Nonlinear mixture autoregressive model: economic applications

The aim of this dissertation is to model economic variables by a mixture autoregressive (MAR) model. The MAR model is a generalization of linear autoregressive (AR) model. The MAR -model consists of K linear autoregressive components. At any given point of time one of these autoregressive components is randomly selected to generate a new observation for the time series. The mixture probability can be constant over time or a direct function of a some observable variable. Many economic time series contain properties which cannot be described by linear and stationary time series models. A nonlinear autoregressive model such as MAR model can a plausible alternative in the case of these time series. In this dissertation the MAR model is used to model stock market bubbles and a relationship between inflation and the interest rate. In the case of the inflation rate we arrived at the MAR model where inflation process is less mean reverting in the case of high inflation than in the case of normal inflation. The interest rate move one-for-one with expected inflation. We use the data from the Livingston survey as a proxy for inflation expectations. We have found that survey inflation expectations are not perfectly rational. According to our results information stickiness play an important role in the expectation formation. We also found that survey participants have a tendency to underestimate inflation. A MAR model has also used to model stock market bubbles and crashes. This model has two regimes: the bubble regime and the error correction regime. In the error correction regime price depends on a fundamental factor, the price-dividend ratio, and in the bubble regime, price is independent of fundamentals. In this model a stock market crash is usually caused by a regime switch from a bubble regime to an error-correction regime. According to our empirical results bubbles are related to a low inflation. Our model also imply that bubbles have influences investment return distribution in both short and long run.

Struggles Over Legitimacy in Global Organizational Restructuring: A Rhetorical Perspective on Legitimation Strategies and Dynamics in a Shutdown Case

Critical organization scholars have focused increasing attention on industrial and organizational restructurings such as shutdown decisions. However, little is known about the rhetorical strategies used to legitimate or resist plant closures in organizational negotiations. In this article, we draw from New Rhetoric to analyze rhetorical struggles, strategies and dynamics in unfolding organizational negotiations. We focus on the shutdown of the bus body unit of the Sweden-based Volvo Bus Corporation in Finland. We distinguish five types of rhetorical legitimation strategies and dynamics. These include the three classical dynamics of logos (rational arguments), pathos (emotional moral arguments), and ethos (authority-based arguments), but also autopoiesis (autopoietic narratives), and cosmos (cosmological constructions). Our analysis contributes to previous studies on organizational restructuring by providing a more nuanced understanding of how contemporary industrial closures are legitimated and resisted in organizational negotiations. This study also increases theoretical understanding of the role of rhetoric in legitimation more generally.

Texts "Con and Pro" : The Early Modern Medical Controversy over Tobacco

The habit of "drinking smoke" , meaning tobacco smoking, caused a true controversy in early modern England. The new substance was used both for its alleged therapeutic properties as well as its narcotic effects. The dispute over tobacco continues the line of written controversies which were an important means of communication in the sixteenth and seventeenth century Europe. The tobacco controversy is special among medical controversies because the recreational use of tobacco soon spread and outweighed its medicinal use, ultimately causing a social and cultural crisis in England. This study examines how language is used in polemic discourse and argumentation. The material consists of medical texts arguing for and against tobacco in early modern England. The texts were compiled into an electronic corpus of tobacco texts (1577 1670) representing different genres and styles of writing. With the help of the corpus, the tobacco controversy is described and analyzed in the context of early modern medicine. A variety of methods suitable for the study of conflict discourse were used to assess internal and external text variation. The linguistic features examined include personal pronouns, intertextuality, structural components, and statistically derived keywords. A common thread in the work is persuasive language use manifested, for example, in the form of emotive adjectives and the generic use of pronouns; the latter is especially pronounced in the dichotomy between us and them. Controversies have not been studied in this manner before but the methods applied have supplemented each other and proven their suitability in the study of conflictive discourse. These methods can also be applied to present-day materials.

Maidontuottajatila ja ravinnepolitiikka: Tilan toimintamekanismit, politiikan tarve ja ohjauskeinojen vaikuttavuus

Tutkielman tarkoituksena on rakentaa malli, jolla voidaan arvioida maitotilan toimintaa monipuolisesti ravinnehuuhtouman kannalta. Tavoitteena on ennen muuta analyyttinen tarkastelu siten, että kuitenkin huomioiden maidontuotannon ominaispiirteet, keskeisinä esimerkkeinä ravinnon, maidontuotannon ja lannan ominaisuuksien väliset riippuvuudet. Analyysissa tarkastellaan tilanomistajan yksityisen hyödynmaksimoinnin ja yhteiskunnan tavoitteiden eroavaisuutta. Lisäksi johdetaan optimaaliset ohjauskeinot ja arvioidaan eräiden yksinkertaisten ohjauskeinojen vaikuttavuutta. Mallin analysoinnin perusteella yksityinen ja yhteiskunnallinen optimiratkaisu eroavat toisistaan kaikkien päätösmuuttujien osalta. Ei voida kuitenkaan yleispätevästi sanoa muutosten merkittävyyttä tai suuntaa ottamatta kantaa mallin funktiomuotoihin ja parametriarvoihin. Optimaaliset ohjauskeinot tulisi asettaa keinolannoitteen ja lannan levitykselle, väkirehuruokinnalle ja säilörehun viljelylle, mutta ei eläinten määrälle. Toisaalta optimaaliset ohjauskeinot ovat hyvin monimutkaisia. Tarkasteltujen yksinkertaisempien ohjauskeinojen vaikuttavuutta ei voida tarkasti arvioida analyyttisella tasolla. Numeeristen tulosten perusteella yhteiskunnallisessa optimissa eläinmäärä olisi hieman yksityistä optimia pienempi, väkirehun käyttö vähäisempää, enemmän peltoa allokoitaisiin säilörehun viljelyyn ja lannoitustasot olisivat kautta linjan hieman pienemmät. Lannanlevitykset eroavat etäisyyden suhteen: molemmissa tapauksissa lannanlevityksen intensiteetti kasvaa kohti tilan keskusta kuljettaessa, mutta yksityisessä optimissa lähimmälle pellolle dumpataan kaikki ylimääräinen lanta, kun taas yhteiskunnallisessa optimissa lanta levitetään tasaisemmin eri pelloille. Ero kokonaishyvinvoinnissa jää pieneksi, mutta eläinmäärän kasvu kärjistäisi lannan dumppauksen aiheuttamia huuhtoumahaittoja. Yksinkertaisista ohjauskeinoista lannoitusrajoitus sekä ravinne- ja keinolannoitevero osoittautuivat kohtalaisen käyttökelpoisiksi numeeristen tulosten perusteella. Sen sijaan eläinmäärän rajoittaminen ja lannan kuljetuskustannuksia kompensoivat tuet vaikuttavat tulosten perusteella huonoilta ratkaisuilta.

Algorithms for Exact Structure Discovery in Bayesian Networks

Bayesian networks are compact, flexible, and interpretable representations of a joint distribution. When the network structure is unknown but there are observational data at hand, one can try to learn the network structure. This is called structure discovery. This thesis contributes to two areas of structure discovery in Bayesian networks: space--time tradeoffs and learning ancestor relations. The fastest exact algorithms for structure discovery in Bayesian networks are based on dynamic programming and use excessive amounts of space. Motivated by the space usage, several schemes for trading space against time are presented. These schemes are presented in a general setting for a class of computational problems called permutation problems; structure discovery in Bayesian networks is seen as a challenging variant of the permutation problems. The main contribution in the area of the space--time tradeoffs is the partial order approach, in which the standard dynamic programming algorithm is extended to run over partial orders. In particular, a certain family of partial orders called parallel bucket orders is considered. A partial order scheme that provably yields an optimal space--time tradeoff within parallel bucket orders is presented. Also practical issues concerning parallel bucket orders are discussed. Learning ancestor relations, that is, directed paths between nodes, is motivated by the need for robust summaries of the network structures when there are unobserved nodes at work. Ancestor relations are nonmodular features and hence learning them is more difficult than modular features. A dynamic programming algorithm is presented for computing posterior probabilities of ancestor relations exactly. Empirical tests suggest that ancestor relations can be learned from observational data almost as accurately as arcs even in the presence of unobserved nodes.