Intraday price information flows between the CSI300 and futures market: an application of wavelet analysis

This study investigates linear and nonlinear price information flows between the Chinese Stock Index 300 (CSI300) and futures market using high-frequency data and their wavelet transformed series for three regimes for which stock short-selling restrictions in China are different. Empirical results generally indicate information feedback between these two markets regardless of assumptions of linear and nonlinear causality and regimes for original series and wavelet transformed data at different scales.     

Strategic decisions on bilateral bidding behavior: evidence from a wholesale electricity market

This article analyzes the strategic bilateral bidding behavior in the Spanish electricity wholesale market (OMEL). The collection of data includes information regarding weekly averages of spot prices, the quantity bid in the wholesale market, the quantities purchased in the wholesale market and sold in the open market, and the behavior of conduct parameters for the period from January 2002 to April 2007 for the four largest firms of the Spanish electricity market: Endesa, Iberdrola, Unión Fenosa and Hidrocantábrico. This article employs the New Empirical Industrial Organization approach. The empirical analysis was based on the autoregressive distributed lag approach to cointegration and on the Toda–Yamamoto Granger causality tests to validate the standard version of the theoretical formulation of the standard Cournot model, and its theoretical extension, to encompass the hypothesis of the presence of bid interdependence for electricity quantities sold and bought in the Spanish electricity wholesale market. The results of cointegration and causality analysis reinforce the empirical results of the extended Cournot model with the inclusion of the two main bidding variables that solved the optimization problem of profit maximization for each of the four firms analyzed.     

The impact of pre-announced day-to-day interventions on the Colombian exchange rate

Using cross-country cross-industry data, this paper explores how industry’s growth in number of firms in Central-East Europe (CEE) region is influenced by bank concentration in both the pre-crisis and crisis periods. The CEE region shows highly concentrated banking markets and less-developed financial markets; thus, the level of bank concentration and the resulting credit supply are crucial for firm creation and survival. Despite this, there is little evidence on these countries in the literature. Our empirical results suggest an inverted-U relationship: industry growth is fostered by bank concentration, but there is a turning point from which higher concentration begins producing the opposite effect. Moreover, the positive impact has a greater intensity during the crisis period compared to the pre-crisis period. Between sectors’ analysis shows that high-tech sectors are less reactive to changes in the concentration level.     

Spatial econometric Monte Carlo studies: raising the bar

We discuss Monte Carlo methodology that can be used to explore alternative approaches to estimating spatial regression models. Our focus is on models that include spatial lags of the dependent variable, e.g., the SAR specification. A major point is that practitioners rely on scalar summary measures of direct and indirect effects estimates to interpret the impact of changes in explanatory variables on the dependent variable of interest. We argue that these should be the focus of Monte Carlo experiments. Since effects estimates reflect a nonlinear function of both \(\beta \) and \(\rho \), past studies’ focus exclusively on \(\beta \) and \(\rho \) parameter estimates may not provide useful information regarding statistical properties of effects estimates produced by alternative estimators. Since effects estimates have recently become the focus of inference regarding the significance of (scalar summary) direct and indirect impacts arising from changes in the explanatory variables, empirical measures of dispersion produced by simulating draws from the (estimated) variance–covariance matrix of the parameters \(\beta \) and \(\rho \) should be part of the Monte Carlo study. An implication is that differences in the quality of estimated variance–covariance matrices arising from alternative estimators also plays a role in determining the accuracy of inference. An applied illustration is used to demonstrate how these issues can impact conclusions regarding the performance of alternative estimators.     

Generalized spatial autocorrelation in a panel-probit model with an application to exporting in China

This paper proposes a generalized spatial panel-data probit model with spatial autocorrelation of the dependent variable, the time-invariant individual shocks, and the remainder disturbances. It proposes its estimation with a Bayesian Markov chain Monte Carlo procedure. Simulation results show that the proposed estimation method performs well in small- to medium-sized samples. This method is then applied to the analysis of export-market participation of 1451 Chinese firms between 2002 and 2006 in the prefecture-level city of Wenzhou in the province of Zhejiang. Empirical results show that two of the three forms of the hypothesized spatial autocorrelation are significant, namely the spatial lag for the dependent variable and the time-invariant firm-specific shocks, but not the time-variant shocks. Ignoring any of these significant spatial effects would lead to misspecification.     

Bootstrap LM tests for higher-order spatial effects in spatial linear regression models

This paper first extends the methodology of Yang (J Econom 185:33–59, 2015) to allow for non-normality and/or unknown heteroskedasticity in obtaining asymptotically refined critical values for the LM-type tests through bootstrap. Bootstrap refinements in critical values require the LM test statistics to be asymptotically pivotal under the null hypothesis, and for this we provide a set of general methods for constructing LM and robust LM tests. We then give detailed treatments for two general higher-order spatial linear regression models: namely the \(\mathtt{SARAR}(p,q)\) model and the \(\mathtt{MESS}(p,q)\) model, by providing a complete set of non-normality robust LM and bootstrap LM tests for higher-order spatial effects, and a complete set of LM and bootstrap LM tests robust against both unknown heteroskedasticity and non-normality. Monte Carlo experiments are run, and results show an excellent performance of the bootstrap LM-type tests.     

Testing spatial regression models under nonregular conditions

In time series context, estimation and testing issues with autoregressive and moving average (ARMA) models are well understood. Similar issues in the context of spatial ARMA models for the disturbance of the regression, however, remain largely unexplored. In this paper, we discuss the problems of testing no spatial dependence in the disturbances against the alternative of spatial ARMA process incorporating the possible presence of spatial dependence in the dependent variable. The problems of conducting such a test are twofold. First, under the null hypothesis, the nuisance parameter is not identified, resulting in a singular information matrix (IM), which is a nonregular case in statistical inference. To take account of singular IM, we follow Davies (Biometrika 64(2):247–254, 1977; Biometrika 74(1):33–43, 1987) and propose a test procedure based on the supremum of the Rao score test statistic. Second, the possible presence of spatial lag dependence will have adverse effect on the performance of the test. Using the general test procedure of Bera and Yoon (Econom Theory 9:649–658, 1993) under local misspecification, we avoid the explicit estimation of the spatial autoregressive parameter. Thus our suggested tests are entirely based on ordinary least squares estimation. Tests suggested here can be viewed as a generalization of Anselin et al. (Reg Sci Urban Econ 26:77–104, 1996). We conduct Monte Carlo simulations to investigate the finite sample properties of the proposed tests. Simulation results show that our tests have good finite sample properties both in terms of size and power, compared to other tests in the literature. We also illustrate the applications of our tests through several data sets.     

Courts,scheduled damages,and medical malpractice insurance

We study how the functioning of the judicial system affects the availability and affordability of medical liability insurance, as proxied by the number of insurers and the premiums paid. We use two unique datasets collected in Italy from 2000 to 2010. Using the first dataset—insurance contracts for hospitals—we estimate the average treatment effect of schedules on insurers and premiums paid, conditional on judicial efficiency and proxied by different measures. Our identification rests on the partial overlap between healthcare districts and judicial districts, meaning that the caseload of a court and malpractice events at the healthcare provider level are not perfectly correlated. On average, the adoption of schedules does not produce any significant effect on insurers or on premiums paid. However, adopting schedules has a robust and significant effect on the number of insurers, but only in inefficient courts. We further investigate these findings using a second dataset comprising 17,578 malpractice insurance claims. We find evidence of a composition effect among claims that is triggered by higher levels of judicial inefficiency: As a court’s inefficiency increases, the likelihood for a case to not be decided on the merits decreases and the levels of reserve and recovery per claim decrease.     

Potential ECB reaction functions with time-varying parameters: an assessment

This paper analyses the behaviour of the European Central Bank over the period 1999–2014 through the estimation of monetary policy reaction functions with time-varying coefficients and heteroskedastic error terms. This allows to evaluate whether relevant shifts in the conduct of monetary policy occurred and whether the current financial crisis had an influence on that. The paper considers two different specifications, one with contemporaneous regressors and one with regressors from surveys. The Taylor rule is then enriched with a set of macroeconomic and financial variables with the aim of testing their significance. Results show that forward-looking variables have a better explanatory power over interest rate policy. All the coefficients are found to be stable along the sample so that no shift in the reaction function can be identified and the financial crisis is found to only lead to a change in the size of the shock. Finally, we also provide evidence about the fact that the ECB has been actually constrained by the zero lower bound during the recent crisis.     

Firm age and performance

Amid increasing interest in firm age and its effects on firm performance, this special issue offers an exhaustive review of the literature and a novel collection of evidence on the effects of firm age on performance, including a special focus of interest on innovation performance, financial performance, exports, survival and growth. This editorial positions the theme in the extant literature, and provides key definitions and challenges ahead in the field of evolutionary economics. It introduces the collection of articles composing the special issue. The papers offer a diversity of country contexts, as well as analytical approaches and methods. They include an exhaustive review of the literature on age and firms’ performance, and present original empirical studies focusing on the effects of age on firms’ economic outcomes on the one hand, and on innovation outcomes on the other hand. While most of the papers use econometric analysis, the level of analysis ranges from firm to individual.