900 resultados para Time inventory models


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INTRODUCTION: Forecasting dengue cases in a population by using time-series models can provide useful information that can be used to facilitate the planning of public health interventions. The objective of this article was to develop a forecasting model for dengue incidence in Campinas, southeast Brazil, considering the Box-Jenkins modeling approach. METHODS: The forecasting model for dengue incidence was performed with R software using the seasonal autoregressive integrated moving average (SARIMA) model. We fitted a model based on the reported monthly incidence of dengue from 1998 to 2008, and we validated the model using the data collected between January and December of 2009. RESULTS: SARIMA (2,1,2) (1,1,1)12 was the model with the best fit for data. This model indicated that the number of dengue cases in a given month can be estimated by the number of dengue cases occurring one, two and twelve months prior. The predicted values for 2009 are relatively close to the observed values. CONCLUSIONS: The results of this article indicate that SARIMA models are useful tools for monitoring dengue incidence. We also observe that the SARIMA model is capable of representing with relative precision the number of cases in a next year.

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Continente Online (COL) is the market leader of online grocery retailing in Portugal. Aiming at sustaining this position, it has been focusing its promotional efforts in acquiring and retaining customers. This report intakes an extensive study on the impact of COL’s exclusive promotional efforts (free delivery offers and ten percent discounts on loyalty card) on customer acquisition and retention, as well as, a cost analysis on these offers. Two econometric models were developed and the results were interesting. It was concluded that free delivery offers have a significant impact both in terms of customer acquisition and retention, having also a lower cost in comparison with ten percent on loyalty card discounts.

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Dissertação de mestrado em Estatística

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Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

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This paper analyzes the propagation of monetary policy shocks through the creation of credit in an economy. Models of the monetary transmission mechanism typically feature responses which last for a few quarters contrary to what the empirical evidence suggests. To propagate the impact of monetary shocks over time, these models introduce adjustment costs by which agents find it optimal to change their decisions slowly. This paper presents another explanation that does not rely on any sort of adjustment costs or stickiness. In our economy, agents own assets and make occupational choices. Banks intermediate between agents demanding and supplying assets. Our interpretation is based on the way banks create credit and how the monetary authority affects the process of financial intermediation through its monetary policy. As the central bank lowers the interest rate by buying government bonds in exchange for reserves, high productive entrepreneurs are able to borrow more resources from low productivity agents. We show that this movement of capital among agents sets in motion a response of the economy that resembles an expansionary phase of the cycle.

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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.

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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.

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Background: Atazanavir boosted with ritonavir (ATV/r) and efavirenz (EFV) are both recommended as first-line therapies for HIV-infected patients. We compared the 2 therapies for virologic efficacy and immune recovery. Methods: We included all treatment-naïve patients in the Swiss HIV Cohort Study starting therapy after May 2003 with either ATV/r or EFV and a backbone of tenofovir and either emtricitabine or lamivudine. We used Cox models to assess time to virologic failure and repeated measures models to assess the change in CD4 cell counts over time. All models were fit as marginal structural models using both point of treatment and censoring weights. Intent-to-treat and various as-treated analyses were carried out: In the latter, patients were censored at their last recorded measurement if they changed therapy or if they were no longer adherent to therapy. Results: Patients starting EFV (n = 1,097) and ATV/r (n = 384) were followed for a median of 35 and 37 months, respectively. During follow-up, 51% patients on EFV and 33% patients on ATV/r remained adherent and made no change to their first-line therapy. Although intent-to-treat analyses suggest virologic failure was more likely with ATV/r, there was no evidence for this disadvantage in patients who adhered to first-line therapy. Patients starting ATV/r had a greater increase in CD4 cell count during the first year of therapy, but this advantage disappeared after one year. Conclusions: In this observational study, there was no good evidence of any intrinsic advantage for one therapy over the other, consistent with earlier clinical trials. Differences between therapies may arise in a clinical setting because of differences in adherence to therapy.

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El projecte realitzat se situa en el marc de la història contemporània, i s’ha centrat en primer lloc, en l’anàlisi, des d’una perspectiva comparativa, del desenvolupament dels discursos de gènere a Catalunya durant la Dictadura Franquista i a la Irlanda postcolonial. Mitjançant l’anàlisi del discurs, s’han estudiat els models de feminitat imposats pel Franquisme i les seves bases ideològiques com són el valors catòlics i l’antindividualisme. En el cas irlandès, s’ha analitzat com, a través de determinades institucions gestionades per l’Església Catòlica, es controlaven aquelles dones que es desviaven del model de gènere que propugnava l’Estat Irlandès, molt similar al proposat pel Franquisme i també basat en els catolicisme. De la mateix manera, s’ha estudiat com el feminisme Català i irlandès dels anys 1970 i 1980 van contrarestar aquests models de gènere imposats, a través de l’anàlisi d’un conjunt d’expressions culturals produïdes per ambdós moviments feministes. La perspectiva comparativa del projecte ha permès: El coneixement dels mecanismes culturals de repressió de les dones així com la seva institucionalització. Revelant els paral•lelismes pel que fa a les polítiques de gènere entre els dos casos estudiats malgrat diferències significatives entre els dos contextos (Catalunya es troba sota una dictadura, Irlanda és un Estat democràtic). La importància de l’agència de les dones i les seves diverses estratègies de resistència, especialment a través d’expressions culturals més efímeres o considerades frívoles que, malgrat el poc reconeixement que han obtingut, són molt eficaces en la deconstrucció de discursos de gènere repressius envers les dones. Ha posat de manifest, també, la importància de l’experiència i les pràctiques personals i íntimes com a pràctiques de resistència. Així mateix, ha visibilitzat les dinàmiques pròpies de moviments feministes.

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In this paper we argue that inventory models are probably not usefulmodels of household money demand because the majority of households does nothold any interest bearing assets. The relevant decision for most people is notthe fraction of assets to be held in interest bearing form, but whether to holdany of such assets at all. The implications of this realization are interesting and important. We find that(a) the elasticity of money demand is very small when the interest rate is small,(b) the probability that a household holds any amount of interest bearing assetsis positively related to the level of financial assets, and (c) the cost ofadopting financial technologies is positively related to age and negatively relatedto the level of education. Unlike the traditional methods of money demand estimation, our methodology allowsfor the estimation of the interest--elasticity at low values of the nominalinterest rate. The finding that the elasticity is very small for interest ratesbelow 5 percent suggests that the welfare costs of inflation are small. At interest rates of 6 percent, the elasticity is close to 0.5. We find thatroughly one half of this elasticity can be attributed to the Baumol--Tobin orintensive margin and half of it can be attributed to the new adopters or extensivemargin. The intensive margin is less important at lower interest rates and moreimportant at higher interest rates.

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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

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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

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Background- An elevated resting heart rate is associated with rehospitalization for heart failure and is a modifiable risk factor in heart failure patients. We aimed to examine the association between resting heart rate and incident heart failure in a population-based cohort study of healthy adults without pre-existing overt heart disease. Methods and Results- We studied 4768 men and women aged ≥55 years from the population-based Rotterdam Study. We excluded participants with prevalent heart failure, coronary heart disease, pacemaker, atrial fibrillation, atrioventricular block, and those using β-blockers or calcium channel blockers. We used extended Cox models allowing for time-dependent variation of resting heart rate along follow-up. During a median of 14.6 years of follow-up, 656 participants developed heart failure. The risk of heart failure was higher in men with higher resting heart rate. For each increment of 10 beats per minute, the multivariable adjusted hazard ratios in men were 1.16 (95% confidence interval, 1.05-1.28; P=0.005) in the time-fixed heart rate model and 1.13 (95% confidence interval, 1.02-1.25; P=0.017) in the time-dependent heart rate model. The association could not be demonstrated in women (P for interaction=0.004). Censoring participants for incident coronary heart disease or using time-dependent models to account for the use of β-blockers or calcium channel blockers during follow-up did not alter the results. Conclusions- Baseline or persistent higher resting heart rate is an independent risk factor for the development of heart failure in healthy older men in the general population.

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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.

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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