Demand for cigarettes in Turkey: an application of count data models

Household cigarette demand in Turkey is examined using the zero-inflated negative binomial model to account for a large portion of households not reporting cigarette smoking or purchase and estimated using the data from the national household survey implemented in 2003. Data were divided into two main groups: families with and without teenagers. Results identify relevant household head and household characteristics needed to develop effective public policy to prevent the decision to begin to smoke and to reduce cigarette purchase to lower the future growth of government healthcare expenditures. Specifically, healthcare expenditure share, income, and cigarette-price elasticities are relevant in lowering cigarette purchases. The calculated price elasticities for cigarette demand falls within the range determined by studies conducted for developed countries including the member states of the European Union. An estimate of the effect of an increase in the excise tax lowering demand is provided.     

Spurious deterministic seasonality and autocorrelation corrections with quarterly data: Further Monte Carlo results

Following recent work of Franses, Hylleberg and Lee (FHL), this paper analyses the consequences of fitting a deterministic seasonal model to a quarterly time series which can be (at least approximately) described by a seasonal unit root(s) model. Besides the distribution of the coefficient of determination, the empirical distributions of two commonly used statistics are also investigated through Monte Carlo experiments for small, moderately large and large samples. FHL's work is also extended allowing the possibility of residual autocorrelation corrections. The main conclusion that emerges from the results is that one should not try to measure the importance of deterministic seasonality nor test for its presence in the context of such (static) regression models, even when using some form of residual autocorrelation correction. A simple empirical application is provided to illustrate our results. First version received: July 1997/final version received: July 1998     

Goodness-of-fit techniques for count data models: an application to the demand for dental care in Spain

This paper compares the practical performance of alternative goodness-of-fit techniques for count data models in the context of a study of the determinants of demand for dental care in Spain. We apply alternative goodness-of-fit techniques to different specifications. In particular, we implement recently proposed specification tests which are consistent in the direction of general nonparametric alternatives. The analysis suggests that a negative binomial model is an appropriate specification for dental care demand. Dental health and income are identified as important predictors of individuals' behavior. First version received: April 2000/Final version received: March 2001     

A GM(1,1)–Markov chain combined model with an application to predict the number of Chinese international airlines

In this paper, we propose a new dynamic analysis model which combines the first-order one-variable grey differential equation model (abbreviated as GM(1,1) model) from grey system theory and Markov chain model from stochastic process theory. We abbreviate the combined GM(1,1)–Markov chain (MC) model as MCGM(1,1) model. This combined model takes advantage of the high predictable power of GM(1,1) model and at the same time take advantage of the prediction power of Markov chain modelling on the discretized states based on the GM(1,1) modelling residual sequence. For prediction accuracy improvements, Taylor approximation is applied to MCGM(1,1) model. We call the improved version as T-MCGM(1,1) model. As an example, we use the statistical data of the number of Chinese international airlines from 1985 to 2003 for a validation of the effectiveness of the T-MCGM(1,1) model.     

Bayesian cost-effectiveness analysis for censored data: an application to antiplatelet therapy

Abstract

Objective:

Cost-effectiveness analysis (CEA) on trial-based data has played an important role in pharmacoeconomics. A regression model can be used to account for patient-level heterogeneity throughout covariates adjustment in CEA. However, the estimates from CEA could be biased if ignoring the censoring issue on effectiveness and costs. This study is to propose a regression model to account for both time-to-event effectiveness and cost.

Methods:

A bivariate regression model was proposed to analyze both effectiveness and cost simultaneously, while censored observations were also taken into account. The regression coefficients were estimated using a Bayesian approach by drawing a random sample from their posterior distribution derived from the Markov chain Monte Carlo (MCMC) method. The proposed method was illustrated using empirical data of anti-platelet therapies to the management of cardiovascular diseases for those patients with high risk of gastrointestinal (GI) bleeding, where cost-effectiveness between different therapies was analyzed under both censored and non-censored circumstances, where the effectiveness was defined as the time to re-hospitalization due to GI complications, and the cost was measured by the total drug expenditure.

Results:

Under censored circumstances, aspirin plus proton-pump inhibitors (PPIs) was considered more cost-effective than clopidogrel with/without PPIs, as shown in the cost-effectiveness acceptability curve, and clopidogrel was preferred to aspirin for a willingness-to-pay of 89 NTD for delaying 1 day to hospitalization due to GI complications.

Conclusions:

Ignoring censoring problems could possibly bias the results in CEA. This study has provided an appropriate method to conduct regression-based CEA to improve the estimation which serves its purpose for CEA concerns.

Limitations:

The normality assumption for the cost and effectiveness in the bivariate normal regression needs to be examined, and the conclusions may be biased if this assumption is violated. However, when sample size is sufficiently large, a slight deviation from normality would not be a serious problem.     

A dynamic Mincer equation with an application to Portuguese data

This article argues in favour of a dynamic specification of the Mincer equation, where the past observed earnings play the role of additional explanatory variable for current observed earnings. A dynamic approach offers an explanation why the return to schooling in terms of observed earnings is not independent of labour-market experience, as suggested by some recent empirical evidence for the United States.     

Decomposition analysis of consumers' demand changes: an application to Greek consumption data

A decomposition analysis for consumer demand functions is developed. Changes in Marshallian demand or expenditure shares functions over time are decomposed into a total substitution effect, an income effect, and a habit effect. This framework is applied to post-war Greek consumption patterns through a habit persistence version of the Quadratic Almost Ideal Demand System (QUAIDS). It is found that for all commodity categories (i.e., food, beverages and tobacco, footwear and clothing, settling and housing, and others) the income effect was the main driving force in explaining changes in both quantity demanded and expenditure shares, followed by habit and total substitution effects.     

Spatial shift-share analysis versus spatial filtering: an application to Spanish employment data

The aim of this work is to analyse the influence of spatial effects in the evolution of regional employment, thus improving the explanation of the existing differences. With this aim, two non-parametric techniques are proposed: spatial shift-share analysis and spatial filtering. Spatial shift-share models based on previously defined spatial weights matrix allow the identification and estimation of the spatial effects. Furthermore, spatial filtering techniques can be used in order to remove the effects of spatial correlation, thus allowing the decomposition of the employment variation into two components, respectively related to the spatial and structural effects. The application of both techniques to the spatial analysis of regional employment in Spain leads to some interesting findings and shows the main advantages and limitations of each of the considered procedures, together with the quantification of their sensitivity with regard to the considered weights matrix.     

Testing the dynamic specification of an econometric model with an application to Belgian data

In this paper, we analyze the Final Equation and Transfer Function form associated with a linear dynamic simultaneous equation model and use the empirical findings as a guidance to a structural form specification in accordance with the information in a sample of monthly Belgian data.     

Using Monte Carlo analysis to investigate the relationship between overconsumption and uncertain access to one's personal utility function

The failure of income and subjective well-being to correlate strongly beyond subsistence levels has been well documented. This is an important observation from an environmental perspective because it suggests that some excess consumption could be curbed without leading to a decline in subjective well-being. At the same time, there is evidence that access to one's internal utility function is imperfect, indicating that choices may approximate utility maximization but that the correspondence is unlikely to be exact. Here I use a Monte Carlo model to incorporate findings from hedonic psychology to investigate the question of how imperfect access to one's utility may lead to negatively affective overconsumption through asymmetries in how people are able to budget their time. According to the model, consumer debt leads to a compounding of this issue. Although it cannot return well-being to the ideal level, some hedonic costs of this overconsumption can be partially mitigated through required vacation in excess of what individuals would chose themselves in a fully functioning market, perhaps along the line of the European model.     

A Monte Carlo approach to compute the asymptotic standard errors of dynamic multipliers

A Monte Carlo method to compute asymptotic standard errors of dynamic multipliers is proposed. It is applied to Hein's Model I to find standard error of interim multipliers of taxes on nominal income.     

Measuring the efficiency of search engines: an application of data envelopment analysis

This paper adopted the data envelopment analysis (DEA) method to investigate the efficiency of several search engines. A query search on a search engine is modelled as a production process. The input and output vectors are defined and measured accordingly. We studied seven engines, Alta Vista, Excite, Hotbot, Lycos, Infoseek, Open Text, and WebCrawler and found that Alta Vista, Excite, Infoseek, and WebCrawler are efficient but the other three are not. Possible efficiency improvements are calculated.     

Estimating exchange rate dynamics with diffusion processes: an application to Greek EMU data

Recently various exchange rate models capturing the dynamics during the transition from an exchange rate arrangement of floating rates into a currency union have been derived. Technically, these stochastic equilibrium models are diffusion processes which have to be estimated by discretely sampled observations. Using daily exchange rate data prior to the Greek EMU-entrance on 1 January 2001, we develop a rigorous estimation procedure. Our estimates point to an increasing interventionist economic policy in the run-up to the Greek EMU entrance. A comparison of this econometric indication with policy information provided (ex-post) by the Bank of Greece (BoG) in its Annual Report 2000 reveals that the BoG indeed pursued such an active policy stance (so-called institutional frontloading strategies).      

Oil market share dynamics: A Markov chain analysis of consumer and producer adjustments

We develop an “optimal market share rule” model of cartel behavior which when applied to the OPEC cartel appears capable of explaining its stability and responses to changed market events. In particular, by attaching importance to market shares based approximately on costs, OPEC members can by maintaining optimal shares deter deviant member attempts to break cartel rules. After a thorough discussion of the theory, the model is tested empirically using a Markov probability model. The estimated Markov transition matrix is further decomposed into what Theil has called the exchange matrix and the mean passage matrix. Dynamic adjustment processes in the market are revealed by the latter while an emerging pattern of OPEC member surveillance of consumers is revealed by the former which facilitates cartel stability. Inspection of these matrixes further suggests that after the formation of OPEC there is evidence of less potential for producer conflict while there appears more evidence for consumer conflict. While these results must be tentative in view of the fact that they have been estimated using a simplified two consumer — two producer model and limited data, it is argued that the results are highly suggestive and the approach in this study can be extended to cover all producer and consumers, and can be integrated into a complete model of the world oil market.     

Parameter estimation of an asset price model driven by a weak hidden Markov chain

We introduce a weak hidden Markov model (WHMM) in an attempt to capture more accurately the evolution of a risky asset. The log returns of assets are modulated by a weak or higher-order Markov chain with finite-state space. In particular, the optimal estimates of the second-order Markov chain and parameters of the model are given in terms of the discrete-time filters for the state of the Markov chain, the number of jumps, occupation time and auxiliary processes. We provide a detailed implementation of the model to a dataset of financial time series along with the analysis of the h-day ahead forecasts. The results of our error analysis suggest that within the dataset studied and considering longer predictive horizons, WHMM gives a better forecasting performance than the traditional HMM.     

The effects of wind generation capacity on electricity prices and generation costs: a Monte Carlo analysis

We use Monte Carlo analysis to examine the potential of increased renewable generation to provide a hedge against variability in energy prices and costs. Fuel costs, electricity demand and wind generation are allowed to vary and a unit commitment and economic dispatch algorithm is employed to produce cost-minimizing generation schedules under different levels of installed wind capacity. Increased wind capacity reduces the mean and the variance of production costs but only the variance of electricity prices. Wind generators see their market revenues increase while consumer payments and fossil generator profits do not considerably vary as wind capacity increases. Risk aversion is captured by considering the conditional value-at-risk for both consumers and producers. The optimal level of wind generation increases as risk aversion increases due to the potential of wind to act as a hedge against very high electricity prices in high fuel price scenarios.     

Monte Carlo simulation of macroeconomic risk with a continuum of agents: the symmetric case

Summary. Suppose a large economy with individual risk is modeled by a continuum of pairwise exchangeable random variables (i.i.d., in particular). Then the relevant stochastic process is jointly measurable only in degenerate cases. Yet in Monte Carlo simulation, the average of a large finite draw of the random variables converges almost surely. Several necessary and sufficient conditions for such “Monte Carlo convergence” are given. Also, conditioned on the associated Monte Carlo -algebra, which represents macroeconomic risk, individual agents' random shocks are independent. Furthermore, a converse to one version of the classical law of large numbers is proved. Received: October 29, 2001; revised version: April 24, 2002 RID="*" ID="*" Part of this work was done when Yeneng Sun was visiting SITE at Stanford University in July 2001. An early version of some results was included in a presentation to Tom Sargent's macro workshop at Stanford. We are grateful to him and Felix Kübler in particular for their comments. And also to Marcos Lisboa for several discussions with Peter Hammond, during which the basic idea of the paper began to take shape. Correspondence to: P.J. Hammond     

股票收益率非正态性的蒙特卡罗模拟检验

现实金融数据的分布通常表现为厚尾性和不对称性,因此用正态分布拟合实际金融数据的分布有很大的局限性.文章利用广义双曲线分布的厚尾性和不对称性对1997年1月2日～2003年9月19日的上证综指日收益率分布分别做了正态分布、广义双曲线分布、正态逆高斯分布和双曲线分布的拟合及蒙特卡罗模拟检验,结果表明广义双曲线分布和正态逆高斯分布可以较好地拟合上证综指日收益率分布.另外,文章还建立了一个带噪声干扰的线性系统,对实际的股票收益率并不服从正态分布,而表现出尖峰厚尾的特征做出了一种可能的解释.     

Monte Carlo simulation of macroeconomic risk with a continuum of agents: the general case

In large random economies with heterogeneous agents, a standard stochastic framework presumes a random macro state, combined with idiosyncratic micro shocks. This can be formally represented by a random process consisting of a continuum of random variables that are conditionally independent given the macro state. However, this process satisfies a standard joint measurability condition only if there is essentially no idiosyncratic risk at all. Based on iteratively complete product measure spaces, we characterize the validity of the standard stochastic framework via Monte Carlo simulation as well as event-wise measurable conditional probabilities. These general characterizations also allow us to strengthen some earlier results related to exchangeability and independence. Parts of this work were done while Yeneng Sun was visiting Stanford University in July 2003, March–May 2005 and July 2006, and while Peter Hammond was visiting the National University of Singapore in March–April 2004. An early version was presented at the World Congress of the Econometric Society in 2005.