Comment

From the point of view of a practitioner, I found Professor Keane's paper interesting and stimulating. I do not agree with the arguments, mainly because the model I have in mind of the way in which the world really works is not consistent with the theoretical model which Keane is addressing. His puzzles and paradoxes do not seem to me to be particularly puzzling or paradoxical when certain unrealistic theoretica assumptions are relaxed.

This paper provides a brief summary of some of Professor Keane's arguments; presents an alternative view of the way equity markets actually behave in the real world; and finally contrasts the implications of this alternative view with Professor Keane' own conclusions on such issues as managements' approach to investment decisions and executive renumeration packages.     

Is it efficient to analyse efficiency rankings?

When production functions are estimated as frontier functions, the deviations from the frontier can be interpreted as individual inefficiency estimates. Unfortunately, it has recently been shown that efficiency differences across individuals are very often statistically insignificant. In this paper, we will analyse the consequences of the consideration of confidence statements for the reliability of efficiency rankings. The stochastic frontier and confidence intervals derived by Horrace and Schmidt are compared to the COLS approach and bootstrap confidence intervals. The membership function is proposed as a simple Monte-Carlo approximation for the probability for an individual to be the most efficient in the sample. First version received: May 1998/final version accepted: July 1999     

Public-private partnerships and scale efficiency in Brazilian ports: Evidence from two-stage DEA analysis

This paper assesses the impacts of public-private partnerships on major Brazilian public ports. It is proposed that these kinds of arrangements with private terminal operators could help achieving higher levels of scale efficiency by enhancing coordination processes, providing more adequate information technologies, and higher connectivity with other transportation modes. Methodology relies on factor extraction of inputs/outputs as a first step to compute DEA efficiency estimates, followed by truncated bootstrapped regression analysis to test different contextual variables. Results indicate a strong positive impact of public-private partnerships on port scale efficiency, corroborating their impacts in relation to the most productive scale size.     

Bootstrapping covariate stationarity tests for inflation rates

This paper investigates the stationarity properties of international inflation rates by bootstrapping two stationarity tests with covariates in Jansson (2004). When the asymptotic critical values are used, the two powerful tests are found to reject the null hypothesis less in the presence of a large negative moving-average (MA) error in inflation. To cope with this problem, a parametric bootstrap scheme is developed and then is investigated by a Monte Carlo study. The simulation results demonstrate that the bootstrap tests display a better control over the empirical rejection rates at finite samples. Furthermore, after applying these tests to the inflation in G-10 countries, we find that one of the two tests using bootstrap critical values yields inferences that differ from when using asymptotic ones, and as a whole, the bootstrap tests consistently provide strong evidence in support of mean reversion in inflation in most countries of the G-10.     

Asymptotic and bootstrap prediction regions for vector autoregression

Small sample properties of asymptotic and bootstrap prediction regions for VAR models are evaluated and compared. Monte Carlo simulations reveal that the bootstrap prediction region based on the percentile-t method outperforms its asymptotic and other bootstrap alternatives in small samples. It provides the most accurate assessment of future uncertainty under both normal and non-normal innovations. The use of an asymptotic prediction region may result in a serious under-estimation of future uncertainty when the sample size is small. When the model is near non-stationary, the use of the bootstrap region based on the percentile-t method is recommended, although extreme care should be taken when it is used for medium to long-term forecasting.     

The relationship between globalization and insurance activities: A panel data analysis

This study applies a Bootstrap Panel Granger causality test to investigate whether there is causal relationship between globalization and insurance activity. We examine data from sigma reports of Swiss Reinsurance Company for 8 Eastern Asian countries over the period of 1979–2008. Empirically, results for one-way Granger causality show the influence of total insurance activity, life insurance activity, and non-life insurance activity on globalization only in Korea. However, there is strong causality from globalization to insurance activity for Thailand, Malaysia, and the Philippines. In our research, the results show that the causality between globalization and insurance activity varies across countries with different conditions. The findings of this study could provide important policy implications for the 8 Eastern Asian countries under study, namely India, Indonesia, Japan, Malaysia, the Philippines, Singapore, South Korea, and Thailand.     

Semiparametric Estimation for Non-Ruin Probabilities

We consider the classical risk model with unknown claim size distribution F and unknown Poisson arrival rate u . Given a sample of claims from F and a sample of interarrival times for these claims, we construct an estimator for the function Z ( u ), which gives the probability of non-ruin in that model for initial surplus u . We obtain strong consistency and asymptotic normality for that estimator for a large class of claim distributions F . Confidence bounds for Z ( u ) based on the bootstrap are also given and illustrated by some numerical examples.     

Can We Bootstrap DEA Scores?

This paper discusses the feasibility of bootstrapping DEA scores in the context of the earlier paper by Ferrier and Hirschberg (1997). A simple experiment is devised to demonstrate that in the one-input, one-output case the bootstrap of the non-modified DEA scores is not a failure of the bootstrap.     

Temporal causality between house prices and output in the US: A bootstrap rolling-window approach

This paper examines the causal relationships between the real house price index and real GDP per capita in the US, using the bootstrap Granger (temporal) non-causality test and a fixed-size rolling-window estimation approach. We use quarterly time-series data on the real house price index and real GDP per capita, covering the period 1963:Q1 to 2012:Q2. The full-sample bootstrap non-Granger causality test result suggests the existence of a unidirectional causality running from the real house price index to real GDP per capita. A wide variety of tests of parameter constancy used to examine the stability of the estimated vector autoregressive models indicate short- and long-run instability. This suggests that we cannot rely on the full-sample causality tests and, hence, this warrants a time-varying (bootstrap) rolling-window approach to examine the causal relationship between these two variables. Using a rolling window size of 28 quarters, we find that while causality from the real house price to real GDP per capita occurs frequently, significant, but less frequent, evidence of real GDP per capita causing the real house price also occurs. These results imply that while the real house price leads real GDP per capita, in general (both during expansions and recessions), significant feedbacks also exist from real GDP per capita to the real house price.