The timing and probability of FDI: an application to US multinational enterprises

An ‘option-pricing’ model is employed to analyse the timing of FDI. Assuming that the firm's profits are determined by the attractiveness of both the home and foreign countries, and that attractiveness follows a Brownian motion, an optimal trigger value of FDI is derived. The model shows that, contrary to the NPV rule, FDI entry should be delayed the greater the uncertainty of attractiveness in both locations. Another important result is that MNEs do not regard FDI as a risk-diversification tool. The results of the model were then tested empirically with US FDI data, using labour costs as a proxy for (the reciprocal of) attractiveness. The results support the findings of the analytical model.     

Score-driven Markov-switching EGARCH models: an application to systematic risk analysis

We introduce new Markov-switching (MS) dynamic conditional score (DCS) exponential generalized autoregressive conditional heteroscedasticity (EGARCH) models, to be used by practitioners for forecasting value-at-risk (VaR) and expected shortfall (ES) in systematic risk analysis. We use daily log-return data from the Standard & Poor’s 500 (S&P 500) index for the period 1950–2016. The analysis of the S&P 500 is useful, for example, for investors of (i) well-diversified US equity portfolios; (ii) S&P 500 futures and options traded at Chicago Mercantile Exchange Globex; (iii) exchange traded funds (ETFs) related to the S&P 500. The new MS DCS-EGARCH models are alternatives to of the recent MS Beta-t-EGARCH model that uses the symmetric Student’s t distribution for the error term. For the new models, we use more flexible asymmetric probability distributions for the error term: Skew-Gen-t (skewed generalized t), EGB2 (exponential generalized beta of the second kind) and NIG (normal-inverse Gaussian) distributions. For all MS DCS-EGARCH models, we identify high- and low-volatility periods for the S&P 500. We find that the statistical performance of the new MS DCS-EGARCH models is superior to that of the MS Beta-t-EGARCH model. As a practical application, we perform systematic risk analysis by forecasting VaR and ES.

Abbreviation Single regime (SR); Markov-switching (MS); dynamic conditional score (DCS); exponential generalized autoregressive conditional heteroscedasticity (EGARCH); value-at-risk (VaR); expected shortfall (ES); Standard & Poor's 500 (S&P 500); exchange traded funds (ETFs); Skew-Gen-t (skewed generalized t); EGB2 (exponential generalized beta of the second kind); NIG (normal-inverse Gaussian); log-likelihood (LL); standard deviation (SD); partial autocorrelation function (PACF); likelihood-ratio (LR); ordinary least squares (OLS); heteroscedasticity and autocorrelation consistent (HAC); Akaike information criterion (AIC); Bayesian information criterion (BIC); Hannan-Quinn criterion (HQC).     

Linear SAM models for inequality changes analysis: an application to the Extremadurian economy

Social accounting matrices are adequate databases for the economic modelling. These matrices emphasize the role of households in the economy, and so, they usually disaggregate the household sector into several groups. This disaggregation allows social accounting matrices to be used for diverse income distribution analysis. The objective of this work is to use the linear SAM models to study how inequality is modified by several exogenous injections of income. The set of multipliers and indicators presented is applied to the economy of Extremadura – a region situated in the southwest of Spain. In particular, together with the accounting multipliers, two redistributed income matrices are presented to show how changes in final demand and in income transfers cause opposite effects in inequality. For contrasting these results, Gini and Theil indices are also used. Finally, a major reduction in both would result from an appropriate re-allocation of transfers.     

Born on the first of July: An (un)natural experiment in birth timing

It is well understood that government policies can distort behavior. But what is less often recognized is that the anticipated introduction of a policy can introduce its own distortions. We study one such “introduction effect,” using evidence from a unique policy change in Australia. In 2004, the Australian government announced that children born on or after July 1, 2004 would receive a $3000 “Baby Bonus.” Although the policy was only announced seven weeks before its introduction, parents appear to have behaved strategically in order to receive the benefit, with the number of births dipping sharply before the policy commenced. On July 1, 2004, more Australian children were born than on any other single date in the past thirty years. We estimate that over 1000 births were “moved” so as to ensure that their parents were eligible for the Baby Bonus, with about one quarter being moved by more than one week. Most of the effect was due to changes in the timing of induction and cesarean section procedures. We find evidence to suggest that babies who were shifted into the eligibility period were more likely to be of high birth weight. Two years later, on July 1, 2006, the Baby Bonus was increased, and we find that this again caused births to be moved from June to July. These birth timing events represent an opportunity for health researchers to study the impact of planned birthdays and hospital management issues.     

The probability of multidimensional poverty: A new approach and an empirical application to EU-SILC data

This paper proposes a novel method to analyze multidimensional poverty by using a large set of feasible weights to summarize the information about the poor, which enables remaining agnostic about the relative importance given to different poverty dimensions. This method allows for the calculation of the individual probability of being poor in a multidimensional perspective. The distribution of individual probabilities can then be combined with Generalized Lorenz dominance techniques to derive unanimous consent for a wide class of social welfare functions with a minimum load of value judgments. The innovations proposed here allow to move from a dual definition of poverty, where poor and non-poor individuals are classified in a mutually exclusive context, to a continuous measure of deprivation capturing both the extensive and intensive margin of multidimensional poverty. The empirical application of the method consists of measuring multidimensional poverty in ten selected countries using four waves of EU-SILC data (2008–2014).     

Endogeneity in panel stochastic frontier models: an application to the Japanese cotton spinning industry

We present a panel stochastic frontier model that handles the endogeneity problem. This model can treat the endogeneity of both frontier and inefficiency variables. We apply our method to examine the technical efficiency of Japanese cotton spinning industry. Our results indicate that market concentration is endogenous, and when its endogeneity is properly handled, it has a larger negative impact on the technical efficiency of cotton spinning plants. We find that the exogenous model substantially overestimates efficiency in concentrated markets.     

On interpreting probability distributions fitted to times of first adoption

This paper attempts to interpret the standard statistical distributions in the innovation diffusion context. A simple rule is provided for classifying a statistical distribution as being an internal, an external, or a mixed influence model. Two distributions, suitable as flexible mixed influence models, are used to analyze several well-known data sets.     

Median-unbiased estimation of structural change models: an application to real exchange rate persistence

Measuring deviations from purchasing power parity has been the subject of extensive investigation. The most common practice in empirical research for measuring real exchange rate persistence is to estimate univariate autoregressive (AR) time series models and calculate the half-life, defined as the number of periods for a unit shock to a time series to decay by 50%. In the presence of structural change, there are two potential biases in the parameter estimates of AR models: (1) a downward small sample median-bias and (2) an upward bias, which occurs when structural change is present and ignored. We conduct a variety of Monte Carlo simulations and demonstrate that the existence of structural change causes a substantial increase in the small sample bias documented in Andrews (1993). We then propose an extension of median-unbiased estimation, which explicitly accounts for structural change, and apply these methods to estimate half-lives of several long-horizon real exchange rates analysed by Lothian and Taylor (1996) and Taylor (2002). The upward bias from neglecting structural change dominates the downward median-bias for these real exchange rates. When structural change is present and accounted for, the median-unbiased half-lives towards a changing mean decrease and the confidence intervals tighten.     

Unobserved heterogeneity in stochastic cost frontier models: an application to Swiss nursing homes

Housing price-to-income and price-to-rent ratios are among the most widely monitored indicators of housing market conditions. While these ratios tend to fluctuate around a constant level or a mild trend over the long term, they also tend to deviate from these benchmarks for protracted periods. Traditional unit root tests often indicate the presence of a unit root. This article uses the framework of fractional integration to test the persistence of price-to-income and price-to-rent ratios in a sample of 16 OECD countries spanning four decades. The results indicate that the ratios are highly persistent. The possibility that persistence estimates may be affected by structural breaks in the series is also considered, but evidence of such breaks is found only in a very limited number of cases. Policy action may be required if high price-to-income and price-to-rent ratios have adverse social and economic consequences. Policies should be guided by a careful analysis of the factors behind high ratios.     

Applying conjoint analysis in economic evaluations: an application to menorrhagia

The increased demand for health care, coupled with limited resources, means that decisions have to be made concerning the allocation of scarce health care resources. This paper considers how conjoint analysis (CA) can be used to aid this decision making process. It is shown how the technique can be used to estimate marginal rates of substitution between attributes, willingness to pay (WTP) if cost is included as an attribute and overall utility scores for different ways of providing a service. The technique is applied to consider women's preferences for two surgical procedures in the treatment of menorrhagia: hysterectomy and conservative surgery. The results suggest conservative surgery is preferred to hysterectomy, as indicated by higher utility scores for the former and a marginal WTP of 7593 to have conservative surgery rather than hysterectomy. The internal validity of CA was also shown. It is concluded that CA is a potentially useful instrument for policy makers. However, numerous methodological issues need addressing before the technique becomes an established instrument within economic evaluations.     

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 generalized framework for endogenous timing in duopoly games and an application to price-quantity competition

This paper extends the analysis of duopoly market by distinguishing two types of competition: (i) the basic form of competition where each firm is unrestricted in its choice of price and quantity and (ii) the non-basic form of competition where firms’ strategic choices over price and quantity are limited a priori. Our analysis focuses on the former rather than the latter. Under a very general setting of concave industrial revenue and asymmetric convex costs, we show that each firm typically makes more profit in the subgame perfect Nash equilibrium (SPNE) of the leader-follower price-quantity competition, one of the basic competition forms, than in the SPNE of the leader-follower price competition and that each firm always makes more profit under simultaneous move price-quantity competition than under simultaneous move price competition. We establish a generalized framework for endogenous timing in duopoly games which is capable of embodying and overcoming the inconsistency across the existing three frameworks in the field. We highlight the advantages of a 3-period general framework.     

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.     

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.     

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.     

Cointegration analysis with structural breaks and deterministic trends: an application to the Canadian dollar

This article applies recent developments in cointegration analysis with structural breaks and deterministic trends to analyse the relationship between the real Canada–US exchange rate and commodity prices. Previous empirical studies disagree on whether these variables are cointegrated. The root of disagreement could be in the handling of deterministic trends and potential structural breaks. I find that even after controlling for these matters, the question of whether the real exchange rate and commodity prices are cointegrated for Canada remains unresolved.     

Endogenous regressor binary choice models without instruments,with an application to migration

This paper shows identification of a semiparametric binary choice model containing an endogenous regressor, when no outside instrumental variable is available. A simple estimator, an easy test for endogeneity, and an empirical application to US migration data are provided.     

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.