We propose a revenue test model for measuring the level of competition in the ship management industry. In addition to revenues, the model incorporates information on the structure of expenditures required in ship management operations to estimate the Panzar–Rosse H-statistic emphasizing crew, technical and administrative expenses. Estimation results using data for Cyprus’s ship management industry for the period 2011–2023, suggest a market structure compatible with monopolistic competition. Furthermore, crew expenses provided the highest input factor elasticity and vessel portfolio size the most important revenue driver in the estimation procedure.
We propose a multiscale version of the seemingly unrelated regressions model, based on wavelet transform-based time series observations. Each regression equation refers to a different time scale, which enables the use of across-scale error covariances in the feasible GLS estimation procedure for efficiency gains. We demonstrate the advantages of the proposed method over OLS with two studies: an empirical study using stock market returns for the main US industrial sectors and a detailed Monte Carlo simulation study with alternative wavelet filters. We also provide explanations for the suitability of the proposed method for estimating long-term systematic risk.
A hedonic regression framework is proposed for evaluating the determinants of beer prices and consumer ratings. This study specifically addresses the endogeneity problem associated with the impact of consumer ratings on beer prices using a set of beer sensory and chemical characteristics as instrumental variables in the estimation procedure. The results suggest that beer prices tend to be influenced by consumer ratings and the objective characteristics of beers, while consumer ratings tend to be influenced by the sensory and chemical characteristics of beers. Also, limited evidence is found for the use of quantity discounts by beer producers.
We examined the evolution of cross-market linkages between four major precious metals and US stock returns, before (Phase I) and after (Phase II) the COVID-19 outbreak. Phase II was also extended to encompass the Ukrainian conflict, which prolonged the period of uncertainty in financial markets. Due to the increase in volatility observed in Phase II, we used a heteroskedasticity-adjusted correlation coefficient to examine the evolution of correlation changes since the COVID-19 outbreak. We also propose a relevant dissimilarity measure in multidimensional scaling analysis that can be used for depicting associations between financial returns in turbulent times. Our results suggest that (i) the correlation levels of gold, silver, platinum, and palladium returns with US stock returns have not changed substantially since the COVID-19 outbreak, and (ii) all precious metal returns exhibit movements that are less synchronized with US stock returns, with palladium and gold being the least synchronized.
The ship management industry operates on a global scale and is often associated with business practices such as repeated short-term contracts, long-term customer relationships, and international variation in pricing agreements. Using a unique dataset and a semi-parametric hedonic model specification, we investigated the determinants of ship management fees with particular emphasis on the identification of price mark-ups associated with switching costs. Our results suggest: 1) the existence of a 'lo-hi' pricing pattern that is consistent with theoretical studies of switching costs; 2) the exercise of geographic price discrimination by ship management firms. Ship management fees were also found to be influenced by a set of vessel, company, and management service characteristics.
A hedonic regression framework is proposed for evaluating the determinants of beer prices and consumer ratings. This study specifically addresses the endogeneity problem associated with the impact of consumer ratings on beer prices using a set of beer sensory and chemical characteristics as instrumental variables in the estimation procedure. The results suggest that beer prices tend to be influenced by consumer ratings and the objective characteristics of beers, while consumer ratings tend to be influenced by the sensory and chemical characteristics of beers. Also, limited evidence is found for the use of quantity discounts by beer producers.
This study proposes a wavelet procedure for estimating partial correlation coefficients between stock market returns over different time scales. The estimated partial correlations are subsequently used in a cluster analysis to identify, for each time scale, groups of stocks that exhibit distinct market movement characteristics and are therefore useful for portfolio diversification. The proposed procedure is demonstrated using all the major S&P 500 sector indices as well as precious metals and energy sector futures returns during the last decade. The results suggest cluster formations that vary by time scale, which entails different stock selection strategies for investors differing in terms of their investment horizon orientation.
The effectiveness of a product’s distribution network in retail stores is an important consideration for marketing managers. An effective distribution network typically covers a large number of stores in the geographic area of a market and establishes a continuous presence in the top-selling outlets of a product category at the same time. This study proposes a semiparametric, brand-level version of the SCAN*PRO sales model, to evaluate the impact of retail distribution changes on sales. The model is estimated using the iteratively reweighted least squares method and provides the following outputs: (i) least squares coefficient estimates for the price and promotional drivers in the model specification and (ii) two-dimensional plots of the nonmonotonic relationship between the weighted distribution and sales. The proposed model can be estimated with commonly available retail scanning data and is demonstrated using three laundry detergent brands from The Netherlands.
We propose the use of wavelet coefficients, which are generated from nondecimated discreet wavelet transforms, to form a correlation-based dissimilarity measure in metric multidimensional scaling. This measure enables the construction of configurations depicting the associations between objects across different timescales. The proposed method is used to examine the similarities between the economic sentiment indicators of the EU member states that are published monthly by the European Commission. The results suggest that economic sentiment differs considerably among the member states in the short term. In contrast, several similarities emerge when considering the associations over longer time horizons. These similarities tend to be related to the countries that are geographically close or that exhibited similar economic behaviour prior to the introduction of the euro. Furthermore, the results of a detailed simulation study suggest that the proposed dissimilarity measure is particularly well suited for identifying long-term associations between nonstationary time series.
Gold is frequently cited by investors as a financial asset that can be associated with a negative beta coefficient. I investigate this hypothesis by estimating the beta coefficient of gold at different time-scales and examining the associated implications for investors with different planning horizons. Estimation is performed using maximal overlap discrete wavelet transforms of gold and stock market returns in four major currencies. The results suggest that gold tends to be associated with a negative beta coefficient when considering long-term investment horizons, and this finding is consistent across markets and currencies.
Shipping and shipping services are a key industry of great importance to the economy of Cyprus and the wider European Union. Assessment, management and future steering of the industry, and its associated economy, is carried out by a range of organisations and is of direct interest to a number of stakeholders. This article presents an analysis of shipping credit flow data: an important and archetypal series whose analysis is hampered by rapid changes of variance. Our analysis uses the recently developed data-driven Haar–Fisz transformation that enables accurate trend estimation and successful prediction in these kinds of situation. Our trend estimation is augmented by bootstrap confidence bands, new in this context. The good performance of the data-driven Haar–Fisz transform contrasts with the poor performance exhibited by popular and established variance stabilisation alternatives: the Box–Cox, logarithm and square root transformations.
An econometric test is proposed to show the existence of nonlinear pricing in the European market for loans. The test incorporates a measure of industry concentration to examine the impact of market structure on the use of nonlinear pricing tactics by banks. Econometric results using a panel dataset consisting of seven European countries suggest that nonlinear pricing is associated with increasing monopoly power in European banking. (C) 2016 Elsevier Inc. All rights reserved.
The mean squared prediction error of the linear regression model is examined when estimation is performed with instrumental variables. It is shown that increasing the number of instruments in the estimation procedure, can reduce the mean squared prediction error of the model through more efficient estimation of the coefficient vector.
This study examines the existence of a liquidity effect in the UK economy over different time-scales. This analysis draws from the liquidity preference framework, an approach to interest rate determination, and uses wavelet multiscale analysis in the context of a standardised regression model. The modelling framework is similar to the one proposed by Cochrane (1989), however, instead of using a band-pass filter the model's variables are analyzed with a wavelet multiresolution analysis which enables a more accurate estimation of the liquidity effect. The results suggest that, in short-term cycles, interest rates are influenced primarily by changes in the money supply (i.e., the liquidity effect). In medium- and long-term cycles, the liquidity effect becomes less important and interest rates are found to be more sensitive to income and price effects.
Abstract This study examines the impact of channel concentration on retail prices in the traditional cheese market of Cyprus. The analysis is based on a panel data model for retail prices and a non-linear simultaneous equations model for the estimation of market power. Our results suggest that retail cheese prices tend to be positively related to producer concentration and negatively related to channel concentration. We provide explanations for these results based on the structure-conduct-performance and countervailing buyer power models in published industrial organization studies.
A maximal overlap discrete wavelet transform is used to obtain time scale decompositions of economic forecasts and their errors. The generated time scale components can be used in loss measures and tests for comparing forecast accuracy to evaluate whether the forecasts accurately capture the cyclical features of the data.
This study examines gold’s contribution to portfolio risk over different time scales. The analysis is based on wavelet decompositions of the variances and covariances associated with a portfolio that includes gold, stocks, 10-year government bonds and three-month Treasury bills. The results suggest that gold provides the lowest contribution to portfolio risk only when considered over medium- and long-term investment horizons.
This article proposes a wavelet smoothing method to improve conditional forecasts generated from linear regression sales response models. The method is applied to the forecasted values of the predictors to remove forecast errors and thereby improve the overall forecasting performance of the models. Eight empirical studies are presented in which the purpose was to forecast detergent sales in the Netherlands, and wavelet smoothing was compared with a moving average and a band-pass filter. All methods were found to improve forecasts. Wavelet smoothing provided the best results when applied on highly volatile marketing time series. In contrast, it was less effective when applied on highly aggregated and smooth time series. An advantage of wavelets is that they are flexible enough to allow for data characteristics like abrupt changes, spikes and cyclical changes that are usually associated with price changes and promotions.
This study presents empirical evidence on the relationship between the forecast errors and the number of individual forecasts used in averages of model forecasts. The investigation is based on forecasts published by Her Majesty's Treasury in the monthly report 'Forecasts for the UK economy: A comparison of independent forecasts'. The results suggest that averages of model forecasts tend to improve forecasting performance when relatively large numbers of forecasts are used in the averages (usually in excess of 40); however, results are not always consistent and averages should be used with caution.
