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Purpose: To establish a non-invasive test method for the rapid detection of severe ischemia (SI) in the limbs in patients with peripheral arterial disease (PAD). Methods: Between November 2019 and May 2021, 22 patients admitted for PAD to 2 hospitals agreed to participate in the study. All patients underwent a local heating load (LHL) test. SI was defined as at least 1 ankle-brachial index value of <.4 and/or transcutaneous oximetry value of <30 mmHg. The other cases were classified as mild-to-moderate ischemia (MMI). The LHL test was performed simultaneously with 15 minutes of heating and measurement by attaching a blood flow meter measuring probe combined with a warmer to the patient's dorsal foot. Evaluation consisted of 200-s periods from the start of heating to 800 seconds. For each period, perfusion value (PV) was evaluated, and slope was calculated graphically based on linear regression as PV fluctuation per minute. Test accuracy was calculated using a receiver operating characteristic curve. Results: Twenty-four limbs of 18 patients were finally evaluated; 4 patients (2 with missing data, 1 with collagen disease, and 1 with embolism) were excluded, with 13 and 11 limbs in the SI and MMI groups, respectively. The SI group showed a significantly lower slope value in the first 200 seconds and lower PV at 200 seconds and thereafter. From the slope value, it was possible to detect SI with 85% sensitivity and 73% specificity at 200 seconds. PV could be determined with higher accuracy in periods after 200 seconds, with 85% sensitivity and 82% specificity at 800 seconds. Conclusions: Our non-invasive LHL test could be used as a rapid screening test to detect SI in limbs within 200 seconds, as well as a more accurate test to detect ischemia within 800 seconds. [ABSTRACT FROM AUTHOR]

We assess the empirical evidence about the great moderation using a comprehensive framework to test for multiple structural changes in the coefficients and in the variance of the error term of a linear regression model provided by Perron et al. (Quant Econ 11:1019–1057, 2020). We apply it to the US real GDP and its major components for the period 1960:1 to 2018:4. A notable feature of our approach is that we adopt an unobserved component model, allowing for two breaks in the trend function in 1973:1 and 2008:1, in order to obtain a stationary or cyclical component modelled as an autoregressive process. First, we confirm evidence about the great moderation, i.e., a structural change in variance of the errors in the mid-80s for the various series. Second, additional breaks in variance are found in 1970:3 for GDP and production (goods), after which the sample variance increased by three times. Hence, a part of the great moderation can be viewed as a reversion to the 1960–1970 level of volatility. Third, the evidence about systematic changes in the sum of the autoregressive coefficients (a measure of persistence) is weak over the whole sample period. Finally, we find little evidence of structural changes occurring in both the variance and the coefficients following the great recession (2007–2008). These results support views emphasizing the "good luck" hypothesis as a source of the great moderation, which continues even after the great recession. [ABSTRACT FROM AUTHOR]

We provide a comprehensive treatment for the problem of testing jointly for structural changes in both the regression coefficients and the variance of the errors in a single equation system involving stationary regressors. Our framework is quite general in that we allow for general mixing‐type regressors and the assumptions on the errors are quite mild. Their distribution can be nonnormal and conditional heteroskedasticity is permitted. Extensions to the case with serially correlated errors are also treated. We provide the required tools to address the following testing problems, among others: (a) testing for given numbers of changes in regression coefficients and variance of the errors; (b) testing for some unknown number of changes within some prespecified maximum; (c) testing for changes in variance (regression coefficients) allowing for a given number of changes in the regression coefficients (variance); (d) a sequential procedure to estimate the number of changes present. These testing problems are important for practical applications as witnessed by interests in macroeconomics and finance where documenting structural changes in the variability of shocks to simple autoregressions or vector autoregressive models have been a concern. [ABSTRACT FROM AUTHOR]

Elliott and Müller (EM) (2007) provide a method for constructing a confidence set for the structural break date by inverting a variant of the locally best test statistic. Previous studies have shown that the EM method produces a set with an accurate coverage ratio even for a small break; however, the set is often overly lengthy. This study proposes a simple modification to rehabilitate their method through the long-run variance estimation. Following the literature, we provide an asymptotic justification for the improvement of the modified method over the original method under a nonlocal asymptotic framework. A Monte Carlo simulation shows that the modified method achieves a shorter confidence set than the EM method, especially when the break is large or the HAC correction is conducted. The modified method may exhibit minor errors in the coverage rate when the break is small; however, the coverage is more stable than alternative methods when the break is large. We apply our method to a level shift in post-1980s Japanese inflation data. [ABSTRACT FROM AUTHOR]

Elliott and Müller (2006) considered the problem of testing for general types of parameter variations, including infrequent breaks. They developed a framework that yields optimal tests, in the sense that they nearly attain some local Gaussian power envelop. The main ingredient in their setup is that the variance of the process generating the changes in the parameters must go to zero at a fast rate. They recommended the so-called qL̂L test, a partial sums type test based on the residuals obtained from the restricted model. We show that for breaks that are very small, its power is indeed higher than other tests, including the popular sup-Wald (SW) test. However, the differences are very minor. When the magnitude of change is moderate to large, the power of the test is very low in the context of a regression with lagged dependent variables or when a correction is applied to account for serial correlation in the errors. In many cases, the power goes to zero as the magnitude of change increases. The power of the SW test does not show this non-monotonicity and its power is far superior to the qL̂L test when the break is not very small. We claim that the optimality of the qL̂L test does not come from the properties of the test statistics but the criterion adopted, which is not useful to analyze structural change tests. Instead, we use fixed-break size asymptotic approximations to assess the relative efficiency or power of the two tests. When doing so, it is shown that the SW test indeed dominates the qL̂L test and, in many cases, the latter has zero relative asymptotic efficiency. [ABSTRACT FROM AUTHOR]

We provide methods for estimating and testing multiple structural changes occurring at unknown dates in linear models using band spectral regressions. We consider changes over time within some frequency bands, permitting the coefficients to be different across frequency bands. Using standard assumptions, we show that the limit distributions obtained are similar to those in the time domain counterpart. We show that when the coefficients change only within some frequency band, we have increased efficiency of the estimates and power of the tests. We also discuss a very useful application related to contexts in which the data are contaminated by some low-frequency process (e.g. level shifts or trends) and that the researcher is interested in whether the original non-contaminated model is stable. All that is needed to obtain estimates of the break dates and tests for structural changes that are not affected by such low-frequency contaminations is to truncate a low-frequency band that shrinks to zero at rate [ABSTRACT FROM AUTHOR]

In empirical applications based on linear regression models, structural changes often occur in both the error variance and regression coefficients, possibly at different dates. A commonly applied method is to first test for changes in the coefficients (or in the error variance) and, conditional on the break dates found, test for changes in the variance (or in the coefficients). In this note, we provide evidence that such procedures have poor finite sample properties when the changes in the first step are not correctly accounted for. In doing so, we show that testing for changes in the coefficients (or in the variance) ignoring changes in the variance (or in the coefficients) induces size distortions and loss of power. Our results illustrate a need for a joint approach to test for structural changes in both the coefficients and the variance of the errors. We provide some evidence that the procedures suggested by Perron et al. (2019) provide tests with good size and power. [ABSTRACT FROM AUTHOR]