This paper develops a new unit root test to allow **smooth** **breaks** in the deterministic components and asymmetric nonlinear adjustment. We also extend the Sollis (2009) asymmetric exponential **smooth** transition autoregressive (AESTAR) nonlinear unit root with **smooth** **breaks** by means of a Fourier function. The paper is to set out as follows. Section 2 introduces new unit root test and its construction of

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Following conflicting empirical results as reported in Table-1, this paper consequently contribute to the existing energy economics by two major folds: (i), This paper uses long historical data for the period of 1800-2008 in order to examine the unit root properties of renewable energy consumption for Canada, France, Germany, Italy, the Netherlands, Portugal, Spain, Sweden, and the UK 4 . (ii), This study also enriches by means of a unit root test which accounts potential sharp shifts and **smooth** **breaks** stemming in renewable energy consumption. It is documented that the persistence parameter of a process may be overestimated if structural **breaks** are omitted or disregarded from unit root analysis, subsequently decreasing the explanatory power to reject a unit root when the stationarity alternative is true (Perron, 1989). Therefore, we model **breaks** in our unit root testing process,

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This paper proposes new three unit root testing procedures which consider jointly for two structural **breaks** and nonlinear adjustment. The structural **breaks** are modelled by means of two logistic **smooth** transition functions and nonlinear adjustment is modelled by means of ESTAR models. In the new unit root testing procedures labelled HM-KSS, HM-Sollis and HM-Kruse, the null hypothesis of the unit root is tested against the alternative of nonlinear and stationary with two **smooth** **breaks**. We study the finite sample properties and the power of proposed tests with Monte Carlo simulations and find that the empirical sizes of three unit root tests are quite close to the nominal one. We also find that our HM-KSS unit root test is more powerful than the alternative tests which are considering only the two **smooth** **breaks** (HM) and only the nonlinear adjustment (KSS). Similarly, HM-Sollis has greater power than the tests of HM (2002), but the Sollis (2009) the Sollis (2009) unit root test performs better than the HM-Sollis test for model A and our HM-Kruse unit root test is superior over the HM (2002) and Kruse (2011) tests. In all cases the new HM-KSS, HM-Sollis and HM-Kruse unit root tests are more powerful than the alternative tests which consider only the two **smooth** **breaks** (HM) and only the nonlinear adjustment (KSS, Sollis and Kruse).

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The results of the ADF, PP, KSS, EG and Sollis unit root tests recommend that the null hypothesis of a unit root is rejected at the conventional significance levels. These results contradict the PPP hypothesis. On the other hand, our newly proposed test that allows for nonlinear adjustment towards LNV type trend function rejects the null hypothesis of a unit root at 1% significance level, which provides an evidence for the PPP hypothesis. This finding recommends that a model that allows for gradual structural **breaks** and nonlinear adjustment might be more suitable for the Argentinian RER series.

Despite increasing evidence in favor of the PPP hypothesis in recent years, support for PPP for the post-Bretton-Woods floating exchange rates period has so far been sparse. This is especially the case for US-dollar-based bilateral RERs. Given the lack of power of standard unit root tests, attempts to solve this problem have ranged from the use of historical datasets to the use of panel methods. In this paper, however, we focus on the potential effect that structural **breaks** and nonlinear mean reversion have on tests of the PPP hypothesis. We present tests that, far from considering these two features separately, model both **breaks** and nonlinear adjustment jointly. We argue that, even in the presence of temporary **breaks** in the mean of the RER, transaction costs or heterogeneous agents’ opinions can lead to a faster adjustment of the RER when it is far from its (possibly breaking) equilibrium.

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I generate series with **breaks** similar to the ones employed in Becker et al. (2004) and Clements and Hendry (1999). Thus, for T = 500, I simulate one break, two **breaks** and trend **breaks** both in the middle and towards the extremes. Cases for temporary, permanent and reinforcing **breaks** are considered. The appendix displays the results in panels 1 through 9. As in Enders and Lee (2006), Panels 1 and 2 display approximations for **breaks** towards the end of a series. In panel 3, the series has a temporary, though long-lasting break. Panels 4 and 5 display permanent **breaks** in opposite directions, while in Panel 6 the **breaks** are in the same direction. Finally, Panels 7-9 depict **breaks** in the intercept and slope of a trending series. I estimate the coefficients of the sinusoidal terms by performing a simple regression of y t on α(t) and a time trend.

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Therefore, it is our goal in this paper to do this, i.e., modify the standard quantile unit root test by incorporating sharp shifts and **smooth** **breaks** to determine if this helps to find support for the PPP in more countries. Indeed, when we apply the modified test to the real effective exchange rate of each of the 34 OECD countries, we validate the PPP if not in all but in more countries. The remainder of this work is organized as follows. Section II discusses the data used in our study. Section III first describes the quantile-based unit root test proposed by Koenker and Xiao (2004) and shows our modification to include sharp and **smooth** **breaks**. We then present the empirical results. Section IV concludes.

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Fanconi anemia (FA) is a rare disease, but it is the most common among the inherited bone marrow failure syndromes. In the present study, one family diagnosed with Fanconi anemia was examined. There was a 22-year-old female in this family who was diagnosed with both breast cancer and with Fanconi anemia. The chromosomal breakage in karyotyping was compatible with Fanconi anemia. Chromosomal analysis of mean **breaks** and rearrangements were calculated. The BRCA2*617delT/88delTG and the BRIP1 (c.2392C>T) mutations which are associated with Fanconi anemia and breast cancer were investigated. In order to, genomic DNA was extracted from blood samples collected from the case with both breast cancer and Fanconi anemia, from her brothers, her sisters and her parents, followed by Polymerase Chain Reactions to detect the BRCA2*617delT/88delTG and BRIP1 (c.2392C>T)mutations of exons 11 and17. In chromosomal analysis, four cases with a mean of 41.33 **breaks** and rearrangements (SEM of ±1.2) were observed in the culture of the prob and, yielding an average of 0.686 **breaks** per metaphase, while only an average of 0.03 **breaks** per metaphase was detected in the control group.The results of DNA sequencing and data analysis showed that there was no variation between the individuals in this family forBRCA2*617delT/88delTG and BRIP1 (c.2392C>T)mutations after alignment to the nucleotide sequences. Investigation of other mutations linked to the BRCA pathway such as FANCJ, BRCA1 and RAD51C/FANCO, or the use whole-exome sequencing for the further investigation of the disease are recommended.

G ENETIC recombination during meiosis is distin- the recombination-initiating events comes from bud- guished from mitotic recombination in several ding yeast (Sun et al. 1989; Cao et al. 1990), where such fundamental respects (Petes et al. 1991; Paques and **breaks** are generated by the activity of Spo11p, a meiosis- Haber 1999). First, the frequency of detectable recom- enriched protein related to archaebacterial type II topo- bination events between homologous chromosomes is isomerases (Klapholz et al. 1985; Bergerat et al. 1997; elevated by several orders of magnitude over mitotic Keeney et al. 1997). This mechanism for initiation of recombination frequencies. Second, meiotic recombi- meiotic recombination is conserved across kingdoms; nation events more frequently involve crossing over, or Dernburg et al. (1998) showed that meiotic recombina- reciprocal exchange, between the participating chroma- tion in Caenorhabditis elegans not only requires the nema- tids. These crossover recombination events are crucial tode SPO11 ortholog (spo-11), but also that artifically for generating chiasmata, physical connections between induced DNA **breaks** could bypass the requirement for homologous chromosomes that persist until the meta- spo-11. Spo11 homologs are likewise required for mei- phase/anaphase transition and allow the homologs to otic recombination in fission yeast and Drosophila, and orient toward opposite poles of the meiosis I spindle for formation of meiosis-induced DSBs recently identi- (Jones 1987; Hawley 1988). fied in fission yeast (Lin and Smith 1994; McKim et al. Crossover recombination during meiosis proceeds by 1998; McKim and Hayashi-Hagihara 1998; Cervan- a specialized double-strand break (DSB) repair pathway tes et al. 2000).

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ABstRACt: In 25 to 40-years-old stands damaged by snow in the Beskids, the fauna of cambioxylophages was analyzed both on standing **breaks** and lying break-off stems. **Breaks** are characterized by the gradually drying phloem, watered phloem and secondary fauna (Hylurgops palliatus, Hylocoetes dermestoides, Dryocoetes sp., Monochamus sp.), which does not represent any danger to spruce stands. The phloem on lying **breaks** withered and died till the end of the growing season. The competing species Pityogenes chalcographus (L.) (46–52% cov- er) and species of the genus Dryocoetes (20% cover) colonized the **breaks** in particular. The upper and the lower side of the lying **breaks**-off differ in the intensity of attack and the degree of cover of these species. In young broken and open stands with the unprocessed wood of lying **breaks**-off there occurs a risk of the creation of bark beetle circles in the subsequent year after the damage.

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Macroeconomists working with multivariate models typically face uncertainty over which (if any) of their variables have long run steady states which are subject to **breaks**. Furthermore, the nature of the break process is often unknown. In this paper, we draw on meth- ods from the Bayesian clustering literature to develop an econometric methodology which: i) …nds groups of variables which have the same number of **breaks**; and ii) determines the nature of the break process within each group. We present an application involving a …ve-variate steady-state VAR.

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sclerotomy was less likely to cause retinal **breaks** during surgery. However, a 20-gage system was only used in two cases in our study so we could not investigate this relation- ship. The relationship between gage size and the likelihood of a retinal break during MH surgery should be investigated in the future. Second, we cannot exclude the possibility that we did not identify all retinal **breaks** that occurred during MH surgery. We searched carefully for retinal **breaks** with scleral indentation intraoperatively, but may have missed small retinal tears. In the current study, there were not RD immediately after MH surgery, we need to observe for longer time to search the postoperative RD. Third, we could not assess the size of the MH. However, we do not believe that the size of the MH was related to the occurrence of intraop- erative retinal **breaks**; we consider that the size of the MH is more likely to be related to the duration of MH than to the area of the peripheral retina where the retinal break occurred

Alizadeh, Brandt [2] propose the use of two-factor stochastic volatility model to model range based volatility measures and find that the range based volatility models are highly efficient than the returns based counterparts. Chou [24] pro- poses the Conditional Autoregressive Range Model (CARR) to model the dy- namics in the range-based volatility measures. Chou [24] also provides the ex- tension of the CARR model which include the exogenous variables and named that model as CARRX model to predict volatilities. Chou [24] finds that the CARR model better predicts the volatility in comparison to the GARCH based models. Brandt and Jones [25] suggest the use of exponential generalized auto- regressive conditional heteroskedasticity (EGARCH) model with trading range to generate more accurate forecasts of the trading range. Chen, Gerlach [26] provide another extension of the CARR model which include the threshold con- ditional heteroskedastic autoregressive model. Chen, Gerlach [26] find the pres- ence of significant threshold non-linearity in the data. Chiang and Wang [27] propose the logarithm conditional autoregressive range based model with log- normal distribution to capture the **smooth** transition in the range process. Li and Hong [28] suggest the use of auto-regression model for range based volatility. Chan, Lam [29] propose the CARR model based on the geometric framework and named that model as the conditional autoregressive geometric process range (CARGPR) model to allows for flexible trend patterns, threshold effects, leverage effects, and long-memory dynamics in financial time series. Kumar and Maheswaran [12] propose the use of ARFIMA based model to generate forecasts based on AddRS estimator. All these studies imply the fact that the forecasts of volatility based on range based volatility estimator are more accurate when compared with the forecasts based on the returns based volatility models. Kumar [30] incorporate the impact of structural **breaks** in the CARR model while mod- elling and predicting the RS estimator. Kumar [31] incorporate the impact of structural **breaks** in modelling the Yang and Zhang [8] volatility estimator. In another study, Kumar [32] incorporate the impact of structural **breaks** in the CARR model to capture the dynamics of the range based volatility estimator.

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In the water pipeline management, water **breaks** are common issues which can be used as an indicator of the system’s performance. Those failures sometimes are regarded random behavior. In some cases, such assumption cannot be justified. This paper explores the arrival pattern of water pipeline **breaks**. Two main issues are ad- dressed. The first problem is the handling of incomplete data set. The 2-sample KS test is adopted to verify the similarities between data sets without indicating any underlying distribution. The reliability analysis is then conducted on all data. The results show that for the annual data set, a slightly increased rate is noticed for the break arrivals. For the entire data set, the 3-parameter Weibull generates a shape parameter well greater than 1, indicating an increased arrival rate. To eliminate the impacts of incomplete 2011 data set, data from 2012 to 2014 were treated as a new set. The analysis reveals that the shape parameter is a little greater than 1. Therefore, there is a slightly increased failure rate over this duration, which is consistent with the conclusions obtained from the annual data set analysis. In summary, results from this paper provide information to the water pipeline managers. In this specific case, more efforts are needed to lower the frequency of the water burst issues.

The analysis of cointegration in non-stationary panels has been recently rapidly expanding in two main directions. The ﬁ rst, urged by the nature of the data actually used in empirical applications, is the eﬀort to generalise the tests to the case of dependent units, either by modelling the dependence (inter alia, Gengenbach, Palm, Urbain, 2005) or reproducing it through the bootstrap (Fachin, 2006, Westerlund and Edgerton, 2006). The second direction follows steps already taken by the cointegration literature in the early ’90’s, tackling the issues of testing (i) cointegration allowing for **breaks** and (ii) the stability of a cointegrating relationship. In this stream of the literature, the ﬁrst problem seems to have received more attention (e.g., Banerjee and Carrion-i-Silvestre, 2004 and 2006, Gutierrez, 2005, Wester- lund, 2006) than the second (to the best of our knowledge, only Emerson and Kao, 2001, 2005, for trend regressions, Kao and Chiang, 2000, for ho- mogenous panel regressions). This is somehow surprising, as stability tests with unknown break points may have very low power with even medium sample sizes. For instance, the rejection rates under H 1 simulated by Gre-

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We would expect that allowing for the possibility of two endogenous break points would provide further evidence against the unit root hypothesis (Lumsdaine and Papell, 1997; Ben-David et al., 2003; Maddala and Kim,2003). However, the percentage of unit root rejected at the 10% is lower than for the one break test (see Table 2). For ten of the sixteen countries, results suggest that any sudden shock has permanent effects. Table 4 reports results for each individual country. The structural **breaks** are all positive and significant. For Denmark, France, Sweden and the United Kingdom, results indicate that divorce may be characterized as being stationary around a mean, which changes in the 1940s and in the 1970s. For two countries, Belgium and Switzerland, evidence suggests that introducing the possibility of two structural **breaks** results in the divorce rate series being identified as stationary around two **breaks** in the mean level, in the 1970s and again in the late 1980s. Intriguingly, in the case of Iceland, Germany and the Netherlands, we conclude that these series are not stationary when allowing for double structural break, but the unit root null hypothesis can be rejected in favour of a stationary divorce rate with just one-time break. All in all, these findings seem to confirm that there is no single scenario for all divorce rate series.

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example, early work by Quandt (1960) suggests using a supremum (sup) type test for inference on a single unknown break-point. Whether in linear or nonlinear set- tings, most subsequent work - see inter alia Anderson and Mizon (1983), Andrews and Fair (1988), Ghysels and Hall (1990), Andrews (1993), Sowell (1996), Hall and Sen (1999) and Andrews (2003) - proposes tests that are designed against the alternative of a one-time parameter variation or of more general model misspeci- fication. For parametric settings, Bai and Perron (1998) is among the few papers that propose tests for identifying multiple **breaks**. Their tests are designed for linear models estimated via ordinary least-squares (OLS). While these tests are useful, the linear framework might be considered a limitation. Subsequent papers such as Kokoszka and Leipus (2000), Lavielle and Moulines (2000) and Andreou and Ghysels (2002) propose tests for parameter instability in nonlinear models, but the nonlinearities considered are confined to special cases such as general autoregressive conditional heteroskedasticity (GARCH) models. The framework considered in this paper is more general, imposing only mild restrictions on the nonlinear regression function.

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Most research on the transfer of hog prices in China have employed relatively simple methods, including applications of the vector autoregression (VAR) model (such as in Ma et al. 2007; He and Fang 2012; Zhang et al. 2014 and Cong and Xiahua 2015) and threshold models (such as in Hu and Wang 2010; Li et al. 2012b and Dong 2015). Although tradi- tional threshold models can depict the asymmetric and nonlinear characteristics of the transforma- tion of price variables in different mechanisms, the transformations have jump characteristics. In 1994, Teräsvirta noted that transformation among different mechanisms might be continuous instead of jumping for many economic time series. Therefore, we have taken advantage of previous studies and explored the asymmetric effects of corn price fluctuations on hog price fluctuations using **smooth** transition regression (STR) models. STR models can describe nonlinear characteristics more accurately than the TAR model (Mao and Zeng 2009).

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Almost all of the empirical studies that use the time series techniques use unit root tests. During the last four decades, an increasing number of studies have developed tests to analyze the order of integration of variables. Unit root tests were first introduced to literature by Dickey and Fuller (1979). The change in general in the testing concept was introduced by Perron (1989). According to Perron (1989), traditional unit root tests will display a tendency not to be stationary in the case of a structural break. After Becker et. al. (2006), the flexible Fourier transformation is used quite frequently in modeling structural **breaks** in recent years. The main advantage of this approach is that it eliminates the need to determine the number and the type of structural **breaks**.

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the stationarity throughout all countries. Therefore, if the null hypothesis of the CBL test is rejected, then we say that all of the series in the panel are non-stationary. Second, the CBL method enables us to consider multiple structural **breaks** positioned at different unknown dates in addition to a different number of **breaks** for each individual. Allowing the existence of structural **breaks** can potentially strengthen our results more correctly in respect of specifying the model. Third and finally, we can allow for more general forms of cross-sectional correlation than previous studies through the conventional cross- sectional demeaning of the data, which assumes that a common factor affects all units with the same intensity. Carrion-i-Silvestre and German-Soto (2009) also indicate that the lack of consideration of the cross-sectional dependence might bias the analysis to conclude in favor of the stationarity of the panel data even in the case that it is non- stationary. It is important to note that the panel stationarity test controls non-parametrically for serial correlation in the error through the estimation of the long-run variance via kernels. In our study, we employ the bootstrap distribution, tailored to the error structure of panel data, in order to accommodate general forms of cross-sectional dependence.

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