Search

AbstractWe propose an extension of the double autoregressive (DAR) model: the sign-double autoregressive (SDAR) model, in the spirit of the GJR-GARCH model (also named the sign-ARCH model). Our model shares the important property of DAR models where a unit root does not imply non stationarity and it allows for asymmetry, as other alternatives in the literature such as the GJR-GARCH or asymmetric linear DAR and dual-asymmetry linear DAR models. We establish consistency and asymptotic normality of the quasi-maximum likelihood estimator in the context of the SDAR model. Furthermore, it is shown by simulations that the asymptotic properties also apply in finite samples. Finally, an empirical application shows the usefulness of our model specially in periods of supply/demand crises of oil disruptions, where spikes of volatility are very likely to be predominant. [ABSTRACT FROM AUTHOR]

Abstract.We study the estimation of nonlinear models with cross-sectional data using two-step generalized estimating equations within the quasi-maximum likelihood estimation framework. To improve efficiency, we propose a grouped estimator that accounts for potential spatial correlation in the underlying innovations of nonlinear models. Under mild weak dependence assumptions, we provide results on estimation consistency and asymptotic normality. Monte Carlo simulations demonstrate the efficiency gain of our approach compared to various estimation methods. Finally, we apply the proposed approach to examine the role of cultural distance in an extended gravity equation using international trade data from China. Compared to existing methods, our approach yields estimates with smaller standard errors and reinforces the hypothesis that both cultural and geographical distances significantly negatively influence international trade. [ABSTRACT FROM AUTHOR]

Abstract.To facilitate semiparametric estimation of general discrete-choice, censored, sample selection, and other complex panel data models, we study identification and estimation of nonseparable multiple-index models in panel data with correlated random effects and a fixed number of time periods. The parameter vectors of interest are shown to be identified up to multiplicative constants and the average marginal effects are identified under the assumption that the distribution of individual effects depends on the explanatory variables only through their averages across time. Under this assumption, we propose to estimate the unknown parameters by the generalized method of moments based on the average and outer product of the difference of derivatives of the regression function. The rate of convergence and asymptotic distribution is established both for the proposed parameter estimates and the average marginal effects. We conduct Monte Carlo simulation studies to assess finite-sample performance of the proposed estimator. We also use it to model dynamic earnings of women, and using our general identification results, we find a negative effect of the number of children not only on the selection into employment as usual, but also on the average earnings. [ABSTRACT FROM AUTHOR]

A point process for event arrivals in high-frequency trading is presented. The intensity is the product of a Hawkes process and high-dimensional functions of covariates derived from the order book. Conditions for stationarity of the process are stated. An algorithm is presented to estimate the model even in the presence of billions of data points, possibly mapping covariates into a high-dimensional space. Large sample sizes can be common for high-frequency data applications using multiple instruments. Consistency results under weak conditions are established. A test statistic to assess out of sample performance of different model specifications is suggested. The methodology is applied to the study of four stocks that trade on the New York Stock Exchange. The out of sample testing procedure suggests that capturing the nonlinearity of the order book information adds value to the self-exciting nature of high-frequency trading events. [ABSTRACT FROM AUTHOR]

In this article, to model risk contagion between the U.S. and China stock markets based on high-frequency financial data, we develop a novel continuous-time jump-diffusion process. For example, we consider three channels for volatility contagion—such as integrated volatility, positive jump variation, and negative jump variation—and each stock market is able to affect the other stock market as an overnight risk factor. We develop a quasi-maximum likelihood estimator for model parameters and establish its asymptotic properties. Furthermore, to identify contagion channels and test the existence of a structural break with a known structural break date, we propose hypothesis test procedures. Using the proposed diffusion model with high-frequency financial data, we investigate the effect of the U.S.–China trade war on stock markets from a financial contagion perspective. From the empirical study, we find evidence of financial contagion from the United States to China and evidence that the risk contagion channel has changed from integrated volatility to negative jump variation. [ABSTRACT FROM AUTHOR]

This paper investigated the determinants of unhealthy BMI categories, namely underweight, overweight, and obesity, for urban and rural women of childbearing age in Bangladesh. Multilevel nested logistic models were estimated using data from multiple waves (2004–18) of the Demographic and Health Surveys of Bangladesh. After controlling for community and regional effects, the underweight status showed an inverse relationship with years of education for urban and rural women. Conversely, the fitted model estimates revealed a positive association between overweight risk and education for both urban and rural women populations. However, the association between obesity and education was significant for only urban women. Employment status appeared to be negatively associated with the risks of being overweight and obese in both population groups. Moreover, the women were found less likely to be underweight and more likely to be overweight and obese when their household socio-economic status improved. Study findings have the potential to inform a wide range of interventions, policies and programs that simultaneously address the adverse consequences of underweight, overweight, and/obese population health conditions in rural and urban Bangladesh. [ABSTRACT FROM AUTHOR]

The processes of kinship, gender and age stratification, early learning opportunities, socialization, and the ability to engage in creativity and optimize craft are crucial for developing the education economy. All these factors in the development of the education economy should turn a young person into an economic person with the ability to make effective production, technological, and managerial decisions regarding the development of his or her personality, society, and economic element.

This paper studies the relationship between risk propensity, education and financial literacy. The results of the empirical investigation confirm the importance of the key explanatory variables of education and financial competence. Since they are both included in the model, the different roles of each are singled out. In particular, while education turns out to be a factor contributing to raising risk tolerance, financial literacy tends to reduce risk propensity. Risk attitude is evaluated by self-reported assessment and modeled through cumulative logit models. In order to handle anomalous data, M estimators with a bounded influence function are considered.

Algerian public banks need to implement credit risk management techniques tailored to the specific characteristics of SMEs to prevent the deterioration of the banks’ solvency due to the degradation of the quality of their SME portfolios. In this regard, our main objective is to highlight the interest that credit risk management will have within the People’s Credit of Algeria by developing a Credit Scoring model based on the logistic regression technique, using a sample of 226 SMEs. The study results demonstrate the importance of the logistic regression model in classifying companies and its predictive ability for default, with a good classification rate of 91.2%.

In Africa, rangeland ecosystems have been exploited due to heavy and unsustainable grazing. Policy and institutional mechanisms such as integrating silvopastoral systems with sustainable grazing practices have been devised to mitigate the negative effects. In this study, we investigated whether the uptake of sustainable grazing management in the form of controlled grazing spurs investment in multipurpose trees (MPTs) and enhances income. Using instrumental variable regression, we find that controlled grazing increases not only the propensity to plant MPTs but also the number of tree species. More importantly, IV and treatment effect results indicate that controlled grazing enhances income from MPTs. [ABSTRACT FROM AUTHOR]

AbstractThis study investigates the optimal use of covariates in reducing variance when analyzing experimental data. We show that finding the variance-minimizing strategy for making use of pre-treatment observables is equivalent to estimating the conditional expectation function of the outcome given all available pre-randomization observables. This is a pure prediction problem, which recent advances in machine learning (ML) are well-suited to tackling. Through a number of empirical examples, we show how ML-based regression adjustments can feasibly be implemented in practical settings. We compare our proposed estimator to other standard variance reduction techniques in the literature. Two important advantages of our ML-based regression adjustment estimator are that (i) they improve asymptotic efficiency relative to other alternatives and (ii) they can be implemented automatically, with relatively little tuning from the researcher, which limits the scope for data-snooping. [ABSTRACT FROM AUTHOR]

Graphical models are a powerful tool to estimate a high-dimensional inverse covariance (precision) matrix, which has been applied for a portfolio allocation problem. The assumption made by these models is a sparsity of the precision matrix. However, when stock returns are driven by common factors, such assumption does not hold. We address this limitation and develop a framework, Factor Graphical Lasso (FGL), which integrates graphical models with the factor structure in the context of portfolio allocation by decomposing a precision matrix into low-rank and sparse components. Our theoretical results and simulations show that FGL consistently estimates the portfolio weights and risk exposure and also that FGL is robust to heavy-tailed distributions which makes our method suitable for financial applications. FGL-based portfolios are shown to exhibit superior performance over several prominent competitors including equal-weighted and index portfolios in the empirical application for the S&P500 constituents. [ABSTRACT FROM AUTHOR]

Bias correction can often improve the finite sample performance of estimators. We show that the choice of bias correction method has no effect on the higher‐order variance of semiparametrically efficient parametric estimators, so long as the estimate of the bias is asymptotically linear. It is also shown that bootstrap, jackknife, and analytical bias estimates are asymptotically linear for estimators with higher‐order expansions of a standard form. In particular, we find that for a variety of estimators the straightforward bootstrap bias correction gives the same higher‐order variance as more complicated analytical or jackknife bias corrections. In contrast, bias corrections that do not estimate the bias at the parametric rate, such as the split‐sample jackknife, result in larger higher‐order variances in the i.i.d. setting we focus on. For both a cross‐sectional MLE and a panel model with individual fixed effects, we show that the split‐sample jackknife has a higher‐order variance term that is twice as large as that of the "leave‐one‐out" jackknife. [ABSTRACT FROM AUTHOR]

The implementation of public projects by organizations requires professionalization of their evaluation methods. The research objective is to identify the key criteria for project evaluation that go beyond the “project triangle.” The following problem was formulated: whether ex post evaluation of the project taking into account parameters of the “design triangle” is not an oversimplification in the context of uniqueness of design solutions that significantly affect civilization and technological development of societies in terms of global. The study was conducted in two stages. In the first stage, 109 completed projects were evaluated using the quantitative method. The quantitative method was used to evaluate the projects. In the second stage, on the basis of the project-evaluation results, the key criteria for ex post evaluation of project implementation were analyzed using the Hellwig’s correlation method. It was shown that for the proper ex post evaluation of project implementation, it is necessary to supplement criteria with strategic and societal effects.

Statistical-based predictions with extreme value theory improve the performance of the risk model not by choosing the model structure that is expected to predict the best but by developing a model whose results are a combination of models with different shapes. Using different ensemble algorithms to conglomerate the TBATS and the GEV distribution, we found that the stacking ensemble algorithm outperforms other ensembles hence the forecasting accuracy of risk measures is improved with the stacking ensemble algorithm. The risk estimates suggest that the returns on losses averaging 0.014 and 0.018 invested at 90 and 99 percent respectively, are riskier than the returns on gains. Backtesting results further revealed that all the risk measures are reliable, and the combined model is a good one for computing financial risk particularly in South Africa. At high confidence levels, all the risk measures seem to perform better than at lower confidence levels, as evidenced by higher probability values from backtesting using the Kupiec and Christoffersen test at 95 percent than at 99 percent levels of significance.

By examining the econometric interrelationships of rice export prices of India, Thailand, Vietnam, Pakistan, the U.S. Argentina, Brazil, and Uruguay, this study examines the most influential rice-exporting countries in the international rice export market. The sampled countries are the source of more than 80% of the global rice exports. In the process, this study relies on online data portals of the United Nations and country-level sources for aromatic and non-aromatic rice export price information from India, Pakistan, and Thailand. This study employed the Prais-Winsten and Autoregressive Distributive Lag (ARDL) estimation processes with the error correction procedure. Combining the findings from both models, this study concludes that in terms of interactions, the non-aromatic long-grain rice price of the U.S. and the aromatic rice price of Pakistan are the most correlated prices in the international rice market. Considering the number of countries influenced by the price of a given country, our results indicate that Vietnam is the price leader in the non-aromatic long-grain market, followed by Argentina, Brazil, and India. India, the largest exporter, is the fourth top price leader. In the case of aromatic rice, Thailand is the absolute leader. Many countries in Asia and Africa rely on rice imports to meet domestic demand, where the food security situation is already precarious. Based on the findings, this study suggests closely monitoring rice production and export trends of Vietnam, Argentina, Brazil, India and Thailand to provide early warning when necessary to better manage supply shocks in the international rice market. [ABSTRACT FROM AUTHOR]

This paper examines the impact of bilateral investment treaties (BITs) and technological intensity on exports. It incorporates technological intensity and firm heterogeneity into a simplified static, partial equilibrium model, proposing that BITs increase the extensive margin of exports and have a greater impact in technologically underdeveloped sectors. The empirical analysis utilizes a comprehensive dataset covering 191 countries and 22 sectors, employing Poisson pseudo maximum likelihood (PPML) estimation with various fixed effects to estimate the gravity equations. It provides robust evidence that BITs primarily affect exports by increasing the extensive margin of exports. Furthermore, in sectors with the lowest average technological intensity, an additional BIT is estimated to increase the extensive margin by approximately 18.6 % , while in sectors with the highest average technological intensity, the increase is estimated to be only 8.1 % . [ABSTRACT FROM AUTHOR]

The covariance matrix associated with multiple financial returns plays foundational roles in many empirical applications, for example, quantifying risks and managing investment portfolios. Such covariance matrices are well known to be dynamic, that is, their structures change with the underlying market conditions. To incorporate such dynamics in a setting with high-frequency noisy data contaminated by measurement errors, we propose a new approach for estimating the covariances of a high-dimensional return series. By utilizing an appropriate localization, our approach is designed upon exploiting generic variables that are informative in accounting for the dynamic changes. We then investigate the properties and performance of the high-dimensional minimal-variance sparse portfolio constructed from employing the proposed dynamic covariance estimator. Our theory establishes the validity of the proposed covariance estimation methods in handling high-dimensional, high-frequency noisy data. The promising applications of our methods are demonstrated by extensive simulations and empirical studies showing the satisfactory accuracy of the covariance estimation and the substantially improved portfolio performance. [ABSTRACT FROM AUTHOR]

Household income is one of the most important economic categories depending on various factors, in particular on the level of education of the head of the household. Salaries are treated as the market valuation of graduates made by employers. Hence, the question arises of how the education system affects the distribution of household income, in particular in the group of people with higher education.