. Empirical Results

2006

"... In PAGE 13: ....12.7 (x86 64-linux-glibc2.3) and ProB version 1.2.4.9 The rst experiment consisted in running the previously discussed factorial function. The results are presented in the upper half of Table1 . Note that ProB does check the function arguments (to see if they are a natural number) at every function application.... ..."

Cited by 2

### Table 1. Empirical comparison of bb+FC and enumerating global revisions (egr) searching exhaustively. Each row presents results of 10 runs on random problems for each algorithm.

1998

"... In PAGE 9: ...). Exhaustive search: Table1 presents empirical results on two variants of egr in comparison with the branch amp;bound algorithm bb+FC. Algorithm bb+FC uses forward checking equivalently to the P-FC3 procedure in [2] combined with a dynamic variable ordering heuristic, which is especially appropriate to constraint hierarchies [6, 7].... ..."

Cited by 2

### Table 2: Empirical Results for Three Independent Tasks

"... In PAGE 34: ....4.1 Independent Tasks Task T(ms) C(ms) D(ms) A 100 20 100 B 150 30 150 C 350 125 350 Table 1: Temporal Characteristics of Three Independent Tasks To obtain more meaningful results for the first task set we will gradually increase task temporal characteristics, such as period, deadline and execution time by a scalar value. The empirical results presented in Table2 were obtained from using real-time scheduling theory (RTST) and symbolic model checking (SMC) methods on the task set from Table 1.... ..."

### Table 1: Sample Database and Sample Temporal Aggregations In this paper, we present several new parallel algorithms for the computation of temporal aggre- gates on shared-nothing architectures #5B12#5D. Speci#0Ccally,we start with the #28sequential#29 Aggregation Tree algorithm #5B9#5D and propose several approaches to parallelize it. The performance of the parallel algorithms relativetovarious data set and operational characteristics is our main interest. This paper is organized as follows. Section 2 gives a review of related work and presents the sequential algorithm on whichwe base our parallel algorithms. Our proposed algorithms on computing parallel temporal aggregates are then described in Section 3. Section 4 presents empirical results obtained from the experiments performed on a shared-nothing Pentium cluster. Finally, Section 5 concludes the paper and summarizes future work. 1

"... In PAGE 3: ... Unfortunately, aggregate computation is traditionally expensive, especially in a temporal database where the problem is complicated byhaving to compute the intervals of time for which the aggre- gate value holds. Consider the sample table in Table1 #28a#29, listing the salaries of employees and when these salaries are valid, indicated by closed-open intervals. Finding the #28time-varying#29 number of employees #28Table 1#28b#29#29 involves computing the temporal extent of eachvalue, which requires deter- mining the tuples that overlap each temporal instant.... In PAGE 3: ... Consider the sample table in Table 1#28a#29, listing the salaries of employees and when these salaries are valid, indicated by closed-open intervals. Finding the #28time-varying#29 number of employees #28 Table1 #28b#29#29 involves computing the temporal extent of eachvalue, which requires deter- mining the tuples that overlap each temporal instant. Similarly, #0Cnding the time-varying maximum salary #28Table 1#28c#29#29 involves computing the temporal extent of each resulting value.... In PAGE 3: ... Finding the #28time-varying#29 number of employees #28Table 1#28b#29#29 involves computing the temporal extent of eachvalue, which requires deter- mining the tuples that overlap each temporal instant. Similarly, #0Cnding the time-varying maximum salary #28 Table1 #28c#29#29 involves computing the temporal extent of each resulting value.... In PAGE 4: ... When an aggregation tree is initialized, it begins with a single node containing #3C 0; 1; 0 #3E #28see the initial tree in Figure 1#29. In the example from the previous section, four tuples from the argument relation #28 Table1 #28a#29#29 are inserted into an empty aggregation tree. The start time value, 18, of the #0Crst entry to be inserted splits the initial tree, resulting in the updated aggregation tree shown in Figure 1.... In PAGE 4: ... Because the original node and the new node share the same end date of 1, a count of 1 is assigned to the new leaf node #3C 18; 1; 1 #3E. The aggregation tree after inserting the rest of the records in Table1 #28a#29 is shown at the bottom of Figure 1. To compute the number of tuples for the period #5B8; 12#29 in this example, we simply take the count from the leaf node #5B8; 12#29 #28which is 1#29, and add its parents apos; countvalues.... In PAGE 4: ... Starting from the root, the sum of the parents apos; counts is 0 + 0 + 1 = 1 and adding the leaf count, gives a total of 2. The six leaf nodes of the aggregation tree correspond to the six tuples in the result of the aggregate #28see Table1 #28b#29#29. Though SEQ correctly computes temporal aggregates, it is still a sequential algorithm, bounded by the resources of a single processor machine.... In PAGE 22: ...4.3 Scale-Up Performance: Time Partitioning The experimental parameters for this are shown in Table1 0, with the results given in Figure 17. We can observe analogous results to the same experiment on data set partitioned by SSN #28cf.... In PAGE 23: ...Actual Values Partitioning Time Number of Processors #28p#29 2, 4, 8, 16, 32 Tuple Size in bytes 93 Tuples per Processor 2,620 Total Number of Tuples p #02 2; 620 Reduction 80.76 percent Table1 0: Experimental Parameters #28Scale-Up, Time Partitioning#29 0 0.5 1 1.... In PAGE 24: ...7.29#2F95.32#2F92.08#2F87.24#2F80.76 percent Table1 1: Experimental Parameters #28Speed-Up, Time Partitioning#29 0 0.5 1 1.... In PAGE 30: ...Node Distributed Centralized Reduction Count Results Results HI Small GTDM GTDM+C Large GTDM PM LOW Small GTDM PM Large GTDM GTDM+C Table1 2: Matrix of Recommendations 4.5.... ..."

### Table 1. Empirical Datasets

"... In PAGE 1: ... 2. Experimental Datasets The 35 datasets utilized in our empirical study are listed in Table1 . The percentage of minority examples varies from 1.... In PAGE 6: ... Relative to G, RUS is clearly the best sampling technique. As would be expected, not using sampling typically results in the highest overall accuracy ( Table1 6), however since we are interested in detecting examples of the positive class, this measure is very misleading. We believe overall accuracy is not an appropriate measure, especially given imbalanced data, however it is presented because it is often used in related work.... ..."

### Table V Terms of the loans for the sample of unrelated and related loans This table presents raw results for the random sample of unrelated and related loans. The table presents, for each empirical proxy, the number of usable observations, the mean, and the median values for unrelated and related loans. We report t-statistics and z-statistics (Wilcoxon rank sum) as the test for significance for the change in mean and median values, respectively. Definitions for each variable can be found in Appendix A.

2003

Cited by 1

### Table V Terms of the loans for the sample of related and unrelated loans This table presents raw results for the random sample of related and unrelated loans. The table presents, for each empirical proxy, the number of usable observations, the mean, and the median values for unrelated and related loans. We report t-statistics and z-statistics (Wilcoxon rank sum) as our test for significance for the change in mean and median values, respectively. Definitions for each variable can be found in Table II.

### Table 4. Empirical Estimates of Logistic Models of the Probability of War based on Executive Constraints

"... In PAGE 12: ... Accordingly, we demonstrate that moving toward stronger executive constraints also yields a visible reduction in the risk of war. Table4 portrays the empirical results from estimating the basic model using the executive constraints variables on the right hand side of equation (1). These results are largely consonant with those found for the democracy and democracy minus autocracy scales presented in Table 3 (above).... In PAGE 14: ... 08 0 . 10 Source: Empirical Estimates in Table4 , above. things being equal, they become more peaceful.... ..."