207 research outputs found

    Cross-cultural representations of musical shape

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    In cross-cultural research involving performers from distinct cultural backgrounds (U.K., Japan, Papua New Guinea), we examined 75 musicians' associations between musical sound and shape, and saw pronounced differences between groups. Participants heard short stimuli varying in pitch contour and were asked to represent these visually on paper, with the instruction that if another community member saw the marks they should be able to connect them with the sounds. Participants from the U.K. group produced consistent symbolic representations, which involved depicting the passage of time from left-to-right. Japanese participants unfamiliar with English language and western standard notation provided responses comparable to the U.K. group's. The majority opted to use a horizontal timeline, whilst a minority of traditional Japanese musicians produced unique responses with time represented vertically. The last group, a non-literate Papua New Guinean tribe known as BenaBena, produced a majority of iconic responses which did not follow the time versus pitch contour model, but highlighted musical qualities other than the parameters intentionally varied in the investigation, focusing on hue and loudness. The participants' responses point to profoundly different 'norms' of musical shape association, which may be linked to literacy and to the functional role of music in a community

    Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

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    We study the joint determination of the lag length, the dimension of the cointegrating space andthe rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using modelselection criteria. We consider model selection criteria which have data-dependent penalties for alack of parsimony, as well as the traditional ones. We suggest a new procedure which is a hybridof traditional criteria and criteria with data-dependant penalties. In order to compute the fit ofeach model, we propose an iterative procedure to compute the maximum likelihood estimates ofparameters of a VAR model with short-run and long-run restrictions. Our Monte Carlo simulationsmeasure the improvements in forecasting accuracy that can arise from the joint determination oflag-length and rank, relative to the commonly used procedure of selecting the lag-length only andthen testing for cointegration.

    Modelling Australian Domestic and International Inbound Travel: a Spatial-Temporal Approach

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    In this paper Australian domestic and international inbound travel are modelled by an anisotropic dynamic spatial lag panel Origin-Destination (OD) travel flow model. Spatial OD travel flow models have traditionally been applied in a single cross-sectional context, where the spatial structure is assumed to have reached its long run equilibrium and temporal dynamics are not explicitly considered. On the other hand, spatial effects are rarely accounted for in traditional tourism demand modelling. We attempt to address this dichotomy between spatial modelling and time series modelling in tourism research by using a spatial-temporal model. In particular, tourism behaviour is modelled as travel flows between regions. Temporal dependencies are accounted for via the inclusion of autoregressive components, while spatial autocorrelations are explicitly accounted for at both the origin and the destination. We allow the strength of spatial autocorrelation to exhibit seasonal variations, and we allow for the possibility of asymmetry between capital-city neighbours and non-capital-city neighbours. Significant spatial dynamics have been uncovered, which lead to some interesting policy implications.Tourism demand, Dynamic panel models, Travel flow model.

    Statistical Inference on Changes in Income Inequality in Australia

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    This paper studies the changes in income inequality of individuals in Australia between 1986 and 1999. Individuals are divided into various subgroups along several dimensions, such as region of residence, age, employment status etc. The changes in inequality over time, between and within the various subgroups is studied, and the bootstrap method is used to establish whether these changes are statistically significant.Income inequality; Gini coefficient; Theil inequality measure; bootstrap.

    VARMA versus VAR for Macroeconomic Forecasting

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    In this paper, we argue that there is no compelling reason for restricting the class of multivariate models considered for macroeconomic forecasting to VARs given the recent advances in VARMA modelling methodology and improvements in computing power. To support this claim, we use real macroeconomic data and show that VARMA models forecast macroeconomic variables more accurately than VAR models.Forecasting, Identification, Multivariate time series, Scalar components, VARMA models.

    A Complete VARMA Modelling Methodology Based on Scalar Components

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    This paper proposes an extension to scalar component methodology for the identification and estimation of VARMA models. The complete methodology determines the exact positions of all free parameters in any VARMA model with a predetermined embedded scalar component structure. This leads to an exactly identified system of equations that is estimated using full information maximum likelihood.Identification, Multivariate time series, Scalar components, VARMA models.

    Multivariate exponential smoothing for forecasting tourist arrivals to Australia and New Zealand

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    In this paper we propose a new set of multivariate stochastic models that capture time varying seasonality within the vector innovations structural time series (VISTS) framework. These models encapsulate exponential smoothing methods in a multivariate setting. The models considered are the local level, local trend and damped trend VISTS models with an additive multivariate seasonal component. We evaluate their performances for forecasting international tourist arrivals from eleven source countries to Australia and New Zealand.Holt-Winters’ method, Stochastic seasonality, Vector innovations state space models.

    The value of feedback in forecasting competitions

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    In this paper we challenge the traditional design used for forecasting competitions. We implement an online competition with a public leaderboard that provides instant feedback to competitors who are allowed to revise and resubmit forecasts. The results show that feedback significantly improves forecasting accuracy.Forecasting competition, feedback.

    Modelling and forecasting Australian domestic tourism

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    In this paper, we model and forecast Australian domestic tourism demand. We use a regression framework to estimate important economic relationships for domestic tourism demand. We also identify the impact of world events such as the 2000 Sydney Olympics and the 2002 Bali bombings on Australian domestic tourism. To explore the time series nature of the data, we use innovation state space models to forecast the domestic tourism demand. Combining these two frameworks, we build innovation state space models with exogenous variables. These models are able to capture the time series dynamics in the data, as well as economic and other relationships. We show that these models outperform alternative approaches for short-term forecasting and also produce sensible long-term forecasts. The forecasts are compared with the official Australian government forecasts, which are found to be more optimistic than our forecasts.Australia, domestic tourism, exponential smoothing, forecasting, innovation state space models.

    Nonlinear Autoregresssive Leading Indicator Models of Output in G-7 Countries

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    This paper studies linear and nonlinear autoregressive leading indicator models of business cycles in G7 countries. The models use the spread between short-term and long-term interest rates as leading indicators for GDP, and their success in capturing business cycles is gauged by non-parametric shape tests, and their ability to predict the probability of recession. We find that bivariate nonlinear models of output and the interest rate spread can successfully capture the shape of the business cycle in cases where linear models fail. Also, our nonlinear leading indicator models for USA, Canada and the UK outperform other models of GDP with respect to predicting the probability of recession.Business Cycles, Leading Indicators, Model Evaluation, Nonlinear Models, Yield Spreads.
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