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不良債権： 貸したのに返ってこないお金 ( 不良な債権 ) マクロ経済学で銀行の不良債権問題を考える理由： 世界恐慌、バブル崩壊、リーマン・ショックなど、銀行の不良債権問題に起因 16.2 貸出時に生じるリスクへの対処 金融機関とリスク： 金融機関にとって最も重要なのは、さまざまなリスクに対処すること

とする。この時の短期的な変化を IS-LM と AD-AS のグラフを用いて示せ。 2.[r]

15.2 財政の 3 大機能：景気の安定・所得の再分配・社会資本の供給 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 15.3 財政の問題点：財政政策は行わない方が良い ? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 15.4 補足資料 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 第 16 章 金融 (1): 中央銀行と金融政策 85 16.1 マネタリーベースとマネーサプライ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

インフレ (物価上昇) が生じている場合の名目 GDP 成長率と実質 GDP 成長率の関係を示せ。. 好況の際の総需要と潜在 GDP の関係を示せ。.[r]

6.4 定式化準備：変数一覧 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

明海大学マクロ経済学：影山純二 ( 学生用 ) IS 曲線の導出： 得られた 3 つの条件を 1 つのグラフにまとめる 7.2 貨幣市場の均衡： LM 曲線のグラフ表現 貨幣市場の均衡条件 ( 貨幣需要 = 実質マネーサプライ ) ： L 1 + L 2 = M s

流動性のわなにおける財政政策と金融政策の効果を、 IS-LM モデルのグラフを用いつつ説明せよ。. 56.[r]

実質マネーサプライ： 名目マネーサプライから物価の影響を排除したもの = M s P • 名目マネーサプライで、実際にどれだけモノを購買できるか表す 例： 名目マネーサプライが 20% 、物価が 20% 上がる場合 • 実質マネーサプライは変化しない

投資が利子率に対してより弾力的 ( 投資が利子率の変化に対してより大きく反応する ) になったとする。この時、投資と利子. 率のグラフはどうななるか。グラフを書きつつ説明せよ。[r]

政府の能力的限界： 政府がわかる情報には限界がある。さらに情報把握や政策実行に時間のラグ (遅れ) がある 資源配分の歪曲： 市場ほど効率的に資源 (お金) を使えない. 将来世代への負担転嫁： 将来世代が現在世代のツケ (借金) を払うことになる[r]

Updates and Implications (Human Capital Updated Files)』CID Working Paper no.[r]

This has two implications. First, parents use the same income-dependent relative preferences to measure their children’s well-being. Second, since parents obtain their own utility from consumption in adulthood, they care about their children’s consumption in their adulthood. To capture this latter aspect, I assume that parents are concerned with children’s growth that signals the children’s consumption in adulthood and measure children’s growth with educational output.

This is rooted in the findings in Kageyama （2012） , which empirically showed that the relationships between LEGAP and these happiness indicators are bidirectional. In one direction, LEGAP negatively affects both HPN and HPGAP . An increase in LEGAP raises women ’ s widowhood ratio, and, since widows are, on average, less happy, it lowers women ’ s average happiness, HPN, and HPGAP . We call this effect the “ marital-status composition effect ” as the marital-status composition plays a central role.

) ↑→マネーサプライ (M s ) ↑→利子率 (R) ↓ • 90 年代前半まで、 M b ↑→ M s ↑→ R ↓。貨幣乗数が安定し、理論通り • 90 年代後半以降、 M b ↑→ M s 変わらず、 R は低位安定。流動性の罠へ陥る

September 1, 2016 Abstract This article examines the effect of government capital injections into financially trou- bled banks on corporate investment during the Japanese banking crisis of the late 1990s. By helping banks meet the capital requirements imposed by Japanese banking regula- tion, recapitalization enables banks to respond to loan demands, which could help firms increase their investment. To test this mechanism empirically, we combine the balance sheet data of Japanese manufacturing firms with bank balance sheet data and estimate a linear investment model where the investment rate is a function of not only firm pro- ductivity and size but also bank regulatory capital ratios. We find that the coefficient of the interaction between a firm’s total factor productivity measure and a bank’s capital ratio is positive and significant, implying that the bank’s capital ratio affects more pro- ductive firms. Counterfactual policy experiments suggest that capital injections made in March 1998 and 1999 had a negligible impact on the average investment rate, although there was a reallocation effect, shifting investments from low- to high-productivity firms.

• We have assumed that there is only one hedge fund style. What might change if we consider multiple hedge fund styles in the model industry? If the manager’s talent also involves an aptitude for one style over another, then this would be equivalent to having several styles calibrated independently. Since the policy results hinge on broad features of the model and the data, the outcomes of policy experiments are unlikely to change much. The only difference is that some styles tend to rely more heavily on leverage than others, so those are more likely to be hurt by leverage limits. In the working version of the paper, we show that most styles have reported leverage around 1. Out of the fourteen styles considered, four report leverage of around 2 (Convertible Arbitrage, Fixed Income, Equity Market Neutral and Relative Value), and three styles report leverage higher than 2 (Fixed income arbitrage, CTAs and CPOs). Table 8 shows the impact of a leverage cap of 1 on the industry

ponent of ) S i g ∀q and weakly decreasing in q ∀s g i , for g ∈ {P, I, D}. Note that this assumption implies that learning other bidders’ signals does not affect one’s own valuation – i.e. we have a setting with private, not interdependent values. This assumption may be more palatable for certain securities (such as shorter term securities, which are essentially cash substitutes) than others, but is the most tractable one under which we can pursue the “demand heterogeneity” vs. “market power” decomposition. Note that under this assumption, the additional information that a primary dealer j possesses due to observing her customers’ orders, Z P

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

Acquirers is the percentage of close CRE potential acquirers whose Tier 1 capital ratios is above the median Tier 1 capital ratio across local potential acquirers. P50 Tier 1 Capital Ratio of HHI Potential Acquirers is the median Tier 1 capital ratio of all potential acquirers whose acquisition of the failed bank would increase local deposit market concentration. % Well-Capitalized HHI Potential Acquirers is the percentage of potential acquirers whose Tier 1 capital ratio is above the median Tier 1 capital ratio among the group of potential acquirers whose acquisition of the failed bank would increase local deposit market concentration. Potential acquirer controls include Size, Liquidity Ratio, % Residential Loans, % CRE Loans, % C&I Loans, % Consumer Loans, 30-89PD Ratio, NPL Ratio, OREO Ratio, and Unused Commitment Ratio . Specifications (3) to (6) include failed bank fixed effects and potential acquirer-quarter fixed effects. Standard errors are presented in parentheses, and are clustered at the level of the failed bank’s state headquarters. ***, **, and * represent statistical significance at 1%, 5%, and 10% levels, respectively.

In particular, we will cover the basic methodology of structural estimation - production function estimation, estimation of static entry/exit models, single-agent dynamic models, and dyn[r]