Semi-parametric seasonal unit root tests

We extend the M class of unit root tests introduced by Stock (1999, Cointegration, Causality and Forecasting. A Festschrift in Honour of Clive W.J. Granger. Oxford University Press), Perron and Ng (1996, Review of Economic Studies 63, 435–463) and Ng and Perron (2001, Econometrica 69, 1519–1554) to the seasonal case, thereby developing semi-parametric alternatives to the regression-based augmented seasonal unit root tests of Hylleberg, Engle, Granger, and Yoo (1990, Journal of Econometrics 44, 215–238). The success of this class of unit root tests to deliver good finite sample size control even in the most problematic (near-cancellation) case where the shocks contain a strong negative moving average component is shown to carry over to the seasonal case as is the superior size/power trade-off offered by these tests relative to other available tests

Testing for Episodic Predictability in Stock Returns

Standard tests based on predictive regressions estimated over the full available sample data have tended to find little evidence of predictability in stock returns. Recent approaches based on the analysis of subsamples of the data have been considered, suggesting that predictability where it occurs might exist only within so-called 'pockets of predictability' rather than across the entire sample. However, these methods are prone to the criticism that the sub-sample dates are endogenously determined such that the use of standard critical values appropriate for full sample tests will result in incorrectly sized tests leading to spurious findings of stock returns predictability. To avoid the problem of endogenously-determined sample splits, we propose new tests derived from sequences of predictability statistics systematically calculated over sub-samples of the data. Specifically, we will base tests on the maximum of such statistics from sequences of forward and backward recursive, rolling, and double-recursive predictive sub-sample regressions. We develop our approach using the over-identified instrumental variable-based predictability test statistics of Breitung and Demetrescu (2015). This approach is based on partial-sum asymptotics and so, unlike many other popular approaches including, for example, those based on Bonferroni corrections, can be readily adapted to implementation over sequences of subsamples. We show that the limiting distributions of our proposed tests are robust to both the degree of persistence and endogeneity of the regressors in the predictive regression, but not to any heteroskedasticity present even if the sub-sample statistics are based on heteroskedasticity-robust standard errors. We therefore develop fixed regressor wild bootstrap implementations of the tests which we demonstrate to be first-order asymptotically valid. Finite sample behaviour against a variety of temporarily predictable processes is considered. An empirical application to US stock returns illustrates the usefulness of the new predictability testing methods we propose

Testing for Episodic Predictability in Stock Returns

Standard tests based on predictive regressions estimated over the full available sample data have tended to find little evidence of predictability in stock returns. Recent approaches based on the analysis of subsamples of the data suggest in fact that predictability where it occurs might exist only within so-called \pockets of predictability" rather than across the entire sample. However, these methods are prone to the criticism that the subsample dates are endogenously determined such that the use of standard critical values appropriate for full sample tests will result in incorrectly sized tests leading to spurious findings of stock returns predictability. To avoid the problem of endogenously-determined sample splits, we propose new tests derived from sequences of predictability statistics systematically calculated over subsamples of the data. Specifically, we will base tests on the maximum of such statistics from sequences of forward and backward recursive, rolling, and double-recursive predictive subsample regressions. We develop our approach using the over-identified instrumental variable-based predictability test statistics of Breitung and Demetrescu (2015). This approach is based on partial-sum asymptotics and so, unlike many other popular approaches including, for example, those based on Bonferroni corrections, can be readily adapted to implementation over sequences of subsamples. We show that the limiting null distributions of our proposed test statistics depend in general on whether the putative predictor is strongly or weakly persistent and on any heteroskedasticity present (indeed on any timevariation present in the unconditional variance matrix of the innovations), the latter even if the subsample statistics are based on heteroskedasticity-robust standard errors. As a consequence, we develop fixed regressor wild bootstrap implementations of the tests which we demonstrate to be first-order asymptotically valid. Finite sample behaviour against a variety of temporarily predictable processes is considered. An empirical application to US stock returns illustrates the usefulness of the new predictability testing methods we propose

Natural parasitism of the Citrus Leafminer (Lepidoptera: Gracillariidae) over eight years in seven citrus regions of São Paulo, Brazil

The citrus leafminer (CLM) Phyllocnists citrella Stainton (Lepidoptera: Gracillariidae) was frst recorded in Brazil in 1996. In 1998, the parasitoid Ageniaspis citricola Logvinovskaya (Hymenoptera: Encyrtdae) was introduced and established in many regions of the country. In this study, 130 onehour-samplings of sweet orange leaves (Citrus sinensis [L.] Osbeck) hostng CLM pupal chambers were carried out to estmate the CLM parasitsm rate (%) by its parasitoids in 7 regions of São Paulo State between 2000 and 2008. The sample sizes varied from 10 to 275 leaves (mean = 65). The most abundant parasitoid was the encyrtd A. citricola (found in 91.8% of the samplings). The highest level of CLM parasitsm by A. citricola was recorded in the southern region (Botucatu), 70.2 ± 6.6 (mean ± SEM), and the lowest level was recorded in the northern region (Barretos), 12.8 ± 5.7%. CLM parasitsm by A. citricola and by natve parasitoids (Galeopsomyia fausta LaSalle, Cirrospilus spp. and Elasmus sp.) did not differ between seasons. The 6-fold increase in the use of insectcides in citrus groves, afer 2004 when the Huanglongbing (HLB) disease was found in São Paulo State, did not reduce the level of CLM parasitsm. The level of parasitsm was 50.8 ± 4.2% before the advent of HLB (2000–2004) and 56.0 ± 4.4% thereafer (2005–2008), indicatng adaptaton of A. citricola in a disturbed agroecosystem.A minadora das folhas dos citros (MFC), Phyllocnistis citrella Stainton (Lepidoptera: Gracillariidae), foi encontrada pela primeira vez no Brasil em 1996. Em 1998, o parasitoide Ageniaspis citricola Logvinovskaya (Hymenoptera: Encyrtidae) foi introduzido e se estabeleceu em várias regiões do país. Nesse estudo, foram feitas130 amostragens, de uma hora, de folhas de laranjeiras doces [Citrus sinensis (L.) Osbeck] com câmaras pupais da MFC, para se estimar o parasitismo da MFC em 7 regiões do estado de São Paulo, entre 2000 e 2008. O tamanho das amostras variou de 10 a 275 folhas (média = 65). O parasitoide mais abundante foi o encirtídeo A. citricola (encontrado em 91.8% das amostragens). O maior parasitismo da MFC por A. citricola foi observado na região sul do estado (Botucatu), 70,2 ± 6,6 (média ± EPM), e o menor parasitismo na região norte (Barretos), 12,8 ± 5,7%. O parasitismo da MFC por A. citricola e seus parasitoides nativos (Galeopsomyia fausta LaSalle, Cirrospilus spp. and Elasmus sp.) não diferiram entre as estações do ano. O aumento de seis vezes no uso de inseticidas nos pomares de citros, após 2004, quando o Huanglongbing (HLB) foi encontrado no estado de São Paulo, não reduziu o nível de parasitismo da MFC. O nível médio de parasitismo foi de 50,8 ± 4,2%, antes do HLB (2000-2004), e 56,0 ± 4,4%, após o HLB (2005-2008), indicando a adaptação de A. citricola a um agroecossistema perturbado.info:eu-repo/semantics/publishedVersio

Brain MRI in a patient with classical galactosemia: acute event of unilateral hemispheric cerebral edema

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Metabolic modelling of polyhydroxyalkanoate copolymers production by mixed microbial cultures

Background: This paper presents a metabolic model describing the production of polyhydroxyalkanoate (PHA) copolymers in mixed microbial cultures, using mixtures of acetic and propionic acid as carbon source material. Material and energetic balances were established on the basis of previously elucidated metabolic pathways. Equations were derived for the theoretical yields for cell growth and PHA production on mixtures of acetic and propionic acid as functions of the oxidative phosphorylation efficiency, P/O ratio. The oxidative phosphorylation efficiency was estimated from rate measurements, which in turn allowed the estimation of the theoretical yield coefficients. Results: The model was validated with experimental data collected in a sequencing batch reactor (SBR) operated under varying feeding conditions: feeding of acetic and propionic acid separately (control experiments), and the feeding of acetic and propionic acid simultaneously. Two different feast and famine culture enrichment strategies were studied: (i) either with acetate or (ii) with propionate as carbon source material. Metabolic flux analysis (MFA) was performed for the different feeding conditions and culture enrichment strategies. Flux balance analysis (FBA) was used to calculate optimal feeding scenarios for high quality PHA polymers production, where it was found that a suitable polymer would be obtained when acetate is fed in excess and the feeding rate of propionate is limited to ∼0.17 C-mol/ (C-mol.h). The results were compared with published pure culture metabolic studies. Conclusion: Acetate was more conducive toward the enrichment of a microbial culture with higher PHA storage fluxes and yields as compared to propionate. The P/O ratio was not only influenced by the selected microbial culture, but also by the carbon substrate fed to each culture, where higher P/O ratio values were consistently observed for acetate than propionate. MFA studies suggest that when mixtures of acetate and propionate are fed to the cultures, the catabolic activity is primarily guaranteed through acetate uptake, and the characteristic P/O ratio of acetate prevails over that of propionate. This study suggests that the PHA production process by mixed microbial cultures has the potential to be comparable or even more favourable than pure cultures.publishersversionpublishe

Transformed Regression-based Long-Horizon Predictability Tests

We propose new tests for long-horizon predictability based on IVX estimation (see Kostakis et al., 2015) of transformed regressions. These explicitly account for the over-lapping nature of the dependent variable which features in a long-horizon predictive regression arising from temporal aggregation. Because we use IVX estimation we can also incorporate the residual augmentation approach recently used in the context of short-horizon predictability testing by Demetrescu and Rodrigues (2020) to improve efficiency. Our proposed tests have a number of advantages for practical use. First, they are simple to compute making them more appealing for empirical work than, in particular, the Bonferroni-based methods developed in, among others, Valkanov (2003) and Hjalmarsson (2011), which require the computation of confidence intervals for the autoregressive parameter characterising the predictor. Second, unlike some of the available tests, they allow the practitioner to remain ambivalent as to whether the predictor is strongly or weakly persistent. Third, the tests are valid under considerably weaker assumptions on the innovations than extant long-horizon predictability tests. In particular, we allow for quite general forms of conditional and unconditional heteroskedasticity in the innovations, neither of which are tied to a parametric model. Fourth, our proposed tests can be easily implemented as either one or two-sided hypotheses tests, unlike the Bonferroni-based methods which require the computation of different confidence intervals for the autoregressive parameter depending on whether left or right tailed tests are to be conducted (see Hjalmarsson, 2011). Finally our approach is straightforwardly generalisable to a multi-predictor context. Monte Carlo analysis suggests that our preferred test displays improved finite properties compared to the leading tests available in the literature. We also report an empirical application of the methods we develop to investigate the potential predictive power of real exchange rates for predicting nominal exchange rates and inflation

Enabling PAT in insect cell bioprocesses: A monitoring toolbox for rAAV production

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Multivariate Fractional Integration Tests allowing for Conditional Heteroskedasticity with an Application to Return Volatility and Trading Volume

We introduce a new joint test for the order of fractional integration of a multivariate fractionally integrated vector autoregressive [FIVAR] time series based on applying the Lagrange multiplier principle to a feasible generalised least squares estimate of the FIVAR model obtained under the null hypothesis. A key feature of the test we propose is that it is constructed using a heteroskedasticity-robust estimate of the variance matrix. As a result, the test has a standard x² limiting null distribution under considerably weaker conditions on the innovations than are permitted in the extant literature. Specifically, we allow the innovations driving the FIVAR model to follow a vector martingale difference sequence allowing for both serial and cross-sectional dependence in the conditional second-order moments. We also do not constrain the order of fractional integration of each element of the series to lie in a particular region, thereby allowing for both stationary and non-stationary dynamics, nor do we assume any particular distribution for the innovations. A Monte Carlo study demonstrates that our proposed tests avoid the large over-sizing problems seen with extant tests when conditional heteroskedasticity is present in the data. We report an empirical case study for a sample of major U.S. stocks investigating the order of fractional integration in trading volume and different measures of volatility in returns, including realized variance. Our results suggest that both return volatility and trading volume are fractionally integrated, but with the former generally found to be more persistent (having a higher fractional exponent) than the latter, when more reliable proxies for volatility such as the range or realized variance are used

Extensions to IVX Methods of Inference for Return Predictability

Predictive regression methods are widely used to examine the predictability of (excess) returns on stocks and other equities by lagged macroeconomic and financial variables. Extended IV [IVX] estimation and inference has proved a particularly valuable tool in this endeavour as it allows for possibly strongly persistent and endogenous regressors. This paper makes three distinct contributions to the literature. First we demonstrate that, provided either a suitable bootstrap implementation is employed or heteroskedasticity-consistent standard errors are used, the IVX-based predictability tests of Kostakis et al. (2015) retain asymptotically pivotal inference, regardless of the degree of persistence or endogeneity of the (putative) predictor, under considerably weaker assumptions on the innovations than are required by Kostakis et al. (2015) in their analysis. In particular, we allow for quite general forms of conditional and unconditional heteroskedasticity in the innovations, neither of which are tied to a parametric model. Second, and associatedly, we develop asymptotically valid bootstrap implementations of the IVX tests under these conditions. Monte Carlo simulations show that the bootstrap methods we propose can deliver considerably more accurate finite sample inference than the asymptotic implementation of these tests used in Kostakis et al. (2015) under certain problematic parameter constellations, most notably for their implementation against one-sided alternatives, and where multiple predictors are included. Third, under the same conditions as we consider for the fullsample tests, we show how sub-sample implementations of the IVX approach, coupled with a suitable bootstrap, can be used to develop asymptotically valid one-sided and two-sided tests for the presence of temporary windows of predictability