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Series Modelling (series + modelling)

Distribution by Scientific Domains
Mathematics and Statistics50%
Business, Economics, Finance and Accounting33%

Kinds of Series Modelling

  • time series modelling
    		•

    Selected Abstracts

    Vector Autoregression (Var) , An Approach to Dynamic analysis of Geographic Processes

    GEOGRAFISKA ANNALER SERIES B: HUMAN GEOGRAPHY, Issue 2 2001
    Max Lu
    Vector autoregression (VAR) is a widely used econometric technique for multivariate time series modelling. This paper shows that with several very attractive features, VAR may also provide a valuable tool for analysing the dynamics among geographic processes and for spatial autoregressive modelling. After a brief discussion of the VAR approach, a VAR model for the dynamics of the US population between 1910 and 1990 is estimated and interpreted to illustrate the techniques. The VAR makes it possible to view the interactions among the four variables used in the model (total population, birth rate, immigration and per capita GNP) more adequately. The paper then discusses recent developments in the VAR methodology such as Bayesian vector autoregression (BVAR), spatial prior for regional modelling and cointegration, as well as the limitations and problems that arise from the application of VARs. [source]

    A data mining approach to financial time series modelling and forecasting

    INTELLIGENT SYSTEMS IN ACCOUNTING, FINANCE & MANAGEMENT, Issue 4 2001
    Zoran Vojinovic
    This paper describes one of the relatively new data mining techniques that can be used to forecast the foreign exchange time series process. The research aims to contribute to the development and application of such techniques by exposing them to difficult real-world (non-toy) data sets. The results reveal that the prediction of a Radial Basis Function Neural Network model for forecasting the daily $US/$NZ closing exchange rates is significantly better than the prediction of a traditional linear autoregressive model in both directional change and prediction of the exchange rate itself. We have also investigated the impact of the number of model inputs (model order), the number of hidden layer neurons and the size of training data set on prediction accuracy. In addition, we have explored how the three different methods for placement of Gaussian radial basis functions affect its predictive quality and singled out the best one. Copyright © 2001 John Wiley & Sons, Ltd. [source]

    Time series modelling of two millennia of northern hemisphere temperatures: long memory or shifting trends?

    JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 1 2007
    Terence C. Mills
    Summary., The time series properties of the temperature reconstruction of Moberg and co-workers are analysed. It is found that the record appears to exhibit long memory characteristics that can be modelled by an autoregressive fractionally integrated moving average process that is both stationary and mean reverting, so that forecasts will eventually return to a constant underlying level. Recent research has suggested that long memory and shifts in level and trend may be confused with each other, and fitting models with slowly changing trends is found to remove the evidence of long memory. Discriminating between the two models is difficult, however, and the strikingly different forecasts that are implied by the two models point towards some intriguing research questions concerning the stochastic process driving this temperature reconstruction. [source]

    Time series modelling of childhood diseases: a dynamical systems approach

    JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 2 2000
    B. F. Finkenstädt
    A key issue in the dynamical modelling of epidemics is the synthesis of complex mathematical models and data by means of time series analysis. We report such an approach, focusing on the particularly well-documented case of measles. We propose the use of a discrete time epidemic model comprising the infected and susceptible class as state variables. The model uses a discrete time version of the susceptible,exposed,infected,recovered type epidemic models, which can be fitted to observed disease incidence time series. We describe a method for reconstructing the dynamics of the susceptible class, which is an unobserved state variable of the dynamical system. The model provides a remarkable fit to the data on case reports of measles in England and Wales from 1944 to 1964. Morever, its systematic part explains the well-documented predominant biennial cyclic pattern. We study the dynamic behaviour of the time series model and show that episodes of annual cyclicity, which have not previously been explained quantitatively, arise as a response to a quicker replenishment of the susceptible class during the baby boom, around 1947. [source]

    ASSOCIATIONS BETWEEN AIR POLLUTION AND HOSPITAL VISITS FOR CARDIOVASCULAR DISEASES IN THE ELDERLY IN SYDNEY USING BAYESIAN STATISTICAL METHODS

    AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 3 2009
    Hiep Duc
    Summary Using generalized linear models (GLMs), Jalaludin,et al. (2006;,J. Exposure Analysis and Epidemiology,16, 225,237) studied the association between the daily number of visits to emergency departments for cardiovascular disease by the elderly (65+) and five measures of ambient air pollution. Bayesian methods provide an alternative approach to classical time series modelling and are starting to be more widely used. This paper considers Bayesian methods using the dataset used by Jalaludin,et al.,(2006), and compares the results from Bayesian methods with those obtained by Jalaludin,et al.,(2006) using GLM methods. [source]

    THE J CURVE: CHINA VERSUS HER TRADING PARTNERS

    BULLETIN OF ECONOMIC RESEARCH, Issue 4 2006
    Mohsen Bahmani-Oskooee
    F31 ABSTRACT The short-run effects of currency depreciation are said to be different from its long-run effects. In the short run, the trade balance deteriorates and improvement comes after some time; hence, the J-curve phenomenon. Previous studies that tested the response of the trade balance to exchange rate changes in China employed aggregate trade data and provided mixed results. Indeed, most of them concluded that real depreciation has no long-run impact on the Chinese trade balance. In this paper, we disaggregate the data by country and using recent advances in time series modelling estimate a trade balance model between China and her 13 major trading partners. We show that real depreciation of the Chinese currency has a favourable impact on her trade balance with a few partners, especially the USA. Not much support is found for the J-curve hypothesis. [source]