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This chapter uses stated tourist preferences as a proxy of visitor yield measures, in order to analyse and understand the yield potential of different markets’ preferences. A literature review revealed that there is much progress to be made in terms of discussion, consensus and stability of methodology for the measurement of visitor yield. The aim of the visitor yield analysis, in the current chapter, is also to bring another dimension into yield analysis and discussion, contributing with a new form of measuring yield potential. Since the objective is to identify yield patterns based on tourist preferences over a period of time, dynamics may be captured from the fluctuation patterns, or expressed as volatility of visitor yield and length of stay throughout the years. Destination management organisations and tourist companies may potentially adopt this visitor yield matrix in order to support future strategic decisions.

In this chapter the development of new sugarcane varieties in Florida and Louisiana is examined, along with the accompanying advancement in mechanization technology through the widespread adoption of sugarcane harvesters. An econometric analysis is carried out to determine the impact of the price of raw sugar on raw-sugar yields in Louisiana and Florida. This study found that in the case of Louisiana, the 3-year lagged US raw-sugar price had a positive and significant impact on sugar yields. The change in raw-sugar prices did not have a significant impact on sugar yields for the Florida industry. Sugar production has increased over time, in part, due to the development of new sugarcane varieties accompanied by modern sugarcane harvesters. Given the relationship between price and yield, particularly in Louisiana, policy makers and producers must be mindful of the potential impact of policy-induced research and development (R&D) on the competitiveness of their industry.

In the spirit of White’s (1982) paper, this paper examines the consequences of model misspecification using a panel data regression model. Maximum likelihood, random and fixed effects estimators are compared using Monte Carlo experiments under normality of the disturbances but with a possibly misspecified variance-covariance matrix. We show that the correct GLS (ML) procedure is always the best according to MSE performance, but the researcher does not have perfect foresight on the true form of the variance covariance matrix. In this case, we show that a pretest estimator is a viable alternative given that its performance is a close second to correct GLS (ML) whether the true specification is a two-way, a one-way error component model or a pooled regression model. Incorrect GLS, ML or fixed effects estimators may lead to a big loss in MSE.

This paper compares alternative estimation procedures for multi-level factor models which imply blocks of zero restrictions on the associated matrix of factor loadings. We suggest a sequential least squares algorithm for minimizing the total sum of squared residuals and a two-step approach based on canonical correlations that are much simpler and faster than Bayesian approaches previously employed in the literature. An additional advantage is that our approaches can be used to estimate more complex multi-level factor structures where the number of levels is greater than two. Monte Carlo simulations suggest that the estimators perform well in typical sample sizes encountered in the factor analysis of macroeconomic data sets. We apply the methodologies to study international comovements of business and financial cycles.

The present study analyses patterns of seasonal tourist consumption, based on data collected in the Minho, a rural region situated in the Northeast of Portugal. The study aims at identifying and discussing main differences regarding socio-demographic profile and tourist behavior between tourists visiting the destination in the high, medium, and low season. Results permit a discussion of implications on destination management and marketing. More specifically, the understanding of these differences, considering the existing resources, constraints, and potentialities of the destination, shall help develop strategies yielding the diversification of demand, creating conditions for attracting, satisfying, and possibly ensuring loyalty of different tourist types in different seasons of the year (Jeffrey, D., & Barden, R. (2001). An analysis of the nature, causes and marketing implications of seasonality in the occupancy performance of English hotels. In T. Baum & S. Lundtorp (Eds.), Seasonality in tourism (pp. 119–140). Amsterdam: Pergamon). That is, the here discussed results should help strategically manage demand yielding sustainable destination development (Kastenholz, 2004).

This chapter identifies three crisis warning indicators driven from trading in emerging markets’ carry trades, and empirically examines whether these indicators could predict two major financial crises that hit the global financial markets in the last decades — The 1997–1998 Asian crisis and the 2007–2008 global crisis. The probit regression is used to examine the power of the three indicators in forecasting financial crises, using data from eight Asian emerging countries which serve as proxies for emerging markets, independent of the origination of the crisis. I use both fixed effect and random effect estimation to measure crisis impacts. The empirical results show that financial crises could have been predicted. Probit estimation show that carry trade returns can predict a financial crisis, and the estimation results are robust to both panel level and country-level analysis. These three indicators are by no means an exhaustive list of all possible predictors of financial crisis. The literature suggests other fundamental indicators of financial crises such as the current account deficit and foreign debt. However, this chapter cannot fully consider these indicators for lack of data at this point in time. Although financial crisis may be better predicted by the well-known fundamental indicators, the contribution of this chapter is simply that carry trade-related indicators can help in predicting crises.