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Measuring treatment effects are a complicated task as the outcomes of receiving and not receiving the treatment cannot be observed simultaneously. Thus, the issue of obtaining accurate measurement is an issue of predicting the counterfactuals accurately. In this study, we explore the suitability of using time-varying parameter models in a panel to generate robust measure of counterfactuals, hence robust measure of treatment effects. We suggest some within-sample tests for constant parameter versus time-varying parameter models and diagnostic tools based on time-varying parameter framework as a flexible alternative to predict missing data. Monte Carlos and two empirical studies are examined in this framework. The results appear to show that if the focus is on minimizing the mean square error of the predicted treatment effects, a “straitjacket” approach relying on the best selected model from the pre-treatment data remains the best option in view of the missing post-treatment information on counterfactuals. On the other hand, the confidence band based on the time-varying parameter model provides a more robust inference to hedge against possible changes of the relations between the treated units and the controls in the post-treatment period.

Hosting mega-events is often perceived as a way to stimulate economic growth through tourism. However, the cost of infrastructure investments and promotion may outweigh the benefits generated by the mega-events. Measuring the impact of such events on a hosting country’s economy is not easy, especially as mega-events generally involve many sectors of a destination’s economy. In this study, we adopt a panel data approach to evaluate the impact of the London Olympic Games, Brazil World Cup, and Rio Olympic Games on the economic growth of the respective destinations. Using cross-sectional correlations between countries, we construct scenarios in which the hosting countries did not hold the mega-events and then estimate the time-varying impact of the events on the economy. The developed and developing countries exhibit different results.

This chapter extends the univariate forecasting method proposed by Wang, Luc, and Hsiao (2013) to forecast the multivariate long memory model subject to structural breaks. The approach does not need to estimate the parameters of this multivariate system nor need to detect the structural breaks. The only procedure is to employ a VAR(k) model to approximate the multivariate long memory model subject to structural breaks. Therefore, this approach reduces the computational burden substantially and also avoids estimation of the parameters of the multivariate long memory model, which can lead to poor forecasting performance. Moreover, when there are multiple breaks, when the breaks occur close to the end of the sample or when the breaks occur at different locations for the time series in the system, our VAR approximation approach solves the issue of spurious breaks in finite samples, even though the exact orders of the multivariate long memory process are unknown. Insights from our theoretical analysis are confirmed by a set of Monte Carlo experiments, through which we demonstrate that our approach provides a substantial improvement over existing multivariate prediction methods. Finally, an empirical application to the multivariate realized volatility illustrates the usefulness of our forecasting procedure.

Tourism demand exhibits growth cycles, and it is important to forecast turning points in these growth cycles to minimise risks to destination management. This study estimates logistic models of Hong Kong tourism demand, which are then used to generate both short- and long-term forecasts of tourism growth. The performance of the models is evaluated using the quadratic probability score and hit rates. The results show that the ways in which this information is used are crucial to the models’ predictive power. Further, we investigate whether combining probability forecasts can improve predictive accuracy, and find that combination approaches, especially nonlinear combination approaches, are sensitive to the quality of forecasts in the pool. In addition, model screening can improve forecasting performance.

Factors affecting subjective well-being and their gross effect measurement have been widely studied. However, how people derive utility from these factors has not been fully explored. This article ...

We explore the pros and cons of two counterfactual analysis methodologies — the panel data approach (PDA) and the synthetic control method (SCM) through a discussion of the differences in their underlying assumptions and a series of simulations.

•Introduce usefulness of density forecasts for visitor arrival in risk management.•Test the appropriateness of model assumption with histogram-based method.•Apply the framework to ARDL model for Hong Kong inbound tourism demand.•Error in ARDL model for the demand growth rates of China exhibit fat tail.•Normality assumption for the growth rates model from Macau is appropriate.

We consider several geometric approaches for combining forecasts in large samples—a simple eigenvector approach, a mean corrected eigenvector and trimmed eigenvector approach. We give conditions where geometric approach yields identical result as the regression approach. We also consider a mean and scale corrected simple average of all predictive models for finite sample and give conditions where simple average is an optimal combination. Monte Carlos are conducted to compare the finite sample performance of these and some popular forecast combination and information combination methods and to shed light on the issues of “forecast combination” vs “information combination”. We also try to shed light on whether there exists an optimal forecast combination method by comparing various forecast combination methods to predict US real output growth rate and excess equity premium.

We explore the pros and cons of two counterfactual analysis methodologies – the panel data approach (PDA) and the synthetic control method (SCM) through a discussion of the differences in their underlying assumptions and a series of simulations.

A time-of-use rate option design allowing an LDC's customers to allocate their consumption to be billed at the fixed and daily-varying TOU rates offers a win–win mechanism for electricity procurement in the face of uncertain spot prices and hedging options. Even if all customers have the same risk preferences, the proposed mechanism is Pareto-superior to the tariffs and procurement strategies commonly used in North America.

A panel data method is used to evaluate the impact of the Closer Economic Partnership Agreement (CEPA) signed between Mainland China and Hong Kong. Using the time series data of Hong Kong, Austria, Denmark, Finland, France, Germany, Italy, Japan, Korea, Netherlands, Norway, Singapore, Taiwan, U.K., and U.S. to construct what would have happened to Hong Kong's unemployment rate had there been no CEPA, we find that the CEPA effects gradually increases over time and eventually reached a constant level of reducing Hong Kong's unemployment rate by 9% a year.

A panel data method is used to evaluate the impact of China's accession to the WTO. Time-series data for China, Australia, Austria, Canada, Denmark, Finland, France, Germany, the UK, Hong Kong, Singapore, Korea, Japan, the Philippines, Indonesia, Malaysia, Thailand, the USA and Taiwan are used to construct the growth path that what would have been followed had there been no entry by China to the WTO. We find that from 2002 to 2007, accession to the WTO raised China's real economic growth rate by 2.4%, its export growth rate by 13.2% and its import growth rate by 18.89% a year.