The Credit Channel in Japan: Resolving the Supply versus Demand Puzzle

A long-standing macroeconomic issue is how monetary policy affects the real economy. Previous VAR research has found that bank loans typically contracted following a monetary tightening. This is consistent with the credit view: a monetary tightening decreases aggregate demand by shifting the loan supply curve left. However, the finding is consistent with another interpretation: a monetary tightening operates through the conventional money channel and decreases the demand for loans. This observational equivalence is called the "supply-versus-demand puzzle." This paper shows that embedding the loan price in a macroeconomic VAR model reduces the puzzle to the simultaneous equation bias. As a proxy for the loan price, the survey-based data is utilised. The main finding is that the loan supply curve shifts left after a monetary tightening. The effectiveness of monetary policy is also confirmed. From these results, this paper concludes that monetary policy operates through the credit channel in Japan

Is the lending channel of monetary policy dominant in Australia? (Revised)

A long-standing macroeconomic issue is how monetary policy affects the real economy. The lending view is that tight money affects aggregate demand by shifting the supply schedule left in the bank loan market. Previous studies have found that loans contract following tight money. It is not clear whether the financial contraction reflects a shift of the supply schedule or the demand schedule in the loan market, however. In an attempt to identify the shifts of the demand and supply schedules in the Australian loan market, this paper employs an original approach, which includes the quantity and the price of new loans. A variety of robustness check confirms that the lending view is not supported. The paper also examines features of Australian bank behaviour which make the lending view less plausible

DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning

Differential private optimization for nonconvex smooth objective is considered. In the previous work, the best known utility bound is $\widetilde O(\sqrt{d}/(n\varepsilon_\mathrm{DP}))$ in terms of the squared full gradient norm, which is achieved by Differential Private Gradient Descent (DP-GD) as an instance, where $n$ is the sample size, $d$ is the problem dimensionality and $\varepsilon_\mathrm{DP}$ is the differential privacy parameter. To improve the best known utility bound, we propose a new differential private optimization framework called \emph{DIFF2 (DIFFerential private optimization via gradient DIFFerences)} that constructs a differential private global gradient estimator with possibly quite small variance based on communicated \emph{gradient differences} rather than gradients themselves. It is shown that DIFF2 with a gradient descent subroutine achieves the utility of $\widetilde O(d^{2/3}/(n\varepsilon_\mathrm{DP})^{4/3})$, which can be significantly better than the previous one in terms of the dependence on the sample size $n$. To the best of our knowledge, this is the first fundamental result to improve the standard utility $\widetilde O(\sqrt{d}/(n\varepsilon_\mathrm{DP}))$ for nonconvex objectives. Additionally, a more computational and communication efficient subroutine is combined with DIFF2 and its theoretical analysis is also given. Numerical experiments are conducted to validate the superiority of DIFF2 framework.Comment: 26 page