The Difference, System and ‘Double-D’ GMM Panel Estimators in the Presence of Structural Breaks

The effects of structural breaks in dynamic panels are more complicated than in time series models as the bias can be either negative or positive. This paper focuses on the effects of mean shifts in otherwise stationary processes within an instrumental variable panel estimation framework. We show the sources of the bias and a Monte Carlo analysis calibrated on United States bank lending data demonstrates the size of the bias for a range of auto-regressive parameters. We also propose additional moment conditions that can be used to reduce the biases caused by shifts in the mean of the data.

Expectations Traps and Coordination Failures with Discretionary Policymaking

Discretionary policymakers cannot manage private-sector expectations and cannot coordinate the actions of future policymakers. As a consequence, expectations traps and coordination failures can occur and multiple equilibria can arise. To utilize the explanatory power of models with multiple equilibria it is first necessary to understand how an economy arrives to a particular equilibrium. In this paper we employ notions of learnability and self-enforceability to motivate and identify equilibria of particular interest. Central among these criteria are whether the equilibrium is learnable by private agents and jointly learnable by private agents and the policymaker. We use two New Keynesian policy models to identify the strategic interactions that give rise to multiple equilibria and to illustrate our methods for identifying equilibria of interest. Importantly, unless the Pareto-preferred equilibrium is learnable by private agents, we find little reason to expect coordination on that equilibrium.

Large Bayesian VARMAs

Vector Autoregressive Moving Average (VARMA) models have many theoretical properties which should make them popular among empirical macroeconomists. However, they are rarely used in practice due to over-parameterization concerns, difficulties in ensuring identification and computational challenges. With the growing interest in multivariate time series models of high dimension, these problems with VARMAs become even more acute, accounting for the dominance of VARs in this field. In this paper, we develop a Bayesian approach for inference in VARMAs which surmounts these problems. It jointly ensures identification and parsimony in the context of an efficient Markov chain Monte Carlo (MCMC) algorithm. We use this approach in a macroeconomic application involving up to twelve dependent variables. We find our algorithm to work successfully and provide insights beyond those provided by VARs.

A systematic and feasible method for computing nuclear contributions to electrical properties of polyatomic molecules

An analytic method to evaluate nuclear contributions to electrical properties of polyatomic molecules is presented. Such contributions control changes induced by an electric field on equilibrium geometry (nuclear relaxation contribution) and vibrational motion (vibrational contribution) of a molecular system. Expressions to compute the nuclear contributions have been derived from a power series expansion of the potential energy. These contributions to the electrical properties are given in terms of energy derivatives with respect to normal coordinates, electric field intensity or both. Only one calculation of such derivatives at the field-free equilibrium geometry is required. To show the useful efficiency of the analytical evaluation of electrical properties (the so-called AEEP method), results for calculations on water and pyridine at the SCF/TZ2P and the MP2/TZ2P levels of theory are reported. The results obtained are compared with previous theoretical calculations and with experimental values

Subsampling the mean of heavy-tailed dependent observations

We establish the validity of subsampling confidence intervals for themean of a dependent series with heavy-tailed marginal distributions.Using point process theory, we study both linear and nonlinear GARCH-liketime series models. We propose a data-dependent method for the optimalblock size selection and investigate its performance by means of asimulation study.

Systematic study of the static electrical properties of the CO molecule: influence of the basis set size and correlation energy

The influence of the basis set size and the correlation energy in the static electrical properties of the CO molecule is assessed. In particular, we have studied both the nuclear relaxation and the vibrational contributions to the static molecular electrical properties, the vibrational Stark effect (VSE) and the vibrational intensity effect (VIE). From a mathematical point of view, when a static and uniform electric field is applied to a molecule, the energy of this system can be expressed in terms of a double power series with respect to the bond length and to the field strength. From the power series expansion of the potential energy, field-dependent expressions for the equilibrium geometry, for the potential energy and for the force constant are obtained. The nuclear relaxation and vibrational contributions to the molecular electrical properties are analyzed in terms of the derivatives of the electronic molecular properties. In general, the results presented show that accurate inclusion of the correlation energy and large basis sets are needed to calculate the molecular electrical properties and their derivatives with respect to either nuclear displacements or/and field strength. With respect to experimental data, the calculated power series coefficients are overestimated by the SCF, CISD, and QCISD methods. On the contrary, perturbation methods (MP2 and MP4) tend to underestimate them. In average and using the 6-311 + G(3df) basis set and for the CO molecule, the nuclear relaxation and the vibrational contributions to the molecular electrical properties amount to 11.7%, 3.3%, and 69.7% of the purely electronic μ, α, and β values, respectively

Biomass estimation of Triplectides egleri Sattler (Trichoptera, Leptoceridae) in a stream at Ducke Reserve, Central Amazonia

ABSTRACT Biomass is a fundamental measure for understanding the structure and functioning (e.g. fluxes of energy and nutrients in the food chain) of aquatic ecosystems. We aim to provide predictive models to estimate the biomass of Triplectides egleri Sattler, 1963, in a stream in Central Amazonia, based on body and case dimensions. We used body length, head-capsule width, interocular distance and case length and width to derive biomass estimations. Linear, exponential and power regression models were used to assess the relationship between biomass and body or case dimensions. All regression models used in the biomass estimation of T. egleri were significant. The best fit between biomass and body or case dimensions was obtained using the power model, followed by the exponential and linear models. Body length provided the best estimate of biomass. However, the dimensions of sclerotized structures (interocular distance and head-capsule width) also provided good biomass predictions, and may be useful in estimating biomass of preserved and/or damaged material. Case width was the dimension of the case that provided the best estimate of biomass. Despite the low relation, case width may be useful in studies that require low stress on individuals.

Una extensión de la regresión propuesta por Geweke y Porter-Hudak para la estimación del orden de diferenciación en modelos ARFIMA

Recientemente, ha aumentado mucho el interés por la aplicación de los modelos de memoria larga a variables económicas, sobre todo los modelos ARFIMA. Sin duda , el método más usado para la estimación de estos modelos en el ámbito del análisis económico es el propuesto por Geweke y Portero-Hudak (GPH) aun cuando en trabajos recientes se ha demostrado que, en ciertos casos, este estimador presenta un sesgo muy importante. De ahí que, se propone una extensión de este estimador a partir del modelo exponencial propuesto por Bloomfield, y que permite corregir este sesgo.A continuación, se analiza y compara el comportamiento de ambos estimadores en muestras no muy grandes y se comprueba como el estimador propuesto presenta un error cuadrático medio menor que el estimador GPH

Una extensión de la regresión propuesta por Geweke y Porter-Hudak para la estimación del orden de diferenciación en modelos ARFIMA

Recientemente, ha aumentado mucho el interés por la aplicación de los modelos de memoria larga a variables económicas, sobre todo los modelos ARFIMA. Sin duda , el método más usado para la estimación de estos modelos en el ámbito del análisis económico es el propuesto por Geweke y Portero-Hudak (GPH) aun cuando en trabajos recientes se ha demostrado que, en ciertos casos, este estimador presenta un sesgo muy importante. De ahí que, se propone una extensión de este estimador a partir del modelo exponencial propuesto por Bloomfield, y que permite corregir este sesgo.A continuación, se analiza y compara el comportamiento de ambos estimadores en muestras no muy grandes y se comprueba como el estimador propuesto presenta un error cuadrático medio menor que el estimador GPH

Elastic scattering of fast electrons and positrons by atoms

Elastic scattering of relativistic electrons and positrons by atoms is considered in the framework of the static field approximation. The scattering field is expressed as a sum of Yukawa terms to allow the use of various approximations. Accurate phase shifts have been computed by combining Bühring¿s power-series method with the WKB and Born approximations. This combined procedure allows the evaluation of differential cross sections for kinetic energies up to several tens of MeV. Numerical results are used to analyze the validity of several approximate methods, namely the first- and second-order Born approximations and the screened Mott formula, which are frequently adopted as the basis of multiple scattering theories and Monte Carlo simulations of electron and positron transport.

Jatkuvatoimisen sellukeittimen säätöjen suorituskyvyn analysointi

Työssä tarkastellaan yleisiä menetelmiä säätöpiirien suorituskyvyn analysointiin ja sovelletaan niitä jatkuvatoimisen sellukeittimen säätöihin. Esitellyt menetelmät tarjoavat keinoja myös huonon säätötuloksen syyn selvittämiseen ja vinkkejä paremman suorituskyvyn saavuttamiseksi. Analyysissä edettiin top-down periaatteen mukaisesti lähtien liikkeelle keittimen tärkeimmästä säädöstä eli kappaluvun säädöstä. Sitten etsittiin tähän vaikuttavia tekijöitä mitatuista suureista. Seuraavaksi arvioitiin tärkeimmäksi katsotun tekijän (hakepinnankorkeus) säädön suorituskyky, jossa havaittiin parannettavaa. Lopuksi hakepinnankorkeuden säädön viritystämuutettiin ja tehtiin identifiointikoe säätörakenteen uudelleen järjestelyä varten.

Sellutehtaan varavoimajärjestelmän mitoitus ja teknistaloudellinen vertailu 400 ja 690 voltin jännitteillä

Tämän diplomityön tarkoitus on selvittää pienjakelujännitteen nostosta aiheutuvia vaikutuksia sellutehtaan varavoimaverkossa. Työn alussa esitellään varavoimaverkkoon kuuluvia osia, selvitetään erilaisten kuormien vaikutusta varavoimaverkon sähkön laatuun sekä kuormien jakoa varmennettuihin verkkoihin. Seuraavaksi suunnitellaan kaksi keskitettyä varavoimaverkkomallia eri pienjakelujännitteillä. Mallien nimellisjännitteet ovat 400 V ja 690 V. Varavoimaverkkomallien kuormat ja osastojen väliset etäisyydet on otettu valmiista sellutehtaista. Tässä diplomityössä painotutaan erityisesti varavoimaverkkomallien mitoitukseen ja jakelujännitteen nostosta aiheutuvien vaikutusten teknistaloudelliseen vertailuun. Vertailu tehdään keskijännitekojeiston ja varavoimakeskusten välisellä alueella, johon kuu-luvat jakelumuuntaja, varavoimakone, varavoimapääkeskus, kiskosillat, jakelukaapelit, kytkinvaroke- ja katkaisijalähdöt sekä mahdolliset välimuuntajat. Varavoimaverkkojen välisessä kustannusvertailussa käytetään nykyarvomenetelmää.

Forecasting tourism demand using consumer expectations

Abstract Purpose- There is a lack of studies on tourism demand forecasting that use non-linear models. The aim of this paper is to introduce consumer expectations in time-series models in order to analyse their usefulness to forecast tourism demand. Design/methodology/approach- The paper focuses on forecasting tourism demand in Catalonia for the four main visitor markets (France, the UK, Germany and Italy) combining qualitative information with quantitative models: autoregressive (AR), autoregressive integrated moving average (ARIMA), self-exciting threshold autoregressions (SETAR) and Markov switching regime (MKTAR) models. The forecasting performance of the different models is evaluated for different time horizons (one, two, three, six and 12 months). Findings- Although some differences are found between the results obtained for the different countries, when comparing the forecasting accuracy of the different techniques, ARIMA and Markov switching regime models outperform the rest of the models. In all cases, forecasts of arrivals show lower root mean square errors (RMSE) than forecasts of overnight stays. It is found that models with consumer expectations do not outperform benchmark models. These results are extensive to all time horizons analysed. Research limitations/implications- This study encourages the use of qualitative information and more advanced econometric techniques in order to improve tourism demand forecasting. Originality/value- This is the first study on tourism demand focusing specifically on Catalonia. To date, there have been no studies on tourism demand forecasting that use non-linear models such as self-exciting threshold autoregressions (SETAR) and Markov switching regime (MKTAR) models. This paper fills this gap and analyses forecasting performance at a regional level. Keywords Tourism, Forecasting, Consumers, Spain, Demand management Paper type Research paper

The Combined treatment of UV Light and Titanium Dioxide (TiO2) in Grease Filtration Technique

The aim of this thesis was to identify the best grease removal technique with the application of low power of UV light to TiO2 coated grease filters. The treatment with various power series of ozone generating and ozone free lamps to normal grease filters and TiO2 coated grease filters were examined and the obtained results are compared to each other in this paper. The effect of ozone reaction was observed and compared with the effect of TiO2. The experiments were solely based on the photo oxidation and photo catalytic oxidation reactions. TiO2 is a green catalyst used in the photocatalytic reaction. Sunflower oil was used for grease production and tetracholoroethylene as a solvent. Grease samples were collected from the ventilation duct connected to the cooking hood system. Sample extraction was done in ultrasonic bath with the principle of sonication. The sample analysis was done by FTIR machine. The result determining the concentration of grease was the quantification of saturated C-H bonds in the chosen peak group of the spectrum. A very low power of UVC light functions perfectly with the Titanium dioxide. The experimental results have shown the combined treatment of titanium dioxide and UV light is an effective method in grease removal process. The photocatalytic reaction with titanium dioxide is better than photo oxidation reaction with ozone treatment. Photocatalytic reaction is environmentally friendly, energy efficient and economical.

Mathematical modeling of microwave dried celery leaves and determination of the effective moisture diffusivities and activation energy

Celery (Apium graveolens L. var. secalinum Alef) leaves with 50±0.07 g weight and 91.75±0.15% humidity (~11.21 db) were dried using 8 different microwave power densities ranging between 1.8-20 W g-1, until the humidity fell down to 8.95±0.23% (~0.1 db). Microwave drying processes were completed between 5.5 and 77 min depending on the microwave power densities. In this study, measured values were compared with predicted values obtained from twenty thin layer drying theoretical, semi-empirical and empirical equations with a new thin layer drying equation. Within applied microwave power density; models whose coefficient and correlation (R²) values are highest were chosen as the best models. Weibull distribution model gave the most suitable predictions at all power density. At increasing microwave power densities, the effective moisture diffusivity values ranged from 1.595 10-10 to 6.377 10-12 m2 s-1. The activation energy was calculated using an exponential expression based on Arrhenius equation. The linear relationship between the drying rate constant and effective moisture diffusivity gave the best fit.