Chew Lian Chua

Bio: Chew Lian Chua is an academic researcher from The University of Nottingham Ningbo China. The author has contributed to research in topics: Interest rate & Bayesian probability. The author has an hindex of 12, co-authored 47 publications receiving 342 citations. Previous affiliations of Chew Lian Chua include University of Wollongong & University of Melbourne.

Airline code-share alliances and costs: Imposing concavity on translog cost function estimation

Abstract: This paper provides an assessment of how airline code-share alliances affect the costs of the airline industry. It makes two contributions to the literature. First, it measures the effects of airline alliances by estimating a translog cost function using a panel dataset of 10 major U.S.-based airlines over 29 quarters. Secondly, it ensures concavity of the estimated cost function by using the procedure suggested by Ryan and Wales (2000, Economics Letters 67, 253–260). A conventional translog cost function is first estimated and scale estimates are computed. Unfortunately, the estimated function fails the curvature requirement, which makes interpreting the estimated effects of alliances somewhat dubious. Hence, we re-estimate the cost function by imposing local concavity restrictions. We find that large alliance partners have a small negative effect on airlines’ costs, but small alliance partners’ effect on costs appear to be positive, although the magnitude is negligible. We also find material differences in the estimates of scale economies after imposing local concavity.

Are empirical measures of macroeconomic uncertainty alike

Abstract: This paper considers a plethora of time-series measures of uncertainty for inflation and real output growth, which are widely used in empirical studies. Their relative performances are compared to a benchmark measure using the uncertainty measure reported by individual forecasters in the Survey of Professional Forecasters (SPF) for the period 1982-2008. The results show that the use of real-time data with fixedsample recursive estimation of an asymmetric bivariate GARCH model produces inflation uncertainty estimates which replicate the survey measure more closely than any other time-series models. There is, however, overwhelming evidence that many of the time series measures of growth uncertainty overestimate the level of benchmark survey measure. The implications of our results are discussed in the context of the extensive empirical studies on macroeconomic uncertainty.

Hospital Competition, Technical Efficiency and Quality

Abstract: This paper studies the link between competition and technical efficiency of public hospitals in the state of Victoria, Australia. It finds a positive relationship between efficiency and competition as measured by the Hirschman-Herfindahl Index (HHI) and a negative relationship when the number of competing private hospitals is used instead of HHI. It also finds that whether or not quality is treated as an endogenous output variable influences the statistical estimates of the link between efficiency and competition. The findings point to possibly undesirable resource allocation effects when public hospitals are made to compete with a large number of private hospitals.

Can consumer sentiment and its components forecast Australian GDP and consumption

Abstract: This paper examines whether the disaggregation of consumer sentiment data into its sub-components improves the real-time capacity to forecast GDP and consumption. A Bayesian error correction approach augmented with the consumer sentiment index and permutations of the consumer sentiment sub-indices is used to evaluate forecasting power. The forecasts are benchmarked against both composite forecasts and forecasts from standard error correction models. Using Australian data, we find that consumer sentiment data increase the accuracy of GDP and consumption forecasts, with certain components of consumer sentiment consistently providing better forecasts than aggregate consumer sentiment data. Copyright © 2009 John Wiley & Sons, Ltd.

Bayesian model averaging in consumer demand systems with inequality constraints

Abstract: Share equations for the translog and almost ideal demand systems are estimated using Markov Chain Monte Carlo. A common prior on the elasticities and budget shares evaluated at average prices and income is used for both models. It includes equality restrictions (homogeneity, adding tip and symmetry) and inequality restrictions (monotonicity and concavity). Posterior densities on the elasticities and shares are obtained; the problem of choosing between the results from the two alternative functional forms is resolved by using Bayesian model averaging. The application is to USDA data for beef pork and poultry. Estimation of elasticities and shares, evaluated at mean prices and expenditure, is insensitive to model choice. At points away from the means, the estimates are sensitive, and model averaging has an impact.

Forecasting, Structural Time Series Models and the Kalman Filter

Abstract: In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Testing Continuous-Time Models of the Spot Interest Rate

Abstract: Different continuous-time models for interest rates coexist in the literature. We test parametric models by comparing their implied parametric density to the same density estimated nonparametrically. We do not replace the continuous-time model by discrete approximations, even though the data are recorded at discrete intervals. The principal source of rejection of existing models is the strong nonlinearity of the drift. Around its mean, where the drift is essentially zero, the spot rate behaves like a random walk. The drift then mean-reverts strongly when far away from the mean. The volatility is higher when away from the mean.

The post-war U.S. Phillips curve: a revisionist econometric history: response to Evans and McCallum

Abstract: In 1958, A.W. Phillips discovered a strong negative correlation between inflation and unemployment in United Kingdom data. Continuing controversy surrounds the long-run trade-off suggested by a curve he drew through these observations. We conduct a wide-ranging investigation of the post-war U.S. Phillips correlations and Phillips curve. Many economists view the Phillips correlations as chimerical, given the rise in both inflation and unemployment during the 197Os, and the Phillips curve as plagued by subtle identification difficulties raised by Lucas and Sargent. Yet, a strikingly stable negative correlation exists over the business cycle, and recent theory indicates the Lucas-Sargent critique may not be empirically relevant. When we estimate the long-run trade-off as Gordon and Solow did, we find it is roughly one-for-one. This traditional Keynesian identification also makes business cycles entirely due to demand shocks. However, the Gordon-Solow model is not the only one that fits the data well. Alternative identifications lead to much more modest effects of demand on business cycles and essentially negligible long-run trade-offs. *We have received many constructive comments on this paper: we particularly thank Charles Evans, Robert J. Gordon, Bennett McCallum, and Charles Plosser. Support was provided by the National Science Foundation via grant NSF-91-22463.