The long-run relationship between private and public savings: An empirical note

This empirical note extends the recent work by Holmes (2006) in examining the long-run relationship between private and public savings in the U.S. over the post-World War II period. Standard Engle-Granger cointegration tests fail to reject the null hypothesis of no cointegration; however, once allowance is made for an endogenous break in the cointegrating relationship, the weak form of the Ricardian equivalence proposition is supported.     

Sources of time varying return comovements during different economic regimes: evidence from the emerging Indian equity market

We study the economic and non-economic sources of stock return comovements of the emerging Indian equity market and the developed equity markets of the US, UK, Germany, France, Canada and Japan. Our findings show that the probability of extreme comovements in the economic contraction regime is relatively higher than in the economic expansion regime. We show that international interest rates, inflation uncertainty and dividend yields are the main drivers of the asymmetric return comovements. Findings reported in the paper imply that the impact of interest rates and inflation on return comovements could be used for anticipating financial contagion and/or spillover effects. This is particularly critical since during extreme market conditions, the tail return comovements can potentially reveal critical information for active portfolio management.     

Trade with Time Zone Differences: Factor Market Implications

The main purpose of this study is to illustrate, with a simple two‐factor (skilled and unskilled labor) model, how a time‐saving improvement in business‐services trade benefitting from differences in time zones can have an impact on national factor markets. In doing so, we intend to capture the situation where the night‐shift work in one country is replaced by the day‐shift work in another country. In other words, we will show that trade with time zone differences will result in shifts of the relative supplies and demands for skilled labor around the globe.     

Postservice Recovery Customer Satisfaction in Indian Retail Banking: Scale Development and Validation

The construct of postservice customer satisfaction with respect to Indian retail banking has been studied. Furthermore, the development and validation of the postservice recovery customer satisfaction scale in Indian retail banking is detailed. The scale development process was carried out over three stages (item generation, scale purification, scale validation) and comprised of conducting depth interviews and focus groups for item generation and three separate phases of data collection, involving a total of 851 individual respondents. The data provide evidence for face, content, discriminant and convergent validity, dimensionality, reliability, and generalizability of the customer satisfaction scale in Indian retail banking.     

Corporate control and the choice of investment financing: the case of corporate acquisitions in India

The market for corporate control offers a rich framework to study the interaction between investment and financing decisions. Do corporations have specific preferences for the means of financing acquisitions, such using cash or equity to pay the claims of the target firm’s shareholders? This study builds a unique sample of 1041 corporate acquisitions over the period 2000–2018 in India, a major emerging economy with fast-growing capital markets. The study investigates separately corporate preferences for the means of payment and the financing sources for acquisitions, using multinomial logit and nested logit models. First, we find that different factors explain the payment and financing decisions. Second, the cash payment decision is best explained by the target’s relative size, greater tender offers, cross-border deals, and cash reserves. Third, the findings are most aligned with pecking order theory and cost of capital considerations.

     

Robust Hierarchical Bayes Small Area Estimation for the Nested Error Linear Regression Model

Standard model‐based small area estimates perform poorly in presence of outliers. Sinha & Rao ( 2009 ) developed robust frequentist predictors of small area means. In this article, we present a robust Bayesian method to handle outliers in unit‐level data by extending the nested error regression model. We consider a finite mixture of normal distributions for the unit‐level error to model outliers and produce noninformative Bayes predictors of small area means. Our modelling approach generalises that of Datta & Ghosh ( 1991 ) under the normality assumption. Application of our method to a data set which is suspected to contain an outlier confirms this suspicion, correctly identifies the suspected outlier and produces robust predictors and posterior standard deviations of the small area means. Evaluation of several procedures including the M‐quantile method of Chambers & Tzavidis ( 2006 ) via simulations shows that our proposed method is as good as other procedures in terms of bias, variability and coverage probability of confidence and credible intervals when there are no outliers. In the presence of outliers, while our method and Sinha–Rao method perform similarly, they improve over the other methods. This superior performance of our procedure shows its dual (Bayes and frequentist) dominance, which should make it attractive to all practitioners, Bayesians and frequentists, of small area estimation.     

Comparison of test vs. control treatments using distance optimality criterion

We consider the problem of comparison of one test treatment (τ₀) with a set of v control treatments (τ₁, τ₂, …, τ_v) using distance optimality [DS-optimality] criterion introduced by Sinha (1970) in some treatment-connected design settings. It turns out that the nature of DS-optimal designs is quite similar to that for the usual A−, D− and E− optimality criteria. However, the optimality problem is quite complicated in most situations. First we deal with the CRD model and derive DS-optimal allocations for a given set of treatments. The results are almost identical to the A-optimal allocations for such problems. Then we consider a block design set-up and examine the nature of DS-optimal designs. In the process, we introduce the method of weighted coverage probability and maximize the resulting expression to obtain an optimal design. Received: December 1999