内生変数 ( モデル内で決まる変数 ) ： Y : GDP 、 R: 利子率。小文字の変数は外生変数 ( モデル外で数値が与えられる変数 )
IS ： C : 消費、 I : 投資、 G : 政府支出、 T : 税金、 N X : 純輸出、 EX : 輸出、 IM : 輸入、
a : 基礎消費、 b : 限界消費性向、 m : 限界輸入性向、 i : 基礎投資、 d : 正の定数、 g : 基礎輸出、 n : 正の定数 LM ： M s
Second, the present study shows that the time cost of raising children is not a prerequisite for the quality-quantity trade-off, pointing to a possibility that fertility may decline even when the opportunity cost of raising children remains constant. 4
This result is consistent with the pattern of fertility decline in Japan in the third quarter of the 20th century where marital fertility declined dramatically while being a housewife remained a common practice. In this period, although women’s participation in the labor force did not rise, the fertility rate declined from 3.65 to 1.91 mainly due to the change in marital fertility (National Institute of Population and Social Security Research of Japan, 2008, Table 4.15). 5
Test 0.00 0.00 0.00
Weak-ID 23.53 23.42 51.60
Test 16.38 16.38 16.38
R-sq 0.23 0.39 0.30 0.42 0.58 0.60 Note: The number of observation is 82. For the IV estimation, we use SMGAP as the instrument to contorl for the endogeneity related to LEGAP . The top figures are the estimated coefficients, and the bottom figures are heteroskedasticity-robust t-statistics. ***, **, and * respectively indicate the significance level at p<0.01, p<0.05, and p<0.10. Under- ID test: Kleibergen-Paap rk LM statistic at the top, and the corresponding p-value at the bottom (Kleibergen & Paap, 2006). Weak-ID test: Kleibergen-Paap rk Wald F statistic at the top, the Stock-Yogo weak ID test critical value for the Cragg-Donald i.i.d. case for a 10% bias at the bottom (Kleibergen & Paap, 2006; Stock & Yogo, 2005). Eqs. (1-1iv) and (1-2iv) are from Kageyama (2013).
considerably lower than startup costs. This implies that the fixed cost c e and the discounted
expected cost of κ affect which funds enter, but that κ has very little impact on fund dynamics beyond entry.
13. Mass of entrants : Set so that the total industry capital in equilibrium equals $1.2 trn. 14. Leverage ratio x : CISDM defines the leverage ratio as the ratio of borrowed funds to capital. Recall that leverage ratios in CISDM are largely clustered around one (Figure 1). Malcolm et al (2009) and FSA (2009, 2010) find the same using different data sets. These numbers are based on self-reporting, and are imprecise – indeed, almost all the responses are integer-valued. This suggests there is some noise in the reported leverage – although the order of magnitude should be correct. Hence, we take 1 as the reported value of leverage. However, because hedge funds may also have “implicit” leverage via derivative positions, we use surveys to get an estimate of “effective” leverage. McGuire and Tsatsaronis (2008) find that “effective” leverage that includes synthetic borrowing (through derivatives) is about 10-20% higher than reported leverage. Hence we set x =1.1, and explore other values as well. 15. Cost of funds p : Calibrating the cost of funds p = + + ( ) 1 k xc requires three inputs: the cost of leverage, the cost of equity, and the leverage ratio. We identify the borrowing cost c with 3-month LIBOR. This implies that c=0, recalling that the borrowing cost is paid up- front. We identify the cost of equity k with the equity risk premium, 15 which is about 4%.
in X j . There are dummies indicating whether a fund charges a
load, and if it is a rear or deferred load. Loads are a pricing element (which we have already amortized into the price mea- sure), but they also indicate funds sold with bundled broker services that investors may value. Rear or deferred loads indicate the presence of formal switching costs to removing assets from the fund. We also include a dummy if the fund is an exchange- traded fund (i.e., SPDRs or Barclay’s iShares) to control for the special liquidity and intraday pricing features of ETFs. We mea- sure the number of additional share classes attached to the fund’s portfolio; for a single-share-class fund this value is zero. The number of other funds managed by the same management com- pany is included to capture any value from being associated with a large fund family. Fund age is in the regressions as well. (Here, both the number of family funds and age enter in logs to parsimoniously embody diminishing marginal effects. Recall that we instrument for age because of its possible correlation with unobservable quality.) We add the current fund manag- er’s tenure, measured in years, as a covariate. And while all of the funds in our sample seek to match the return profile of the S&P 500 index, they do exhibit some small differences in their financial characteristics. These can result from skilled trading activities by a fund’s management despite having a severely constrained portfolio. We thus include measures of tax expo- sure (the taxable distributions yield rate), the yearly average of the ratio of monthly fund returns to those of the S&P 500 index, and the standard deviation of monthly returns. To the extent that fund buyers prefer any persistent positive varia- tions in financial performance, these controls should capture much of this effect. 32
are expected profit maximizers who will exercise their market power in a unilateral, noncooperative fashion, we can then estimate the willingness-to-pay/demand that rationalizes the observed bid.
3 Model of Bidding
Our analysis is based on the share auction model of Wilson (1979) with private information, in which both quantity and price are assumed to be continuous. Wilson’s model was modified to take into account the discreteness of bidding (i.e., finitely many steps in bid functions) as in Kastl (2011). In Horta¸csu and Kastl (2012), we further adapted this model to allow primary dealers to observe the bids of others, hence allowing for “indirect bidders,” whose bids are routed by primary dealers.
Financial Health of Local Banks and Failed Bank Acquisition Likelihood
This table reports results of a fixed effects logit regression. The dependent variable Pr(acquisition) takes the value of one if potential acquirer j acquires failed bank i and zero otherwise. Tier 1 Capital Ratio (potential acquirer) is the Tier 1 capital ratio of the potential acquirer. Leverage Ratio (potential acquirer) is the common leverage ratio of the potential acquirer (the ratio of Tier 1 (core) capital and (adjusted) total assets). Distance is the average pairwise distance (in 100-mile increments) between all pairs of branches of the failed bank and potential acquirer. Distance (% CRE Loans) is the absolute difference between the failed bank’s and the potential acquirer’s percentage of total loans held in CRE loans. HHI is the average increase in local deposit market concentration that would result from potential acquirer j acquiring the branch network of failed bank i. HHI ranges from 0 to 1,000, where 0 indicates a merger that does not increase local market concentration and 1,000 indicates a merger that transforms a perfectly competitive local market into a local monopoly. P50 Tier 1 Capital Ratio of Local Potential Acquirers is the median Tier 1 capital ratio of the failed bank’s local potential acquirers. Local potential acquirers are potential acquirers whose branch network overlaps in at least one zip code with the branch network of the failed bank. % Well-Capitalized Local Potential Acquirers is the percentage of local potential acquirers whose Tier 1 capital ratio is above the median Tier 1 capital ratio across local potential acquirers. P50 Tier 1 Capital Ratio Close CRE Potential Acquirers is the median Tier 1 capital ratio in the group of close CRE potential acquirers. Close CRE potential acquirers are potential acquirers within the first quartile of loan portfolio closeness according to the CRE distance metric. % Well-Capitalized Close CRE Potential
Considering externalities in a matching model poses a challenge (see, e.g., Sasaki and Toda, 1996, and Hafalir, 2008). This is because, with externalities, payo¤s depend not only on matching but also on the entire assignment of who match with whom. The solution concept in such a case thus has to take into consideration, for each deviation, what the entire assignment would be in addition to with whom the deviating players would match. To incorporate what the assignment would be after each deviation, Sasaki and Toda (1996) and Hafalir (2008) propose an estimation function that maps each deviation to an expectation about the possible assignments following the deviation. Despite taking this approach, the model with general form of externalities is complex and the existence of a stable matching requires strong conditions. In our case, however, complexities are reduced by the fact that the externalities take a particular form — it depends only on the aggregate number of operating …rms negatively (i.e., negative network externalities). Hence, we can modify the estimation function approach so that the estimation is only about the aggregate number of operating …rms.
and significant. This result suggests that banks’ BCR matters in firms’ investment decisions when firms are productive and thus have higher demand for investment. Using the estimated model, we conduct counterfactual experiments to quantify the effect of capital injections that took place in March 1998 and 1999 in Japan. The counterfactual experiments suggest that the capital injections had a negligible impact on the average investment rate, although there is a reallocation effect, with investment shifted from low- to high-productivity firms. The paper most closely related to ours is that of GS, who also examine the effects of bank recapitalization policies on the supply of credit and client firm performance, including firm investment, using matched firm–bank data from the Japanese banking crisis. The authors find that the size of the capital injection is important for its success: If capital injections are large enough so that recapitalized banks achieve capital requirements, such banks increase the supply of credit and firms that borrow from the recapitalized banks increase their investment. This paper’s contribution beyond that of GS is as follows. First, we examine whether firms’ loan and investment responses to their banks’ recapitalization depend on their TFP. This question naturally arises because, theoretically, the higher firm productivity, the larger firm investment and the demand for external finance tend to be. Therefore, bank lending attitude, which likely depends on BCR under the banking regulation, may be more important for high-productivity firms. The finding that high-productivity firms increase their investments more than low-productivity firms in response to their associated banks’ recapitalization would suggest that the resource is allocated toward more productive firms as a result of capital injection. 3 Second, we use the BCR as the main variable to