Sensitivity and out-of-sample error in continuous time data assimilation

Data assimilation refers to the problem of finding trajectories of a prescribed dynamical model in such a way that the output of the model (usually some function of the model states) follows a given time series of observations. Typically though, these two requirements cannot both be met at the same time–tracking the observations is not possible without the trajectory deviating from the proposed model equations, while adherence to the model requires deviations from the observations. Thus, data assimilation faces a trade-off. In this contribution, the sensitivity of the data assimilation with respect to perturbations in the observations is identified as the parameter which controls the trade-off. A relation between the sensitivity and the out-of-sample error is established, which allows the latter to be calculated under operational conditions. A minimum out-of-sample error is proposed as a criterion to set an appropriate sensitivity and to settle the discussed trade-off. Two approaches to data assimilation are considered, namely variational data assimilation and Newtonian nudging, also known as synchronization. Numerical examples demonstrate the feasibility of the approach.

Finite sample weighting of recursive forecast errors

This paper proposes and tests a new framework for weighting recursive out-of-sample prediction errors according to their corresponding levels of in-sample estimation uncertainty. In essence, we show how to use the maximum possible amount of information from the sample in the evaluation of the prediction accuracy, by commencing the forecasts at the earliest opportunity and weighting the prediction errors. Via a Monte Carlo study, we demonstrate that the proposed framework selects the correct model from a set of candidate models considerably more often than the existing standard approach when only a small sample is available. We also show that the proposed weighting approaches result in tests of equal predictive accuracy that have much better sizes than the standard approach. An application to an exchange rate dataset highlights relevant differences in the results of tests of predictive accuracy based on the standard approach versus the framework proposed in this paper.

Forecasting exchange rate volatility using conditional variance models selected by information criteria

This paper uses appropriately modified information criteria to select models from the GARCH family, which are subsequently used for predicting US dollar exchange rate return volatility. The out of sample forecast accuracy of models chosen in this manner compares favourably on mean absolute error grounds, although less favourably on mean squared error grounds, with those generated by the commonly used GARCH(1, 1) model. An examination of the orders of models selected by the criteria reveals that (1, 1) models are typically selected less than 20% of the time.

Superensembles of linear viscoelastic models of polymer melts

The idea of incorporating multiple models of linear rheology into a superensemble, to forge a consensus forecast from the individual model predictions, is investigated. The relative importance of the individual models in the so-called multimodel superensemble (MMSE) was inferred by evaluating their performance on a set of experimental training data, via nonlinear regression. The predictive ability of the MMSE model was tested by comparing its predictions on test data that were similar (in-sample) and dissimilar (out-of-sample) to the training data used in the calibration. For the in-sample forecasts, we found that the MMSE model easily outperformed the best constituent model. The presence of good individual models greatly enhanced the MMSE forecast, while the presence of some bad models in the superensemble also improved the MMSE forecast modestly. While the performance of the MMSE model on the out-of-sample training data was not as spectacular, it demonstrated the robustness of this approach.

Drivers of in-group and out-of-group electronic word-of-mouth (eWOM)

Purpose – The purpose of this study is to address a recent call for additional research on electronic word-of-mouth (eWOM). In response to this call, this study draws on the social network paradigm and the uses and gratification theory (UGT) to propose and empirically test a conceptual framework of key drivers of two types of eWOM, namely in-group and out-of-group. Design/methodology/approach – The proposed model, which examines the impact of usage motivations on eWOM in-group and eWOM out-of-group, is tested in a sample of 302 internet users in Portugal. Findings – Results from the survey show that the different drivers (i.e. mood-enhancement, escapism, experiential learning and social interaction) vary in terms of their impact on the two different types of eWOM. Surprisingly, while results show a positive relationship between experiential learning and eWOM out-of-group, no relationship is found between experiential learning and eWOM in-group. Research limitations/implications – This is the first study investigating the drivers of both eWOM in-group and eWOM out-of-group. Additional research in this area will contribute to the development of a general theory of eWOM. Practical implications – By understanding the drivers of different eWOM types, this study provides guidance to marketing managers on how to allocate resources more efficiently in order to achieve the company's strategic objectives. Originality/value – No published study has investigated the determinants of these two types of eWOM. This is the first study offering empirical considerations of how the various drivers differentially impact eWOM in-group and eWOM out-of-group.

Forecasting US output growth with non-linear models in the presence of data uncertainty

We consider the impact of data revisions on the forecast performance of a SETAR regime-switching model of U.S. output growth. The impact of data uncertainty in real-time forecasting will affect a model's forecast performance via the effect on the model parameter estimates as well as via the forecast being conditioned on data measured with error. We find that benchmark revisions do affect the performance of the non-linear model of the growth rate, and that the performance relative to a linear comparator deteriorates in real-time compared to a pseudo out-of-sample forecasting exercise.

Drivers of in‐group and out‐of‐group electronic word‐of‐mouth (eWOM)

Purpose– The purpose of this study is to address a recent call for additional research on electronic word‐of‐mouth (eWOM). In response to this call, this study draws on the social network paradigm and the uses and gratification theory (UGT) to propose and empirically test a conceptual framework of key drivers of two types of eWOM, namely in‐group and out‐of‐group. Design/methodology/approach– The proposed model, which examines the impact of usage motivations on eWOM in‐group and eWOM out‐of‐group, is tested in a sample of 302 internet users in Portugal. Findings– Results from the survey show that the different drivers (i.e. mood‐enhancement, escapism, experiential learning and social interaction) vary in terms of their impact on the two different types of eWOM. Surprisingly, while results show a positive relationship between experiential learning and eWOM out‐of‐group, no relationship is found between experiential learning and eWOM in‐group. Research limitations/implications– This is the first study investigating the drivers of both eWOM in‐group and eWOM out‐of‐group. Additional research in this area will contribute to the development of a general theory of eWOM. Practical implications– By understanding the drivers of different eWOM types, this study provides guidance to marketing managers on how to allocate resources more efficiently in order to achieve the company's strategic objectives. Originality/value– No published study has investigated the determinants of these two types of eWOM. This is the first study offering empirical considerations of how the various drivers differentially impact eWOM in‐group and eWOM out‐of‐group.

Real-time factor model forecasting and the effects of instability

Factor forecasting models are shown to deliver real-time gains over autoregressive models for US real activity variables during the recent period, but are less successful for nominal variables. The gains are largely due to the Financial Crisis period, and are primarily at the shortest (one quarter ahead) horizon. Excluding the pre-Great Moderation years from the factor forecasting model estimation period (but not from the data used to extract factors) results in a marked fillip in factor model forecast accuracy, but does the same for the AR model forecasts. The relative performance of the factor models compared to the AR models is largely unaffected by whether the exercise is in real time or is pseudo out-of-sample.

Did they talk their way out of Africa?

Corballis suggests that fully vocal communication was invented by modern humans between 170,000 and 50,000 years ago. Because this new form of communication did not require hand gestures, he wondered whether this may have facilitated the development of lithic manufacture. I cast doubt on this interesting notion but offer an enhanced version that may have more potential.