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This paper considers estimation and inference about tail features when the observations beyond some threshold are censored. We first show that ignoring such tail censoring could lead to substantial bias and size distortion, even if the censored probability is tiny. Second, we propose a new maximum likelihood estimator (MLE) based on the Pareto tail approximation and derive its asymptotic properties. Third, we provide a small sample modification to the MLE by resorting to Extreme Value theory. The MLE with this modification delivers excellent small sample performance, as shown by Monte Carlo simulations. We illustrate its empirical relevance by estimating (i) the tail index and the extreme quantiles of the US individual earnings with the Current Population Survey dataset and (ii) the tail index of the distribution of macroeconomic disasters and the coefficient of risk aversion using the dataset collected by Barro and Urs{\'u}a (2008). Our new empirical findings are substantially different from the existing literature.

Economic and financial time series data can exhibit nonstationary and nonlinear patterns simultaneously. This paper studies copula-based time series models that capture both patterns. We introduce a procedure where nonstationarity is removed via a filtration, and then the nonlinear temporal dependence in the filtered data is captured via a flexible Markov copula. We propose two estimators of the copula dependence parameters: the parametric (two-step) copula estimator where the marginal distribution of the filtered series is estimated parametrically; and the semiparametric (two-step) copula estimator where the marginal distribution is estimated via a rescaled empirical distribution of the filtered series. We show that the limiting distribution of the parametric copula estimator depends on the nonstationary filtration and the parametric marginal distribution estimation, and may be non-normal. Surprisingly, the limiting distribution of the semiparametric copula estimator using the filtered data is shown to be the same as that without nonstationary filtration, which is normal and free of marginal distribution specification. The simple and robust properties of the semiparametric copula estimators extend to models with misspecified copulas, and facilitate statistical inferences, such as hypothesis testing and model selection tests, on semiparametric copula-based dynamic models in the presence of nonstationarity. Monte Carlo studies and real data applications are presented.

The right tail of the distribution of financial variables provides important information to investors and decision-makers. In this paper, we study the role of the right tail distributional informat...

Summary In the literature, a discrepancy in the limiting distributions of least square estimators between the stationary and nonstationary cases exists in various regression models with different persistence level regressors. This hinders further statistical inference since one has to decide which distribution should be used next. In this paper, we develop a semiparametric partially linear regression model with stationary and nonstationary regressors to attenuate this difficulty, and propose a unifying inference procedure for the coefficients. To be specific, we propose a profile weighted estimation equation method that facilitates the unifying inference. The proposed method is applied to the predictive regressions of stock returns, and an empirical likelihood procedure is developed to test the predictability. It is shown that the Wilks theorem holds for the empirical likelihood ratio regardless of predictors being stationary or not, which provides a unifying method for constructing confidence regions of the coefficients of state variables. Simulations show that the proposed method works well and has favourable finite sample performance over some existing approaches. An empirical application examining the predictability of equity returns highlights the value of our methodology.

This note re-investigates the smooth tests for the equality of distributions introduced by Bera et al. (2013, Econometric Theory 29, 419–446) and provides a modified smooth test which works for the general case with two sample sizes m and n. Asymptotic properties of the proposed test statistic under both the null and the alternative hypothesis are studied.

This collection of two issues of Econometric Reviews is the Reviews’ modest way to participate in honoring the pathbreaking contributions and leadership of Peter C. B. Phillips in the sciences, gen...

Model averaging for forecasting and mixed estimation is a recognized improved statistical approach. This paper is a first report on: (1). aggregate information from different conditional quantiles ...

A catalytic asymmetric conjugate addition/Schmidt-type rearrangement of vinyl azides and (E)-alkenyloxindoles was realized. It afforded a variety of optically active 3,2′-pyrrolinyl spirooxindoles with high yields (up to 98%), and excellent diastereo- and enantioselectivities (up to 98% ee, >19 : 1 dr), even at the gram-scale in the presence of a chiral N,N′-dioxide–nickel(II) complex. In addition, a possible catalytic cycle and transition state model were proposed to rationalize the stereoselectivity.

This article proposes a consistent nonparametric test for common trend specifications in panel data models with fixed effects. The test is general enough to allow for heteroscedasticity, cross-sectional and serial dependence in the error components, has an asymptotically normal distribution under the null hypothesis of correct trend specification, and is consistent against various alternatives that deviate from the null. In addition, the test has an asymptotic unit power against two classes of local alternatives approaching the null at different rates. We also propose a wild bootstrap procedure to better approximate the finite sample null distribution of the test statistic. Simulation results show that the proposed test implemented with bootstrap p-values performs reasonably well in finite samples. Finally, an empirical application to the analysis of the U.S. per capita personal income trend highlights the usefulness of our test in real datasets.

This paper develops bootstrap methods for practical statistical inference in panel data quantile regression models with fixed effects. We consider random-weighted bootstrap resampling and formally establish its validity for asymptotic inference. The bootstrap algorithm is simple to implement in practice by using a weighted quantile regression estimation for fixed effects panel data. We provide results under conditions that allow for temporal dependence of observations within individuals, thus encompassing a large class of possible empirical applications. Monte Carlo simulations provide numerical evidence the proposed bootstrap methods have correct finite sample properties. Finally, we provide an empirical illustration using the environmental Kuznets curve.

A catalytic asymmetric conjugate addition/Schmidt-type rearrangement of vinyl azides and (E)-alkenyloxindoles was realized. It afforded a variety of optically active 3,2′-pyrrolinyl spirooxindoles with high yields (up to 98%), and excellent diastereo- and enantioselectivities (up to 98% ee, >19 : 1 dr), even at the gram-scale in the presence of a chiral N,N′-dioxide–nickel(ii) complex. In addition, a possible catalytic cycle and transition state model were proposed to rationalize the stereoselectivity., Lewis acid catalyzed asymmetric synthesis of 3,2′-pyrrolinyl spirooxindole skeletons via conjugate addition/Schmidt-type rearrangement of vinyl azides and (E)-alkenyloxindoles.

Corporate diversification is one of the most debated topics in finance over the past two decades. While it is widely believed that there exists a discount in the stock market valuation of conglomerate firms, the extant research based on least squares methods points to different directions. We argue that the existing empirical analyses ignore some important data features, especially cross sectional heterogeneity, predicted by both theories and casual observations on corporate diversification, and thus cannot provide a complete picture of the diversification discount. Using a quantile regression analysis on U.S. public firms, we investigate the importance of heterogeneity of diversification as well as other firm characteristics. Estimated quantile treatment effects exhibit substantial heterogeneity as predicted. Thus mean impacts miss a great deal. We also tie back differences in the effect of diversification in high-valued and low-valued firms to observable agency characteristics; the most interesting finding is that CEOs seem to play vastly different roles in high-valued and low-valued firms. We conclude that the effect of diversification is likely more varied and more extensive than has been recognized.

A simple apparatus was designed to effectively isolate magnetotactic bacteria from soils or sediments based on their magnetotaxis. Through a series of processes including sample incubation, MTB harvesting, isolation, purification and identification, several strains of bacteria were isolated from the samples successfully. By Transmission Electron Microscopy (TEM) and Energy-Dispersive X-ray Analysis (EDXA), these bacteria were certificated to be magnetotactic bacteria. The phylogenetic relationship between the isolated magnetic strains and some known magnetotactic bacteria was inferred by the construction of phylogenetic tree based on 16SrDNA sequences. This apparatus has been proven to have the advantages of being inexpensive, simple to assemble, easy to perform and highly efficient to isolate novel magnetotactic bacteria. The research indicated that the combined approach of harvesting MTB by home-made apparatus and the method of plate colony isolation could purify and isolate magnetotactic bacteria effectively.

Background: Lipoprotein lipase (LPL) is a glycoprotein enzyme playing a pivotal role in energy and lipoprotein metabolism. It hydrolyzes triglyceride-rich lipoprotein and produces fatty acids as well as monoacylglycerol. Multiple risk factors including age, sex, hypertension, smoking and hyperlipidemia contribute to ischemic stroke (IS). Hyperlipidemia is an extremely pivotal risk factor for ischemic stroke especially atherothrombotic stroke (ATS) in that it can lead to intracranial or extracranial vessels’ atherosclerosis analogous to atherosclerosis of coronary artery. However, potential epigenetic mechanisms contributing to IS are not been explored thoroughly. The aim of this study was to illuminate relationship among individuals’ peripheral blood leukocytes methylation level of LPL-promoter-CpG dinucleotides, serum lipid and ATS of Chinese Han population in Hunan Province. Methods: Peripheral blood samples were collected from acute atherothrombotic stroke patients and healthy people, and methylation level of cytosine–phosphate–guanine (CpG) island in LPL promoter region was measured by qPCR and pyrosequencing. Biochemical and anthropometric variables have also been taken into consideration.Results: Integral LPL-promoter-CpG dinucleotides methylation level of case group is evidently higher than control group (38.8±8.4% percentile vs 25.7±6.6% percentile, P value=0.000). DNA methylation at the CpG9~16 locus and CpG20~21 locus was related to ATS after adjusting for gender, previous history of diabetes and hypertension, smoking, drinking, body mass index, and blood lipid levels(CpG9 49.3±24.9, 31.3±13.6, P value =0.02; CpG10 61.3±24.5, 34.0±18.4, P value =0.002; CpG11 71.3±17.3, 32.0±21.1, P value =0.000; CpG12 75.3±17.7, 44.7±19.6, P value =0.000; CpG13 72.0±20.4, 50.0±25.4, P value =0.014; CpG14 63.3±18.0, 45.3±22.0, P value =0.021; CpG15 56.7±13.5, 34.7±14.6, P value =0.000; CpG16 50.0±12.5, 31.3±20.0, P value =0.005; CpG20 52.0±13.2, 27.3±20.9, P value =0.001; CpG21 55.3±11.9, 34.0±22.3, P value =0.004). There is no statistically significant connection between either carotid atherosclerosis or gender and methylation level. Besides, we found a negative association between LPL methylation status and HDL-C levels, whereas LPL gene methylation was linked with LDL-C levels and age positively.Conclusion: We illustrate that epigenetic modification of LPL methylation may have an influence on lipids metabolism and occurrence of ATS, which may be a promising indicator for occurrence risk of ischemic stroke. Whether these findings are valid need further warrant and future prospective.

Stroke-associated pneumonia (SAP) is a spectrum of pulmonary infections in non-mechanical ventilation patients within 7 days of stroke. SAP is one of the most common complications after stroke, with an incidence of 7%-38%, which is significantly associated with poor prognosis of stroke. Stroke-induced immune-depression syndrome (SIDS) is one of the main pathogenesis of SAP, which is closely related to autoimmune, sympathetic nervous system (SNS), hypothalamic-pituitary-adrenalin axis (HPA axis), parasympathetic nervous system (PNS), and damage-related molecular patterns (DAMPs). It is unclear how the lungs and brain interact during the development of SAP. Some clinical studies have found that some clinical indicators such as monocyte human leukocyte antigen-DR (mHLA-DR), neutrophil to lymphocyte ratio (NLR) and heart rate variability (HRV) can be used to predict SAP occurrence. Old age, male, and diabetes are currently considered risk factors for SAP. Furthermore, a variety of SAP risk scales such as A2DS2 scale (age, atrial fibrillation, dysphagia, gender and stroke severity), preventive antibacterial therapy in acute ischemic stroke (PANTHERIS) scale, acute ischemic stroke-associated pneumonia scale (AIS-APS), and ISAN scale (pre-stroke independence, gender, age, and stroke severity) have been developed. According to the opinion of Pneumonia in Stroke Consensus in 2015, it is recommended to use the modified Centers for Disease Control and Prevention (CDC) pneumonia clinical diagnostic criteria for the diagnosis of SAP. Prevention of SAP is the most important part of clinical practice. Preventive antibiotics are not recommended, and once SAP is diagnosed, the antibiotic strategies should be followed. Neuroprotective and anti-inflammatory treatments are still being studied.

In this paper, we propose a consistent U‐statistic test with good sampling properties to detect changes in volatility. We show that the test has a limiting standard normal distribution under the null hypothesis, and that it is powerful compared with various alternatives. A Monte Carlo experiment is conducted to highlight the merits of the proposed test relative to other popular tests for structural changes in volatility. An empirical example is examined to demonstrate the practical application of the proposed testing method.

For estimation of time-varying coefficient longitudinal models, the widely used local least-squares (LS) or covariance-weighted local LS smoothing uses information from the local sample average. Mo...

We propose a non-parametric test for trend specification with improved properties. Many existing tests in the literature exhibit non-monotonic power. To deal with this problem, Juhl and Xiao 2005 proposed a non-parametric test with good power by detrending the data non-parametrically. However, their test is developed for smooth changing trends and is constructed under the assumption of correct specification in the dynamics. In addition, their test suffers from size distortion in finite samples and imposes restrictive assumptions on the variance structure. The current article tries to address these issues. First, the proposed test allows for both abrupt breaks and smooth structural changes in deterministic trends. Second, the test employs a sieve approach to avoid the misspecification problem. Third, the extended test can be applied to the data with conditional heteroskedasticity and time-varying variance. Fourth, the power properties under alternatives are also investigated. Finally, a partial plug-in method is proposed to alleviate size distortion. Monte Carlo simulations show that the new test not only has good size but also has monotonic power in finite samples.

We study the square†root LASSO method for high†dimensional sparse linear models with weakly dependent errors. The asymptotic and non†asymptotic bounds for the estimation errors are derived. Our results cover a wide range of weakly dependent errors, including I±â€ mixing, I †mixing, I•â€ mixing, and m†dependent types. Numerical simulations are conducted to show the consistency property of square†root LASSO. An empirical application to financial data highlights the importance of the results and method.

Tests for stochastic dominance constructed by translating the inference problem of stochastic dominance into parameter restrictions in quantile regressions are proposed. They are variants of the one-sided Kolmogorov–Smirnoff statistic with a limiting distribution of the standard Brownian bridge. The procedure to obtain the critical values of our proposed test statistics are provided. Simulation results show their superior size and power. They are applied to the NASDAQ 100 and S&P 500 indices to investigate dominance relationship before and after major turning points. Results show no arbitrage opportunity between the bear and bull markets. Our results infer that markets are inefficient and risk averters are better off investing in the bull rather than the bear market.

Forecasts are pervasive in all areas of applications in business and daily life. Hence evaluating the accuracy of a forecast is important for both the generators and consumers of forecasts. There are two aspects in forecast evaluation: (a) measuring the accuracy of past forecasts using some summary statistics, and (b) testing the optimality properties of the forecasts through some diagnostic tests. On measuring the accuracy of a past forecast, this paper illustrates that the summary statistics used should match the loss function that was used to generate the forecast. If there is strong evidence that an asymmetric loss function has been used in the generation of a forecast, then a summary statistic that corresponds to that asymmetric loss function should be used in assessing the accuracy of the forecast instead of the popular root mean square error or mean absolute error. On testing the optimality of the forecasts, it is demonstrated how the quantile regressions set in the prediction–realization framework of Mincer and Zarnowitz (in J. Mincer (Ed.), Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance (pp. 14–20), 1969) can be used to recover the unknown parameter that controls the potentially asymmetric loss function used in generating the past forecasts. Finally, the prediction–realization framework is applied to the Federal Reserve's economic growth forecast and forecast sharing in a PC manufacturing supply chain. It is found that the Federal Reserve values overprediction approximately 1.5 times more costly than underprediction. It is also found that the PC manufacturer weighs positive forecast errors (under forecasts) about four times as costly as negative forecast errors (over forecasts).

We present a new command, qctest, to implement tests for normality of a random variable based on the quantile-mean covariance. The test procedures are based on recent results by Bera et al. (2016, Econometric Theory 32: 1216–1252) and are an efficient alternative to existing normality tests in the literature.

In the Android system security issue, the maliciousness of the applications is closely related to the permissions they applied. In this paper, a population-based model is proposed for detecting Android malicious application. Which is in the view of the current disadvantages of missing report, long detection period caused by features redundancy, and the instability of detection rate lead by unbalanced data of benign and malicious samples. Drawing on the idea of population in biology, each app was labeled by preprocessing. And adaptive feature vectors were automatically selected through the feature engineering. Thus the malicious application detection is carried out in the form of hybrid model voting. The experimental results show that feature engineering can remove a large amount of redundancy before classification. And the hybrid voting model can provide adaptive detection service for different populations.

We study, via quantile regression, time series models whose conditional distribution may change over different quantile range of a threshold variable. We derive the limiting distribution of the estimated threshold parameter under the frameworks of asymptotically shrinking and fixed regime change magnitude. We construct confidence intervals for the estimated threshold parameter via a likelihood-ratio-type statistic and tabulate critical values, and by extensive simulation, we investigate their coverage probabilities. We also derive the Bahadur representation allowing for serially correlated errors and discuss related inference problems on threshold effects. Our asymptotic and simulation results complement the existing literature of Caner (2002), Galvao et al (2011, 2014) on threshold regression models.