Grass-endophytes in plant protection

Agriculturists have long known that certain grass species infected with systemic fungal endophytes are highly resistant to both vertebrate and invertebrate herbivores

Asexual grass endophyte symbiosis - mutual exploitation or reciprocal cooperation?

Asexual endophyte-grass associations are generally viewed as the epitome of specialized mutualism because of reciprocal benefits to the partners

Noncausal autoregressions for economic time series

This paper is concerned with univariate noncausal autoregressive models and their potential usefulness in economic applications. In these models, future errors are predictable, indicating that they can be used to empirically approach rational expectations models with nonfundamental solutions. In the previous theoretical literature, nonfundamental solutions have typically been represented by noninvertible moving average models. However, noncausal autoregressive and noninvertible moving average models closely approximate each other, and therefore,the former provide a viable and practically convenient alternative. We show how the parameters of a noncausal autoregressive model can be estimated by the method of maximum likelihood and derive related test procedures. Because noncausal autoregressive models cannot be distinguished from conventional causal autoregressive models by second order properties or Gaussian likelihood, a model selection procedure is proposed. As an empirical application, we consider modeling the U.S. inflation which, according to our results, exhibits purely forward-looking dynamics

NÄIN VOIMME TYÖSSÄMME– Työhyvinvointitutkimus nuorisokotien työntekijöistä

Tutkimustehtävänä oli tuottaa neljälle Jyväskylän alueella toimivalle nuorisokodin johtajalle tietoa heidän työpaikkansa työntekijöiden työhyvinvoinnin tilasta tällä hetkellä. Tutkimus toteutettiin joulukuussa 2014. Aineistonkeruumenetelmänä toimi kyselylomake, joka koostui 44 väittämästä ja avoimista vastaus-vaihtoehdoista. Avoimien kysymysten tarkoituksena oli tuoda esille työntekijöiden omat kokemukset ja ajatukset työn vaatimustason, työilmapiirin ja esimiestyön onnistumisesta. Osallistujia oli 35. Tutkimuk-seen osallistuneita nuorisokoteja emme tuo julki, jotta yksittäisten nuorisokotien tuloksia ei voi vertailla. Toimitimme kuitenkin jokaiseen mukana olleeseen nuorisokotiin heidän omat tulokset, tulokset lähetettiin johtajille tammikuussa 2015. Olemme koonneet tutkimustulokset yhteenvetona kaikista tutkituista nuorisokodeista, johtopäätökset perustuvat koko aineistoon. Lastensuojelutilastojen perusteella lastensuojelun tarve on noussut viime vuosina, joten on tärkeää että lastensuojelun työntekijät voivat työssään hyvin. Tutkimustulosten perusteella toteamme, että nuorisokodinohjaajat voivat hyvin työssään. Tutkimuksemme osoitti, että työkykyä tukevaan toimintaan on panostettava lisää. Työilmapiiri ja työkavereiden tuki ja arvostus arvioitiin erittäin tärkeäksi tekijäksi työhyvinvoinnin kokemisessa. Tutkimuksemme vahvisti käsitystä siitä miten tärkeää työntekijän kokema työhyvinvointi on lastensuoje-lutyössä. Lastensuojelutyössä työn luonne on sosiaalisia ja psyykkisiä voimavaroja vievää. Tutkimustie-tomme myös edesauttaa esimiehiä työhyvinvoinnin edistämisessä ja toimii tarvittaessa työpaikoilla käy-tävien keskusteluiden avaajana.The research task was to produce information for four different youth homes in Jyväskylä about their work wellbeing at the moment. The research was conducted in December 2014. Data were collected by a questionnaire which consisted 44 statements and open-answer questions. The open questions were de-signed to bring out the workers’ own experiences and thoughts about work standards, work environment and leadership. 35 employees participated in the study. In this thesis we will not publish which youth homes took part in our study in order not to compare the youth homes. However, we delivered the results on their work wellbeing for each one individually. The results were sent in January 2015. We have compiled a summary of the results of all the surveyed youth homes and the conclusions are based on the entire data. Child welfare statistics show that children’s need for child welfare has risen in the recent years, and therefore it is important that employees in youth homes feel well at work. The results indicate that youth home employers feel well in their work. However, our study also shows that more should be invested in supporting working ability. The atmosphere at work, co-workers’ support and appreciation was seen as a very important factor in work wellbeing. Our study confirms how important employees’ work wellbeing is in child welfare work. Socially and psychologically the nature of child welfare work is consuming. Our research data helps the directors of the four youth homes to promote their work wellbeing, and if necessary helps to open discussions about work wellbeing

Parameter estimation in nonlinear AR–GARCH models

This paper develops an asymptotic estimation theory for nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a general nonlinear autoregression of order p (AR(p)) with the conditional variance specified as a general nonlinear first order generalized autoregressive conditional heteroskedasticity (GARCH(1,1)) model. We do not require the rescaled errors to be independent, but instead only to form a stationary and ergodic martingale difference sequence. Strong consistency and asymptotic normality of the global Gaussian quasi maximum likelihood (QML) estimator are established under conditions comparable to those recently used in the corresponding linear case. To the best of our knowledge, this paper provides the first results on consistency and asymptotic normality of the QML estimator in nonlinear autoregressive models with GARCH errors.Nonlinear Autoregression, Generalized Autoregressive Conditional Heteroskedasticity, Nonlinear Time Series Models, Quasi-Maximum Likelihood Estimation, Strong Consistency, Asymptotic Normality

Parameter Estimation in Nonlinear AR-GARCH Models

This paper develops an asymptotic estimation theory for nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a functional coefficient autoregression of order p (AR(p)) with the conditional variance specified as a general nonlinear first order generalized autoregressive conditional heteroskedasticity (GARCH(1,1)) model. Strong consistency and asymptotic normality of the global Gaussian quasi maximum likelihood (QML) estimator are established under conditions comparable to those recently used in the corresponding linear case. To the best of our knowledge, this paper provides the first results on consistency and asymptotic normality of the QML estimator in nonlinear autoregressive models with GARCH errors.AR-GARCH, asymptotic normality, consistency, nonlinear time series, quasi maximum likelihood estimation

Noncausal autoregressions for economic time series

This paper is concerned with univariate noncausal autoregressive models and their potential usefulness in economic applications. In these models, future errors are predictable, indicating that they can be used to empirically approach rational expectations models with nonfundamental solutions. In the previous theoretical literature, nonfundamental solutions have typically been represented by noninvertible moving average models. However, noncausal autoregressive and noninvertible moving average models closely approximate each other, and therefore,the former provide a viable and practically convenient alternative. We show how the parameters of a noncausal autoregressive model can be estimated by the method of maximum likelihood and derive related test procedures. Because noncausal autoregressive models cannot be distinguished from conventional causal autoregressive models by second order properties or Gaussian likelihood, a model selection procedure is proposed. As an empirical application, we consider modeling the U.S. inflation which, according to our results, exhibits purely forward-looking dynamics.Noncausal autoregression; expectations; inflation persistence

A Skewed GARCH-in-Mean Model: An Application to U.S. Stock Returns

In this paper we consider a GARCH-in-Mean (GARCH-M) model based on the so-called z distribution. This distribution is capable of modeling moderate skewness and kurtosis typically encountered in financial return series, and the need to allow for skewness can be readily tested. We apply the new GARCH-M model to study the relationship between risk and return in monthly postwar U.S. stock market data. Our results indicate the presence of conditional skewness in U.S. stock returns, and, in contrast to the previous literature, we show that a positive and significant relationship between return and risk can be uncovered, once an appropriate probability distribution is employed to allow for conditional skewnessConditional skewness, GARCH-in-Mean, Risk-return tradeoff