Econometric Studies of Business Cycles in the History of Econometrics

This study examines the evolution of econometric research in business cycle analysis during the 1960-90 period. It shows how the research was dominated by an assimilation of the tradition of NBER business cycle analysis by the Haavelmo-Cowles Commission approach, catalysed by time-series statistical methods. Methodological consequences of the assimilation are critically evaluated in light of the meagre achievement of the research in predicting the current global recession.Business cycles, NBER, Forecasting

Exploring the Environmental Kuznets Hypothesis. Theoretical and Econometric Problems

Focussing on the prime example of CO2 emissions, we discuss several important theoretical and econometric problems that arise when studying environmental Kuznets curves (EKCs). The dominant theoretical approach is given by integrated assessment modelling, which consists of economic models that are combined with environmental impact models. We critically evaluate the aggregation, model dynamics and calibration aspects and their implications for the validity of the results. We then turn to a discussion of several important econometric problems that go almost unnoticed in the literature. The most fundamental problems relate to nonlinear transformations of nonstationary regressors and, in a nonstationary panel context, to neglected cross–sectional dependence. We discuss the implications of these two major and some minor problems that arise in the econometric analysis of Kuznets curves. Our discussion shows that EKC modelling as performed to date is subject to major drawbacks at both the theoretical and the econometric level.Carbon Kuznets curve, Integrated assessment models, Regressions with integrated variables, Nonstationary panels

Surface profile prediction and analysis applied to turning process

An approach for the prediction of surface profile in turning process using Radial Basis Function (RBF) neural networks is presented. The input parameters of the RBF networks are cutting speed, depth of cut and feed rate. The output parameters are Fast Fourier Transform (FFT) vector of surface profile for the prediction of surface profile. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. A very good performance of surface profile prediction, in terms of agreement with experimental data, was achieved with high accuracy, low cost and high speed. It is found that the RBF networks have the advantage over Back Propagation (BP) neural networks. Furthermore, a new group of training and testing data were also used to analyse the influence of tool wear and chip formation on prediction accuracy using RBF neural networks

Effect of flow pattern at pipe bends on corrosion behaviour of low carbon steek and its challenges

Recent design work regarding seawater flow lines has emphasized the need to identify, develop, and verify critical relationships between corrosion prediction and flow regime mechanisms at pipe bend. In practice this often reduces to an pragmatic interpretation of the effects of corrosion mechanisms at pipe bends. Most importantly the identification of positions or sites, within the internal surface contact areas where the maximum corrosion stimulus may be expected to occur, thereby allowing better understanding, mitigation, monitoring and corrosion control over the life cycle. Some case histories have been reviewed in this context, and the interaction between corrosion mechanisms and flow patterns closely determined, and in some cases correlated. Since the actual relationships are complex, it was determined that a risk based decision making process using selected ‘what’ if corrosion analyses linked to ‘what if’ flow assurance analyses was the best way forward. Using this in methodology, and pertinent field data exchange, it is postulated that significant improvements in corrosion prediction can be made. This paper outlines the approach used and shows how related corrosion modelling software data such as that available from corrosion models Norsok M5006, and Cassandra to parallel computational flow modelling in a targeted manner can generate very noteworthy results, and considerably more viable trends for corrosion control guidance. It is postulated that the normally associated lack of agreement between corrosion modelling and field experience, is more likely due to inadequate consideration of corrosion stimulating flow regime data, rather than limitations of the corrosion modelling. Applications of flow visualization studies as well as computations with the k-ε model of turbulence have identified flow features and regions where metal loss is a maximu

Forecasting environmental migration to the United Kingdom, 2010 - 2060: an exploration using Bayesian models

Over the next fifty years the potential impact on human livelihoods of environmental change could be considerable. One possible response may be increased levels of human mobility. This paper offers a first quantification of the levels of environmental migration to the United Kingdom that might be expected. The authors apply Bijak and Wi?niowski’s (2010) methodology for forecasting migration using Bayesian models. They seek to advance the conceptual understanding of forecasting in three ways. First, the paper is believed to be the first time that the Bayesian modelling approach has been attempted in relation to environmental mobility. Second, the paper examines the plausibility of Bayesian modelling of UK immigration by cross-checking expert responses to a Delphi survey with the expectations about environmental mobility evident in the recent research literature. Third, the values and assumptions of the expert evidence provided in the Delphi survey are interrogated to illustrate the limited set of conditions under which the forecasts of environmental mobility, as set out in this paper, are likely to hold

Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection

This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning techniques. We compare the fo recastaccuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a benchmark. We focus on international tourism demand to all seventeen regions of Spain. The SVR with a Gaussian radial basis function kernel outperforms the rest of the models for the longest forecast horizons. We also find that machine learning methods improve their forecasting accuracy with respect to linear models as forecast horizons increase. This results shows the suitability of SVR for medium and long term forecasting.Peer ReviewedPostprint (published version

Consciousness complexity

Copyright © 2015 by author and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0

Space syntax and spatial cognition: or why the axial line?

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