Distribution by Scientific Domains

Kinds of Autoregressive

  • threshold autoregressive
  • vector autoregressive

  • Terms modified by Autoregressive

  • autoregressive distributed lag
  • autoregressive integrate moving average
  • autoregressive model
  • autoregressive models
  • autoregressive moving average
  • autoregressive parameter
  • autoregressive process

  • Selected Abstracts

    Identification of Time-Variant Modal Parameters Using Time-Varying Autoregressive with Exogenous Input and Low-Order Polynomial Function

    C. S. Huang
    By developing the equivalent relations between the equation of motion of a time-varying structural system and the TVARX model, this work proves that instantaneous modal parameters of a time-varying system can be directly estimated from the TVARX model coefficients established from displacement responses. A moving least-squares technique incorporating polynomial basis functions is adopted to approximate the coefficient functions of the TVARX model. The coefficient functions of the TVARX model are represented by polynomials having time-dependent coefficients, instead of constant coefficients as in traditional basis function expansion approaches, so that only low orders of polynomial basis functions are needed. Numerical studies are carried out to investigate the effects of parameters in the proposed approach on accurately determining instantaneous modal parameters. Numerical analyses also demonstrate that the proposed approach is superior to some published techniques (i.e., recursive technique with a forgetting factor, traditional basis function expansion approach, and weighted basis function expansion approach) in accurately estimating instantaneous modal parameters of a structure. Finally, the proposed approach is applied to process measured data for a frame specimen subjected to a series of base excitations in shaking table tests. The specimen was damaged during testing. The identified instantaneous modal parameters are consistent with observed physical phenomena. [source]

    Analysis of determinants of mammalian species richness in South America using spatial autoregressive models

    ECOGRAPHY, Issue 4 2004
    Marcelo F. Tognelli
    Classically, hypotheses concerning the distribution of species have been explored by evaluating the relationship between species richness and environmental variables using ordinary least squares (OLS) regression. However, environmental and ecological data generally show spatial autocorrelation, thus violating the assumption of independently distributed errors. When spatial autocorrelation exists, an alternative is to use autoregressive models that assume spatially autocorrelated errors. We examined the relationship between mammalian species richness in South America and environmental variables, thereby evaluating the relative importance of four competing hypotheses to explain mammalian species richness. Additionally, we compared the results of ordinary least squares (OLS) regression and spatial autoregressive models using Conditional and Simultaneous Autoregressive (CAR and SAR, respectively) models. Variables associated with productivity were the most important at determining mammalian species richness at the scale analyzed. Whereas OLS residuals between species richness and environmental variables were strongly autocorrelated, those from autoregressive models showed less spatial autocorrelation, particularly the SAR model, indicating its suitability for these data. Autoregressive models also fit the data better than the OLS model (increasing R2 by 5,14%), and the relative importance of the explanatory variables shifted under CAR and SAR models. These analyses underscore the importance of controlling for spatial autocorrelation in biogeographical studies. [source]

    Robust estimation of critical values for genome scans to detect linkage

    Silviu-Alin BacanuArticle first published online: 15 SEP 200
    Abstract Estimation of study specific critical values for linkage scans (suggestive and significant thresholds) is important to identify promising regions. In this report, I propose a fast and concrete recipe for finding study specific critical values. Previously, critical values were derived theoretically or empirically. Theoretically-derived values are often conservative due to their assumption of fully informative transmissions. Empirically-derived critical values are computer and skill intensive and may not even be computationally feasible for large pedigrees. In this report, I propose a method to estimate critical values for multipoint linkage analysis using standard, widely used statistical software. The proposed method estimates study-specific critical values by using Autoregressive (AR) models to estimate the correlation between standard normal statistics at adjacent map points and then use this correlation to estimate study-specific critical values. The AR-based method is evaluated using different family structures and density of markers, under both the null hypothesis of no linkage and the alternative hypothesis of linkage between marker and disease locus. Simulations results show the AR-based method accurately predicts critical values for a wide range of study designs. © 2004 Wiley-Liss, Inc. [source]

    Local environmental effects and spatial effects in macroecological studies using mapped abundance classes: the case of the rook Corvus frugilegus in Scotland

    Summary 1The study of the spatial pattern of species abundance is complicated by statistical problems, such as spatial autocorrelation of the abundance data, which lead to the confusion of environmental effects and dispersal. 2Atlas-derived data for the rook in Scotland are used as a case study to propose an approach for assessing the likely contribution of dispersal and local environmental effects, based on a Bayesian Conditional Autoregressive (CAR) approach. 3The availability of moist grasslands is a key factor explaining the spatial pattern of abundance. This is influenced by a combination of climatic and soil-related factors. A direct link to soil properties is for the first time reported for the wide-scale distribution of a bird species. In addition, for this species, dispersal seems to contribute significantly to the spatial pattern and produces a smoother than expected decline in abundance at the north-western edge of its distribution range. Areas where dispersal is most likely to be important are highlighted. 4The approach described can help ecologists make more efficient use of atlas data for the investigation of the structure of species abundance, and can highlight potential sink areas at the landscape and regional scale. 5Bayesian spatial models can deal with data autocorrelation in atlas-type data, while clearly communicating uncertainty through the estimation of the full posterior probability distribution of all parameters. [source]

    Arbitrage Bounds and the Time Series Properties of the Discount on UK Closed-End Mutual Funds

    Laurence Copeland
    Abstract:, In a dataset of weekly observations over the period since 1990, the discount on UK closed-end mutual funds is shown to be nonstationary, but reverting to a nonzero long run mean. Although the long run discount could be explained by factors like management expenses etc., its short run fluctuations are harder to reconcile with an arbitrage-free equilibrium. In time series terms, there is evidence of long memory in discounts consistent with a bounded random walk. This conclusion is supported by explicit nonlinearity tests, and by results which suggest the behaviour of the discount is perhaps best represented by one of the class of Smooth-Transition Autoregressive (STAR) models. [source]

    Performance evaluation of the New Connecticut Leading Employment Index using lead profiles and BVAR models

    Anirvan Banerji
    Abstract The leading and coincident employment indexes for the state of Connecticut developed following the recession of the early 1990s fell short of expectations. This paper performs two tasks. First, it describes the process of revising the Connecticut Coincident and Leading Employment Indexes. Second, it analyzes the statistical properties and performance of the new indexes by comparing the lead profiles of the new and old indexes as well as their out-of-sample forecasting performance, using the Bayesian Vector Autoregressive (BVAR) method. The new coincident index shows improved performance in dating employment cycle chronologies. The lead profile test demonstrates that superiority in a rigorous, non-parametric statistic fashion. The mixed evidence on the BVAR forecasting experiments illustrates that leading indexes properly predict cycle turning points and do not necessarily provide accurate forecasts except at turning points, a view that our results support.,,Copyright © 2006 John Wiley & Sons, Ltd. [source]

    Expectations Formation and Business Cycle Fluctuations: An Empirical Analysis of Actual and Expected Output in UK Manufacturing, 1975,1996

    Kevin Lee
    Direct measures of expectations, derived from survey data, are used in a Vector Autoregressive (VAR) model of actual and expected output in eight industries in the UK manufacturing sector. No evidence is found with which to reject rationality in the derived expectations series when measurement error is appropriately taken into account. The VAR analysis illustrates the importance of intersectoral interactions and business confidence in explaining the time profile of industrial outputs, examines the mechanisms by which shocks are propagated across sectors and over time and investigates the relative importance of sectoral and aggregate shocks of different types. [source]

    Threshold Dynamics of Short-term Interest Rates: Empirical Evidence and Implications for the Term Structure

    ECONOMIC NOTES, Issue 1 2008
    Theofanis Archontakis
    This paper studies a nonlinear one-factor term structure model in discrete time. The short-term interest rate follows a self-exciting threshold autoregressive (SETAR) process that allows for shifts in the intercept and the variance. In comparison with a linear model, we find empirical evidence in favour of the threshold model for Germany and the US. Based on the estimated short-rate dynamics we derive the implied arbitrage-free term structure of interest rates. Since analytical solutions are not feasible, bond prices are computed by means of Monte Carlo integration. The resulting term structure captures stylized facts of the data. In particular, it implies a nonlinear relation between long rates and the short rate. [source]

    Modeling monthly temperature data in Lisbon and Prague

    ENVIRONMETRICS, Issue 7 2009
    Teresa Alpuim
    Abstract This paper examines monthly average temperature series in two widely separated European cities, Lisbon (1856,1999) and Prague (1841,2000). The statistical methodology used begins by fitting a straight line to the temperature measurements in each month of the year. Hence, the 12 intercepts describe the seasonal variation of temperature and the 12 slopes correspond to the rise in temperature in each month of the year. Both cities show large variations in the monthly slopes. In view of this, an overall model is constructed to integrate the data of each city. Sine/cosine waves were included as independent variables to describe the seasonal pattern of temperature, and sine/cosine waves multiplied by time were used to describe the increase in temperature corresponding to the different months. The model also takes into account the autoregressive, AR(1), structure that was found in the residuals. A test of the significance of the variables that describe the variation of the increase in temperature shows that both Lisbon and Prague had an increase in temperature that is different according to the month. The winter months show a higher increase than the summer months. Copyright © 2009 John Wiley & Sons, Ltd. [source]

    Space,time zero-inflated count models of Harbor seals,

    ENVIRONMETRICS, Issue 7 2007
    Jay M. Ver Hoef
    Abstract Environmental data are spatial, temporal, and often come with many zeros. In this paper, we included space-time random effects in zero-inflated Poisson (ZIP) and ,hurdle' models to investigate haulout patterns of harbor seals on glacial ice. The data consisted of counts, for 18 dates on a lattice grid of samples, of harbor seals hauled out on glacial ice in Disenchantment Bay, near Yakutat, Alaska. A hurdle model is similar to a ZIP model except it does not mix zeros from the binary and count processes. Both models can be used for zero-inflated data, and we compared space-time ZIP and hurdle models in a Bayesian hierarchical model. Space-time ZIP and hurdle models were constructed by using spatial conditional autoregressive (CAR) models and temporal first-order autoregressive (AR(1)) models as random effects in ZIP and hurdle regression models. We created maps of smoothed predictions for harbor seal counts based on ice density, other covariates, and spatio-temporal random effects. For both models predictions around the edges appeared to be positively biased. The linex loss function is an asymmetric loss function that penalizes overprediction more than underprediction, and we used it to correct for prediction bias to get the best map for space-time ZIP and hurdle models. Published in 2007 by John Wiley & Sons, Ltd. [source]

    Multi-step forecasting for nonlinear models of high frequency ground ozone data: a Monte Carlo approach

    ENVIRONMETRICS, Issue 4 2002
    Alessandro Fassò
    Abstract Multi-step prediction using high frequency environmental data is considered. The complex dynamics of ground ozone often requires models involving covariates, multiple frequency periodicities, long memory, nonlinearity and heteroscedasticity. For these reasons parametric models, which include seasonal fractionally integrated components, self-exciting threshold autoregressive components, covariates and autoregressive conditionally heteroscedastic errors with heavy tails, have been recently introduced. Here, to obtain an h step ahead forecast for these models we use a Monte Carlo approach. The performance of the forecast is evaluated on different nonlinear models comparing some statistical indices with respect to the prediction horizon. As an application of this method, the forecast precision of a 2 year hourly ozone data set coming from an air traffic pollution station located in Bergamo, Italy, is analyzed. Copyright © 2002 John Wiley & Sons, Ltd. [source]

    Feature extraction by autoregressive spectral analysis using maximum likelihood estimation: internal carotid arterial Doppler signals

    EXPERT SYSTEMS, Issue 4 2008
    Elif Derya Übeyli
    Abstract: In this study, Doppler signals recorded from the internal carotid artery (ICA) of 97 subjects were processed by personal computer using classical and model-based methods. Fast Fourier transform (classical method) and autoregressive (model-based method) methods were selected for processing the ICA Doppler signals. The parameters in the autoregressive method were found by using maximum likelihood estimation. The Doppler power spectra of the ICA Doppler signals were obtained by using these spectral analysis techniques. The variations in the shape of the Doppler spectra as a function of time were presented in the form of sonograms in order to obtain medical information. These Doppler spectra and sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of stenosis and occlusion in the ICA. Reliable information on haemodynamic alterations in the ICA can be obtained by evaluation of these sonograms. [source]

    Remote Monitoring Integrated State Variables for AR Model Prediction of Daily Total Building Air-Conditioning Power Consumption

    Chuzo Ninagawa Member
    Abstract It is extremely difficult to predict daily accumulated power consumption of the entire building air-conditioning facilities because of a huge number of variables. We propose new integrated state variables, i.e. the daily operation amount and the daily operation-capacity-weighted average set temperature. Taking advantage of a remote monitoring technology, time series data of the integrated state variables were collected and an autoregressive (AR) model prediction for the daily total power consumption has been tried. © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source]

    Identification of autoregressive models in the presence of additive noise

    Roberto Diversi
    Abstract A common approach in modeling signals in many engineering applications consists in adopting autoregressive (AR) models, consisting in filters with transfer functions having a unitary numerator, driven by white noise. Despite their wide application, these models do not take into account the possible presence of errors on the observations and cannot prove accurate when these errors are significant. AR plus noise models constitute an extension of AR models that consider also the presence of an observation noise. This paper describes a new algorithm for the identification of AR plus noise models that is characterized by a very good compromise between accuracy and efficiency. This algorithm, taking advantage of both low and high-order Yule,Walker equations, also guarantees the positive definiteness of the autocorrelation matrix of the estimated process and allows to estimate the equation error and observation noise variances. It is also shown how the proposed procedure can be used for estimating the order of the AR model. The new algorithm is compared with some traditional algorithms by means of Monte Carlo simulations. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Cyclicity analysis of precipitation regimes in the Yangtze River basin, China

    S. Becker
    Abstract Daily precipitation data of 148 weather stations located in the Yangtze River basin (P.R. China) are analysed to detect cycles in the annual frequency of occurrence of precipitation events of 1-, 5- and 10 days duration. These events were defined in terms of exceedances of some selected thresholds. The events corresponding to 10, 25 and 30 mm thresholds for 1-, 5- and 10-day precipitation totals, respectively, are analysed in detail. For the identification of cycles, basin-wide averaged standardized time series of frequency of precipitation events are used. It is found that peaks in the smoothed time series occurred around 1974, 1982 and 1991. The Fourier, autoregressive and wavelet analyses reveal distinct cycles of 7,9 and 2,3 year periods, which dominate large parts of the time series. In addition, a shift towards a 4,5 year period in the annual frequency of precipitation events is noticed since the mid- to late-nineties. Major peaks in the annual frequency of occurrence of precipitation events are expected to occur around 2012, 2015 and 2018 according to the spectrum analyses. Copyright © 2007 Royal Meteorological Society [source]

    Global analysis of runs of annual precipitation and runoff equal to or below the median: run length

    Murray C. Peel
    Abstract The investigation of fluctuations of wet and dry years has a long history in the climatology and hydrology literature. In this, the first of two papers investigating runs of consecutive dry years, the lengths (persistence) of dry runs are investigated. In the second paper the magnitude/intensity and severity (length × magnitude) of dry runs will be investigated. Consecutive dry years are associated with drought, which is a significant physical and economic phenomenon that imposes great stress on ecosystems and societies. Run lengths of consecutive years equal to or below the median were analysed for 3863 precipitation and 1236 runoff stations from around the world. Run lengths were found to be similar across all continents and Köppen climate zones, expect for tropical and arid North Africa (Sahel), which showed a distinct bias toward longer run lengths than any other region of the world. Generally, the run length observed in annual runoff was found to be similar to that observed in annual precipitation for the same location. Both annual precipitation and runoff data were found to be well described by the lag-one autoregressive (AR(1)) model or by white noise. The influence of the El Niño,southern oscillation on run lengths was not observed to be significant. The presence of decadal and multi-decadal oscillations was weakly observed in the results of the precipitation runs analysis. The faintness of the decadal and multi-decadal oscillation signal may be due to the sample sizes not being long enough and/or the runs analysis not being sensitive enough to detect their presence. Copyright © 2004 Royal Meteorological Society [source]

    Patterns of Transnational Terrorism, 1970,1999: Alternative Time-Series Estimates

    Walter Enders
    Using alternative time-series methods, this paper investigates the patterns of transnational terrorist incidents that involve one or more deaths. Initially, an updated analysis of these fatal events for 1970,1999 is presented using a standard linear model with prespecified interventions that represent significant policy and political impacts. Next, a (regime-switching) threshold autoregressive (TAR) model is applied to this fatality time series. TAR estimates indicate that increases above the mean are not sustainable during high-activity eras, but are sustainable during low-activity eras. The TAR model provides a better fit than previously tried methods for the fatality time series. By applying a Fourier approximation to the nonlinear estimates, we get improved results. The findings in this study and those in our earlier studies are then applied to suggest some policy implications in light of the tragic attacks on the World Trade Center and the Pentagon on September 11, 2001. [source]

    Numerical fluctuations in the northern short-tailed shrew: evidence of non-linear feedback signatures on population dynamics and demography

    Mauricio Lima
    Summary 1,We studied a fluctuating population of the northern short-tailed shrew (Blarina brevicauda) in the Appalachian Plateau Province of Pennsylvania, USA, spanning 21 years of monitoring. We analysed the pattern of annual temporal variation fitting both time-series models and capture,mark,recapture (CMR) statistical models for survival and recruitment rates. 2,We determined that non-linear first-order models explain almost 80% of the variation in annual per capita population growth rates. In particular, a non-linear self-excited threshold autoregressive (SETAR) model describes the time-series data well. Average snowfall showed positive and non-linear effects on population dynamics. 3,The CMR statistical models showed that a non-linear threshold model with strong effects of population density was the best one to describe temporal variation in survival rates. On the other hand, population density or climatic variables did not explain temporal variation in recruitment rates. Survival rates were high during the study period. Weekly changes in population size attributable to new recruits entering in the population fluctuate between 21% and 0%, while the changes in population size related to survival fluctuate between 79% and 100%. 4,Two important results arise from this study. First, non-linear models with first-order feedback appear to capture the essential features of northern short-tailed shrew dynamics and demography. Secondly, climate effects represented by snowfall appear to be small and non-linear on this insectivore. The population dynamics of this shrew in the Appalachian Plateau are determined apparently by a strong non-linear first-order feedback process, which is related to survival rates. 5,This study links population dynamics and demography by detecting the underlying demographic mechanisms driving population dynamics. The feedback structure of this shrew suggests the existence of population dynamics dominated by intraspecific competitive interactions, such as aggression, solitary nesting, non-overlapping home ranges and territoriality. [source]

    Forecasting realized volatility: a Bayesian model-averaging approach

    Chun Liu
    How to measure and model volatility is an important issue in finance. Recent research uses high-frequency intraday data to construct ex post measures of daily volatility. This paper uses a Bayesian model-averaging approach to forecast realized volatility. Candidate models include autoregressive and heterogeneous autoregressive specifications based on the logarithm of realized volatility, realized power variation, realized bipower variation, a jump and an asymmetric term. Applied to equity and exchange rate volatility over several forecast horizons, Bayesian model averaging provides very competitive density forecasts and modest improvements in point forecasts compared to benchmark models. We discuss the reasons for this, including the importance of using realized power variation as a predictor. Bayesian model averaging provides further improvements to density forecasts when we move away from linear models and average over specifications that allow for GARCH effects in the innovations to log-volatility. Copyright © 2009 John Wiley & Sons, Ltd. [source]

    Effects of habitat history and extinction selectivity on species-richness patterns of an island land snail fauna

    Satoshi Chiba
    Abstract Aim, Local-scale diversity patterns are not necessarily regulated by contemporary processes, but may be the result of historical events such as habitat changes and selective extinctions that occurred in the past. We test this hypothesis by examining species-richness patterns of the land snail fauna on an oceanic island where forest was once destroyed but subsequently recovered. Location, Hahajima Island of the Ogasawara Islands in the western Pacific. Methods, Species richness of land snails was examined in 217 0.25 × 0.25 km squares during 1990,91 and 2005,07. Associations of species richness with elevation, current habitat quality (proportion of habitat composed of indigenous trees and uncultivated areas), number of alien snail species, and proportion of forest loss before 1945 in each area were examined using a randomization test and simultaneous autoregressive (SAR) models. Extinctions in each area and on the entire island were detected by comparing 2005,07 records with 1990,91 records and previously published records from surveys in 1987,91 and 1901,07. The association of species extinction with snail ecotype and the above environmental factors was examined using a spatial generalized linear mixed model (GLMM). Results, The level of habitat loss before 1945 explained the greatest proportion of variation in the geographical patterns of species richness. Current species richness was positively correlated with elevation in the arboreal species, whereas it was negatively correlated with elevation in the ground-dwelling species. However, no or a positive correlation was found between elevation and richness of the ground-dwelling species in 1987,91. The change of the association with elevation in the ground-dwelling species was caused by greater recent extinction at higher elevation, possibly as a result of predation by malacophagous flatworms. In contrast, very minor extinction levels have occurred in arboreal species since 1987,91, and their original patterns have remained unaltered, mainly because flatworms do not climb trees. Main conclusions, The species-richness patterns of the land snails on Hahajima Island are mosaics shaped by extinction resulting from habitat loss more than 60 years ago, recent selective extinction, and original faunal patterns. The effects of habitat destruction have remained long after habitat recovery. Different factors have operated during different periods and at different time-scales. These findings suggest that historical processes should be taken into account when considering local-scale diversity patterns. [source]

    Direct Evidence of Non-trading on the London Stock Exchange

    Andrew Clare
    The extent of non-trading is shown to be much greater in the UK than in the more heavily researched US equity markets. Over the period 1975 to 1995 we find that almost 44% of all stocks in our sample failed to trade on the last day of a given month, a figure which is significantly higher than for stocks in the US (see Foerster and Keim, 1993). In this paper we investigate the relationship between the non-trading of UK stocks and the autoregressive and seasonal behaviour of UK stock returns. In addition, we find that stocks are much more likely to be recorded as not having traded on the last day of the month in the period prior to April 1981 than after this date. We trace this result to a reporting requirement change on the London Stock Exchange and investigate whether the change has any real implications for systematic risk estimates over this period. We also find that alternative methods for calculating betas, in the presence of thin trading, are very sensitive to stock size and to non-trading. [source]

    Estimation and forecasting in first-order vector autoregressions with near to unit roots and conditional heteroscedasticity

    Theologos Pantelidis
    Abstract This paper investigates the effects of imposing invalid cointegration restrictions or ignoring valid ones on the estimation, testing and forecasting properties of the bivariate, first-order, vector autoregressive (VAR(1)) model. We first consider nearly cointegrated VARs, that is, stable systems whose largest root, lmax, lies in the neighborhood of unity, while the other root, lmin, is safely smaller than unity. In this context, we define the ,forecast cost of type I' to be the deterioration in the forecasting accuracy of the VAR model due to the imposition of invalid cointegration restrictions. However, there are cases where misspecification arises for the opposite reasons, namely from ignoring cointegration when the true process is, in fact, cointegrated. Such cases can arise when lmax equals unity and lmin is less than but near to unity. The effects of this type of misspecification on forecasting will be referred to as ,forecast cost of type II'. By means of Monte Carlo simulations, we measure both types of forecast cost in actual situations, where the researcher is led (or misled) by the usual unit root tests in choosing the unit root structure of the system. We consider VAR(1) processes driven by i.i.d. Gaussian or GARCH innovations. To distinguish between the effects of nonlinear dependence and those of leptokurtosis, we also consider processes driven by i.i.d. t(2) innovations. The simulation results reveal that the forecast cost of imposing invalid cointegration restrictions is substantial, especially for small samples. On the other hand, the forecast cost of ignoring valid cointegration restrictions is small but not negligible. In all the cases considered, both types of forecast cost increase with the intensity of GARCH effects. Copyright © 2009 John Wiley & Sons, Ltd. [source]

    Forecasting growth and inflation in an enlarged euro area

    Thomas Flavin
    Abstract We compare models for forecasting growth and inflation in the enlarged euro area. Forecasts are built from univariate autoregressive and single-equation models. The analysis is undertaken for both individual countries and EU aggregate variables. Aggregate forecasts are constructed by both employing aggregate variables and by aggregating country-specific forecasts. Using financial variables for country-specific forecasts tends to add little to the predictive ability of a simple AR model. However, they do help to predict EU aggregates. Furthermore, forecasts from pooling individual country models usually outperform those of the aggregate itself, particularly for the EU25 grouping. This is particularly interesting from the perspective of the European Central Bank, who require forecasts of economic activity and inflation to formulate appropriate economic policy across the enlarged group. Copyright © 2008 John Wiley & Sons, Ltd. [source]

    Testing for Granger (non-)causality in a time-varying coefficient VAR model

    Dimitris K. Christopoulos
    Abstract In this paper we propose Granger (non-)causality tests based on a VAR model allowing for time-varying coefficients. The functional form of the time-varying coefficients is a logistic smooth transition autoregressive (LSTAR) model using time as the transition variable. The model allows for testing Granger non-causality when the VAR is subject to a smooth break in the coefficients of the Granger causal variables. The proposed test then is applied to the money,output relationship using quarterly US data for the period 1952:2,2002:4. We find that causality from money to output becomes stronger after 1978:4 and the model is shown to have a good out-of-sample forecasting performance for output relative to a linear VAR model. Copyright © 2008 John Wiley & Sons, Ltd. [source]

    Can forecasting performance be improved by considering the steady state?

    An application to Swedish inflation, interest rate
    Abstract This paper investigates whether the forecasting performance of Bayesian autoregressive and vector autoregressive models can be improved by incorporating prior beliefs on the steady state of the time series in the system. Traditional methodology is compared to the new framework,in which a mean-adjusted form of the models is employed,by estimating the models on Swedish inflation and interest rate data from 1980 to 2004. Results show that the out-of-sample forecasting ability of the models is practically unchanged for inflation but significantly improved for the interest rate when informative prior distributions on the steady state are provided. The findings in this paper imply that this new methodology could be useful since it allows us to sharpen our forecasts in the presence of potential pitfalls such as near unit root processes and structural breaks, in particular when relying on small samples.,,Copyright © 2008 John Wiley & Sons, Ltd. [source]

    Forecasting interest rate swap spreads using domestic and international risk factors: evidence from linear and non-linear models

    Ilias Lekkos
    Abstract This paper explores the ability of factor models to predict the dynamics of US and UK interest rate swap spreads within a linear and a non-linear framework. We reject linearity for the US and UK swap spreads in favour of a regime-switching smooth transition vector autoregressive (STVAR) model, where the switching between regimes is controlled by the slope of the US term structure of interest rates. We compare the ability of the STVAR model to predict swap spreads with that of a non-linear nearest-neighbours model as well as that of linear AR and VAR models. We find some evidence that the non-linear models predict better than the linear ones. At short horizons, the nearest-neighbours (NN) model predicts better than the STVAR model US swap spreads in periods of increasing risk conditions and UK swap spreads in periods of decreasing risk conditions. At long horizons, the STVAR model increases its forecasting ability over the linear models, whereas the NN model does not outperform the rest of the models.,,Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Beating the random walk in Central and Eastern Europe

    Jesús Crespo Cuaresma
    Abstract We compare the accuracy of vector autoregressive (VAR), restricted vector autoregressive (RVAR), Bayesian vector autoregressive (BVAR), vector error correction (VEC) and Bayesian error correction (BVEC) models in forecasting the exchange rates of five Central and Eastern European currencies (Czech Koruna, Hungarian Forint, Slovak Koruna, Slovenian Tolar and Polish Zloty) against the US Dollar and the Euro. Although these models tend to outperform the random walk model for long-term predictions (6 months ahead and beyond), even the best models in terms of average prediction error fail to reject the test of equality of forecasting accuracy against the random walk model in short-term predictions. Copyright © 2005 John Wiley & Sons, Ltd. [source]

    Robustness of alternative non-linearity tests for SETAR models

    Wai-Sum Chan
    Abstract In recent years there has been a growing interest in exploiting potential forecast gains from the non-linear structure of self-exciting threshold autoregressive (SETAR) models. Statistical tests have been proposed in the literature to help analysts check for the presence of SETAR-type non-linearities in an observed time series. It is important to study the power and robustness properties of these tests since erroneous test results might lead to misspecified prediction problems. In this paper we investigate the robustness properties of several commonly used non-linearity tests. Both the robustness with respect to outlying observations and the robustness with respect to model specification are considered. The power comparison of these testing procedures is carried out using Monte Carlo simulation. The results indicate that all of the existing tests are not robust to outliers and model misspecification. Finally, an empirical application applies the statistical tests to stock market returns of the four little dragons (Hong Kong, South Korea, Singapore and Taiwan) in East Asia. The non-linearity tests fail to provide consistent conclusions most of the time. The results in this article stress the need for a more robust test for SETAR-type non-linearity in time series analysis and forecasting. Copyright © 2004 John Wiley & Sons, Ltd. [source]

    Bootstrap prediction intervals for autoregressive models of unknown or infinite lag order

    Jae H. Kim
    Abstract Recent studies on bootstrap prediction intervals for autoregressive (AR) model provide simulation findings when the lag order is known. In practical applications, however, the AR lag order is unknown or can even be infinite. This paper is concerned with prediction intervals for AR models of unknown or infinite lag order. Akaike's information criterion is used to estimate (approximate) the unknown (infinite) AR lag order. Small-sample properties of bootstrap and asymptotic prediction intervals are compared under both normal and non-normal innovations. Bootstrap prediction intervals are constructed based on the percentile and percentile- t methods, using the standard bootstrap as well as the bootstrap-after-bootstrap. It is found that bootstrap-after-bootstrap prediction intervals show small-sample properties substantially better than other alternatives, especially when the sample size is small and the model has a unit root or near-unit root. Copyright © 2002 John Wiley & Sons, Ltd. [source]

    Estimation in nonstationary random coefficient autoregressive models

    István Berkes
    Primary 62F05; secondary 62M10 Abstract., We investigate the estimation of parameters in the random coefficient autoregressive (RCA) model Xk = (, + bk)Xk,1 + ek, where (,, ,2, ,2) is the parameter of the process, , . We consider a nonstationary RCA process satisfying E log |, + b0| , 0 and show that ,2 cannot be estimated by the quasi-maximum likelihood method. The asymptotic normality of the quasi-maximum likelihood estimator for (,, ,2) is proven so that the unit root problem does not exist in the RCA model. [source]