### Table 1 Idiosyncratic Endowment Process: Parameter Estimates

1999

"... In PAGE 17: ...e discuss some robustness checks along these lines #28e.g., di#0Berencing and quasi-di#0Berencing the data#29 and #0Cnd the qualitative nature of our results to be relatively stable. Table1 reports parameter estimates obtained using GMM in conjunction with the mo- ments in equations #2812#29. In Panel A we report estimates which constrain #1B H = #1B L and in Panel B we relax this restriction.... In PAGE 18: ... Our estimates incorporate this cross sectional evidence, but only implicitly #28via age-dependence in our moments#29. Our guess is that a more formal treatment of how inequality increases with age would generate an even higher value for the parameter #1A than we report in Table1 #28Storesletten, Telmer, and Yaron #281997#29 investigate this further#29. Finally, providing a frame of reference for our results on heteroskedastic idiosyncratic shocks over the business cycle is more problematic; aside from Heaton and Lucas #281996#29 we are not aware of comparable studies.... In PAGE 19: ...blush, this might seem inconsistent with the values reported in Table1 , where we estimate the increase in the conditional standard deviation of the persistent shock, coincident with a downturn, to be on the order of 126#25. What apos;s going on, however, is that the larger number is associated with the conditional distribution of a given agent apos;s innovation, whereas the smaller number is much more closely associated with the unconditional distribution.... In PAGE 22: ...hose of U.S. females in 1991 and population growth is set to 1.0#25. The process for idiosyncratic labor supply, equation #282#29, is implemented as a discrete approximation to the autoregressive time series model and is parameterized using our point estimates from Table1 . The age dependentintercept terms, #14 h , are chosen so that, on aver- age, our theoretical age-earnings pro#0Cle matches that of the PSID.... In PAGE 22: ... Further details are provided in appendix B. This calibration of our theoretical income process, equation #282#29, su#0Bers from two poten- tial sources of discrepancy relative to the PSID-based estimates from Table1 . The #0Crst is the error induced by approximating an in#0Cnite-state autoregression with a #0Cnite-state Markov chain.... In PAGE 22: ... More to the point, we #0Cnd that if we use our theoretical economy to generate an arbitrarily long sequence of 3 year panel data sets #28which corresponds to our PSID sampling method#29, the application of our GMM estimator #28section 3.2#29 to these data yields estimates which closely match those from Table1 ; our simulated point estimates are 0.... In PAGE 42: ... When we use our methodology we obtain an estimate of 0.931, which is essentially identical to that from the overlapping panels in Table1 . The di#0Berences in our approaches are as follows.... In PAGE 49: ... For our speci#0Ccation, if we ignore the transitory shocks, quot; it , as well as the terms which capture cross sectional variation due to age and education #28see equation #287#29 in section 3.2#29, then our estimation in Table1 boils down to a time series model of the residuals from a regression involving only year-dummy variables. In a large cross section this will be, z it = log c it , ~ E t log c it ; whichhave a cross sectional mean of zero, by construction, and a sample mean of zero, by least squares.... In PAGE 50: ... The quantityofinterest in equation #2820#29 can now be written as, log c i;t+1 =c t+1 c it =c t #11 log #0D i;t+1 , log #0D it = z i;t+1 , z it , 1 2 #10 ~ V t+1 #28log #0D i;t+1 #29 , ~ V t #28log #0D it #29 #11 #2821#29 The term in parentheses | the di#0Berence in the variances | does not vary in the cross section. Consequently, application of the cross sectional variance operator to both sides of equation #2821#29 implies, ~ V t+1 #12 log c i;t+1 =c t+1 c it =c t #13 = ~ V t+1 #28z i;t+1 , z it #29 : Ignoring the transitory shocks, the process underlying the estimates in Table1 is: z i;t+1 , z it =#281,#1A#29z it + #11 i;t+1 ; where the variance of #11 i;t+1 depends on the aggregate shock. For values of #1A close to unity the variance of changes in z it is approximately equal to the variance of #11 i;t+1 .... In PAGE 50: ... For values of #1A close to unity the variance of changes in z it is approximately equal to the variance of #11 i;t+1 . The left side of equation #2820#29 is, therefore, approximately equal to the variance of innovations, #11 i;t+1 , ~ V t+1 #12 log c i;t+1 =c t+1 c it =c t #13 #19 ~ V t+1 #28#11 i;t+1 #29 : In this sense, the estimates of #1B H and #1B L in Table1 provide estimates of what is necessary to calibrate the Constantinides-Du#0Ee model. All that remains are to map our estimates into numerical values for a and b from equation #2820#29.... In PAGE 50: ... Since aggregate consumption growth | the variable on the right side of equation #2820#29 | takes on only two values #283.8 percent and ,0:8 percent#29, computing the parameters a and b simply involves two linear equations: 0:037 = a +0:038b 0:181 = a , 0:008b ; where the values on the left are the cross sectional variances from Table1 . The resulting values are a =0:156 and b = ,3:130.... ..."

Cited by 14

### Table 1 Idiosyncratic Income Process: Parameter Estimates

1997

"... In PAGE 11: ... After presenting our benchmark estimation results we discuss a number of modi cations intended to incor- porate a non-degenerate distribution of initial conditions and/or agent speci c xed e ects. Table1 , panel A reports parameter estimates obtained using GMM in conjunction with the moments in equations (11). Our point estimate of is 0.... In PAGE 12: ... We estimated the parameters of our time series process using a GMM estimator analogous to that outlined in equations (11). The results for the rst-di erenced data, which handles what we view as the most important omission | xed e ects | are reported in Table1 , panel B. We see that the the conditional variances are qualitatively very similar to those from panel A, whereas the estimates of are, not surprisingly, somewhat smaller at .... In PAGE 13: ... The solid-dotted graph in Figure 3 reports the age-dependent cross-sectional variance of log of idiosyncratic earnings, yit, which would apply in a life cycle economy with no aggregate shocks and a large number of agents of each generation, where each agent derives their earnings from the process (9). Population moments are evaluated at the point estimates from Table1 , panel A. When estimating this process we controlled for some characteristics, in particular education.... In PAGE 13: ... We x the ratio of the transitory to the permanent standard deviation at 1.44 (corresponding to Table1 , panel A). We then choose the remaining three parameters, , and to match the cross-sectional standard deviation of the young (0.... In PAGE 14: ...5 Benchmark processes We have chosen to explore the implications of two di erent processes for log of indi- vidual earnings: 1. Based on our GMM estimates from Table1 we set = 0:9345, 2 = 0:061, and 2 = 0:0172. When estimating this process we controlled for some character- istics, in particular education.... ..."

### Table 3 Idiosyncratic Consumption Process: Parameter Estimates

2003

"... In PAGE 10: ... For many questions, most notably asset pricing, we are equally interested in consumption and how its cross-sectional distribution is related to aggregate variation. Table3 replicates the estimation in Panels C, D and F of Table 2 using data on food expenditure from the PSID (the only consumption data available).... ..."

Cited by 5

### Table 12: Sensitivity analysis with respect to the cost of capital and the entrepreneurial risk. Numbers are in percentage term.

2000

"... In PAGE 45: ... Because the stochastic properties of the shock determine the minimum value of assets that are necessary to start a business or to implement larger projects, that is, the borrowing limits, the sensitivity analysis with respect to this parameter provides a joint evaluation of the importance of the riskiness of the business and the borrowing limits. The rst line of Table12 reports distributional statistics for the economy without inter- mediation cost , and therefore, the marginal return on savings is the same for workers and entrepreneurs. These statistics can be compared with the same statistics for the baseline model that are reported at the bottom of the table.... In PAGE 45: ...The riskiness of the business and the borrowing limits also have distributional consequences, as can be seen in the middle section of Table12 which reports distributional statistics when the idiosyncratic technological shock takes the mean value (low risk) and when the depreciation rate associated with a low value of the shock is doubled (high risk). Note that when 1 = 2 = , entrepreneurs still face the risk of loosing the business, even though without losses.... ..."

Cited by 13

### Table 5: Idiosyncratic Term Descriptive Statistics Germany Sweden Spain Euro

"... In PAGE 23: ...3.1 RBF Residuals - Descriptive Statistics Table5 presents the descriptive statistics for country-level idiosyncratic residual returm obtained by the application of the Kajiji-4 spillover model. At four significant digits, the results show a mean of zero and a relatively s~nall to near-zero variance measurement.... ..."

### Table 6: Second-Order Approximation Using Idiosyncratic Data

"... In PAGE 27: ...8. Table6 presents the results when the corresponding experiment is performed in my model under the baseline set of parameter values, and under several alternative parametric... In PAGE 31: ... These are eminently testable propositions.18 Given the results of Table6 , it even seems worthwhile to attempt to estimate an equation of the form of the second-order approximation to the Euler equation (but only if idiosyncratic data are used). The point of the earlier discussion of Table 6 was that the coe cient on Ei;t 2 i;t+1 didnotyieldanunbiasedestimateof .... In PAGE 31: ...arameter values). These are eminently testable propositions.18 Given the results of Table 6, it even seems worthwhile to attempt to estimate an equation of the form of the second-order approximation to the Euler equation (but only if idiosyncratic data are used). The point of the earlier discussion of Table6 was that the coe cient on Ei;t 2 i;t+1 didnotyieldanunbiasedestimateof . From a less structural point of view, however, the lesson of the table is that for any tested set of parameter values the model implies a hugely statistically signi cant relationship between consumption growth and Ei;t 2 i;t+1.... ..."

### Table 3 Diversi cation in the emerging markets: Monthly Data

"... In PAGE 3: ...f 1.7. However, we have found that a dominant portion of these higher moments in the emerging markets equity returns are idiosyncratic and can thus be diversi- ed away. In Table3 , we present the summary statistics of the number-average portfolios of these emerging market equity indices. What is common knowledge is the obvious e ect of diversi cation in reducing volatility: The average standard deviation (annualized) decreases from 43% for single country portfolios to less than 19% for the nine-country portfolio.... In PAGE 10: ... Both skewness and kurtosis are downward sloping, implying a dominant proportion of idiosyncratic jump risks. Table3 reports the summary statistics of the portfolio return.... ..."

### Table 3: Time Slot E ect, Day E ect, and Idiosyncratic Taste for the Outside Alternative

1997

Cited by 4