This paper re-evaluates key past results of unit root tests, emphasizing that the use of a conventional level of significance is not in general optimal due to the test having low power. The decision-based significance levels for popular unit root tests, chosen using the line of enlightened judgement under a symmetric loss function, are found to be much higher than conventional ones. We also propose simple calibration rules for the decision-based significance levels for a range of unit root tests. At the decision-based significance levels, many time series in Nelson and Plosser (1982) (extended) data set are judged to be trend-stationary, including real income variables, employment variables and money stock. We also find that nearly all real exchange rates covered in Elliott and Pesavento (2006) study are stationary; and that most of the real interest rates covered in Rapach and Weber (2004) study are stationary. In addition, using a specific loss function, the U.S. nominal interest rate is found to be stationary under economically sensible values of relative loss and prior belief for the null hypothesis.

This paper presents a brief review of interval-based hypothesis testing, widely used in bio-statistics, medical science, and psychology, namely, tests for minimum-effect, equivalence, and non-inferiority. We present the methods in the contexts of a one-sample t-test and a test for linear restrictions in a regression. We present applications in testing for market efficiency, validity of asset-pricing models, and persistence of economic time series. We argue that, from the point of view of economics and finance, interval-based hypothesis testing provides more sensible inferential outcomes than those based on point-null hypothesis. We propose that interval-based tests be routinely employed in empirical research in business, as an alternative to point null hypothesis testing, especially in the new era of big data.

As the guest editors of this Special Issue, we feel proud and grateful to write the editorial note of this issue, which consists of seven high-quality research papers [...]

We decompose exchange rate exposure into systematic and partial parts. The former is the product of the exposure of the market portfolio and a firm’s market beta, reflecting the risk of the exchange rate to a macroeconomy. The latter is the residual one that most previous studies have examined. Using Japanese data, we find that Japanese firms are systematically exposed to the exchange rate from the beginning of 2000. We also highlight the timely yen-selling intervention by the Bank of Japan when the firms are systematically exposed. However, we find that, even when most Japanese firms are significantly exposed to the exchange rate, the partial exposure can seriously underestimate the full extent of the exchange rate exposure.