Bank Liquidity Hoarding Strategies in Uncertain Times: New Evidence from an Emerging Market with Bank-level Data

The paper explores the impact of uncertainty on bank liquidity hoarding, particularly providing new insights on the nature of the impact by bank-level heterogeneity. We consider the cross-sectional dispersion of shocks to key bank variables to estimate uncertainty in the banking sector and include all banking items to construct a comprehensive measure of bank liquidity hoarding. Using a sample of Vietnamese banks during 2007–2019, we document that banks tend to increase total liquidity hoarding in response to higher uncertainty; this pattern is still valid for on- and off-balance sheet liquidity hoarding. Further analysis with bank-level heterogeneity indicates that the impact of banking uncertainty on liquidity hoarding is significantly stronger for weaker banks, i. e., banks that are smaller, more poorly capitalized, and riskier. In testing the “search for yield” hypothesis to explain the linkage between uncertainty and bank liquidity hoarding, we do not find it to be the case. Our findings remain extremely robust after multiple robustness tests.

Demystifying a class of multiply robust estimators

Summary For estimating the population mean of a response variable subject to ignorable missingness, a new class of methods, called multiply robust procedures, has been proposed. The advantage of multiply robust procedures over the traditional doubly robust methods is that they permit the use of multiple candidate models for both the propensity score and the outcome regression, and they are consistent if any one of the multiple models is correctly specified, a property termed multiple robustness. This paper shows that, somewhat surprisingly, multiply robust estimators are special cases of doubly robust estimators, where the final propensity score and outcome regression models are certain combinations of the candidate models. To further improve model specifications in the doubly robust estimators, we adapt a model mixing procedure as an alternative method for combining multiple candidate models. We show that multiple robustness and asymptotic normality can also be achieved by our mixing-based doubly robust estimator. Moreover, our estimator and its theoretical properties are not confined to parametric models. Numerical examples demonstrate that the proposed estimator is comparable to and can even outperform existing multiply robust estimators.

Multiply robust bootstrap variance estimation in the presence of singly imputed survey data

Item nonresponse in surveys is usually dealt with through single imputation. It is well known that treating the imputed values as if they were observed values may lead to serious underestimation of the variance of point estimators. In this article, we propose three pseudo-population bootstrap schemes for estimating the variance of imputed estimators obtained after applying a multiply robust imputation procedure. The proposed procedures can handle large sampling fractions and enjoy the multiple robustness property. Results from a simulation study suggest that the proposed methods perform well in terms of relative bias and coverage probability, for both population totals and quantiles.

Does High-Speed Rail Promote Enterprises Productivity? Evidence from China

High-speed rail (HSR) is often claimed to bring different regions and cities closer together by shortening travel times, which can reduce the costs and increase enterprises productivity to promote a sustainable economy. However, another view argues that HSR transfers economic activities from peripheral cities to core cities, resulting in unbalanced regional economic development and damaging the sustainability of the economy. Based on microdata from China, this paper empirically investigates the impact of HSR on the enterprises productivity in both core cities and peripheral cities and explores the impact mechanism from the perspective of allocation effect and distribution effect caused by HSR. The results show that the connection of HSR positively affects the enterprises productivity in core cities, while it negatively affects the enterprises productivity in peripheral cities, with effect values of 1.38% and -8.45%, respectively. The conclusion still holds after endogenous treatment and multiple robustness tests are conducted. Additionally, the allocation effect analysis shows that the market access caused by HSR has an optimization effect on the resource allocation efficiency of both core cities and peripheral cities. The distribution effect analysis reveals that the distribution of enterprise productivity in peripheral cities has market heterogeneity, regional heterogeneity, and location heterogeneity. The important policy significance of this paper is that, in order to promote the sustainable development of enterprises and the economy, it should reduce policy restrictions and promote the effective flow of capital and talents, carry out the dislocation development of industry for peripheral cities, and “build a nest to attract the phoenix.”

The consequences of unconventional monetary policy in euro area in times of monetary easing

Research background: In this research paper, an attempt is made to evaluate the impacts of ECB’s unconventional monetary policy which has been applied after Global Financial Crisis. Because of the new economic and monetary conditions, the effectiveness of conventional monetary tools has been questioned. Purpose of the article: Designed models examine the consequences of unconventional monetary policy for macroeconomic variables, monetary variables and interest rates in the euro area. Particular attention is paid to the response of the price level, represented by HICP, to various monetary policy innovations. Except a shock in credit multiplier and asset purchase programme (APP), also the effectiveness of a conventional monetary tool, such as main refinancing operation (MRO) interest rate, is inspected. Methods: Use has been made of impulse responses from structural VAR models to analyze a large sample that covers the time horizon of 1999 to 2016. Several econometric tests are performed to provide a profound analysis. The conclusions from baseline models are verified in multiple robustness check models, which are specified under alternative conditions. Findings & Value added: It has been found that, in the aftermath of the Global Financial Crisis, conventional monetary instruments are effective in the short-run. In the long-run, unconventional monetary policy has a greater potential to stabilize the economy than the traditional interest rate transmission channel. The conclusions from the baseline models are verified in multiple robustness check models, which are specified under alternative conditions.