Liquidity and Optimal Market Transparency

In this paper I explore some of the consequences of greater market transparency for market performance in the presence of a strategic specialist. Although numerous studies have dealt with this issue, previous work has only considered either fully transparent or fully opaque markets. My model allows for different levels of transparency, and therefore sheds light on how transparency affects market performance. I show that an intermediate level of transparency can improve market performance relative to the more extreme cases of full transparency or no transparency at all.     

The Impact of Higher Education Funding on Socio-Economic Variables: Evidence from EU Countries

Education is an important vector of smart growth, sustainable development, and economic competitiveness. Higher education plays an important catalytic role in human capital development, and ensures a better insertion of individuals in the socio-economic environment. However, at present, it faces complex challenges in the context of public financing contraction. The aim of our article is to investigate the level of higher education funding and to reveal the impact it has on the socio-economic environment. The novelty of our article resides in the multifaceted analysis we propose combining both qualitative and empirical methods, in the comprehensive sample of EU countries, and in the large time horizon we consider. We first assess the relationship between public financial resources allocated to higher education and socio-economic indicators by means of an exploratory technique, called cluster analysis. Our findings reveal a pattern of similarity among EU countries by building smaller but homogenous groups. We then use the vector autoregressive method to complement the cluster analysis by providing further information on the strength, sign, and causal relationship between government expenditure in tertiary education and several socio-economic indicators. We perform an analysis for each country in the sample. Our findings are mixed, with some countries displaying a greater sensitivity of public financing (channeled to higher education) to changes in socio-economic indicators.     

Why Do Some Consumers Make Unhealthy Food Choices?

This research examines the impact of gender, age, and education on food choices and addresses issues of causality in these observed relationships; a logit model was used to test the mediation effects and hypotheses. The results from a self-administered online survey indicate that gender and education are two key predictors of consumers’ food choices for their children. These findings are further explained by attitudes toward obesity. Specifically, female consumers and parents with lower levels of formal education tend to select food products that are nutritionally inferior because they are not necessarily concerned about their child’s weight and do not usually restrict their child’s food and diet. This research advances a causal mechanism that explains unexpected consumers’ food choices; it essentially proposes and tests two mediators—restrictions of a child’s weight and concerns about a child’s weight—of the relationship between key demographic variables and consumers’ food choices.     

Complete Neyman measurement procedure

Traditional estimation theory generally starts from point estimators, and based on them confidence regions with given confidence level are constructed. However, this approach works only in some special cases and, even more severe, it is based on the unrealistic but mathematical necessary assumption of a generally unbounded parameter space.  The procedures derived in this paper, start from a bounded measurement range which contains the potential values of the parameter of interest. For given measurement range and given reliability requirement measurement procedures including a point estimator are developed. The result are complete measurement procedures for distribution parameters. Most precise procedures are derived and called complete Neyman measurement procedures.     

The strategic specialist and imperfect competition in a limit order market

The empirical literature suggests that the limit order book contains information that might be used by the specialist to his own advantage. I develop a model where there is a strategic specialist who competes against a limit order book and has information about supply. The presence of a strategic specialist in an imperfectly competitive limit order book market induces non-monotonicity of market indicators with respect to the variance of liquidation value. Moreover, the existence of private information about supply significantly affects market performance as it induces, among other effects, lower market liquidity. Finally, this model suggests another link between Kyle’s (1985, 1989) [Kyle, A., 1985. Continuous auctions and insider trading. Econometrica 53, 1315–1336, Kyle, A., 1989. Informed speculators with imperfect competition. Review of Economic Studies 56, 317–356] and Glosten and Milgrom’s (1985) [Glosten, L., Milgrom, P., 1985. Bid, ask and transaction prices in a specialist market with heterogeneously informed markets. Journal of Financial Economics 14, 71–100] models by allowing for strategic behaviour of the specialist.     

Testing for Granger non-causality in heterogeneous panels

This paper proposes a very simple test of Granger (1969) non-causality for heterogeneous panel data models. Our test statistic is based on the individual Wald statistics of Granger non causality averaged across the cross-section units. First, this statistic is shown to converge sequentially to a standard normal distribution. Second, the semi-asymptotic distribution of the average statistic is characterized for a fixed T sample. A standardized statistic based on an approximation of the moments of Wald statistics is hence proposed. Third, Monte Carlo experiments show that our standardized panel statistics have very good small sample properties, even in the presence of cross-sectional dependence.     

Does self-fulfilment of the inflation expectation exist?

This study examines the causal relationship between the actual and expected inflation in the U.S., using the bootstrap Granger full-sample causality test and sub-sample rolling-window estimation test to illustrate the self-fulfilment of inflation expectations. The full-sample result indicates that there is a bidirectional causality between the actual and expected inflation, showing the possibility of self-fulfilment of inflation expectations which illustrates that inflation is driven by inflation expectation. However, considering structural changes in two series, we find that the short-run causal nexus between the actual and expected inflation using full-sample data are fraudulent. By adopting a time-varying rolling-window approach to revisiting the dynamic causal relationships, this article identifies that the actual inflation has both positive and negative impacts on the expected inflation in distinct sub-periods. Nevertheless, inflation expectation exerts only negative effects on the actual inflation. The results point out that the self-fulfilment of inflation expectations does not exist. Given the bidirectional nexus between the actual and expected inflation, keeping a low and stable inflation is critical to price stability and anchoring of inflation expectation.