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We propose a framework to study optimal trading policies in a one-tick pro-rata limit order book, as typically arises in short-term interest rate futures contracts. The high-frequency trader has the choice to trade via market orders or limit orders, which are represented respectively by impulse controls and continuous controls. We model and discuss the consequences of the two main features of this particular microstructure: first, the limit orders sent by the high frequency trader are only partially executed, and therefore she has no control on the executed quantity. For this purpose, we model cumulative executed volumes by compound Poisson processes. Second, the high frequency trader faces the overtrading risk, which is the risk of brutal variations in her inventory. The consequences of this risk are investigated in the context of optimal liquidation. We study the regular/impulse control problem using dynamic programming methods, which leads to a characterization of the value function in terms of an integro quasi-variational inequality. We then provide the associated numerical resolution procedure, and convergence of this computational scheme is proved. Next, we examine several situations where we can one one hand simplify the numerical procedure by reducing the number of state variables, and on the other hand focus on specific cases of practical interest. We examine both a market making problem and a best execution problem in the case where the mid-price process is a martingale. We also detail a high frequency trading strategy in the case where a (predictive) directional information on the mid-price is available. Each of the resulting strategies are illustrated by numerical tests.

The ultimate goal of development is to improve health and well-being at the individual level. Yet, survey data are routinely collected at the household level. To bridge this gap the existing literature has used various, not fully satisfactory direct and indirect methods to impute individual level outcomes from household data. This paper proposes a new direct statistical method that constructs BMI Z-scores for all ages. It develops age-gender specific Body Mass Index (BMI) growth curve distributions using generalized LMS estimations in a GAMLSS framework which allows flexible modelling of all parameters. These growth curve distributions are then used to construct BMI Z-scores that are consistent for children and adults and that can thus be used to examine individual-level well-being and intra-household allocation patterns. Compared to existing methods that estimate intra-household allocations, BMI Z-scores present several advantages. One, the BMI measures health outcomes rather than caloric or monetary health inputs. Two, it is easier to collect and is measured with less error. Three, the BMI Z-score methodology also enables a range of previously unmeasurable within-household comparisons and it opens up the use of existing sources of survey data to answer new research questions in health and development economics. An empirical example for Tanzania provides one simple application of the BMI Z-score method, showing how household-level measures significantly overestimate individual measures of well-being and health.

This analysis sought to determine an upper limit for the S&P 500 price-to-earnings ratio (P/E) under normal market and economic conditions considering interest rates. Data covered the period from Jan 1962 to Jan 2021. Time series were the weekly S&P 500 price, earnings, P/E, plus the yield of the US 10-year treasury bond. Exploratory data analysis showed smoothing and linear regression would be inappropriate. Instead, clustering with proper visualization answers the question. The 95% upper limit for the S&P 500 P/E under normal market and economic conditions is 25.73, if the 10-year yield is < 7.0%. A more aggressive 99% upper limit is 29.27. Upper limits for the S&P 500 P/E at higher interest rates may be found by using the visualization.

The calculation of the intrinsic value of shares is essential for investors. The result can be used as a guideline for buying, selling, or holding shares. This research was conducted because the stock price on the Indonesia Stock Exchange (IDX) in March 2020 experienced a deep decline, namely a decrease of 38% on average, so it is interesting to study. In determining the intrinsic value of these shares, the Dividend Discount Model (DDM) and Price Earning Ratio (P/E Ratio) methods will be used as well as evaluate the most accurate method. The study uses stock data from all business sectors on the IDX for the 2014-2019 period, especially companies that pay dividends. The sample used in this study were 49 companies (22%) of the average 222 companies that distributed dividend. Based on the results of the study, DDM, and P/E Ratio there are 43 companies (86%) in undervalued positions or only seven overvalued companies (10%). MOS is greater than 30% with DDM and P/E Ratio of 26 companies (52%). Likewise, with the results of the accuracy-test using the Mean Absolute Pricing Error (MAPE), it turns out that DDM is only 0.14% greater than the P/E Ratio. It can be said that DDM is relatively more accurate than the P/E Ratio. Thus overall the calculation of the intrinsic value of shares using DDM and the P/E Ratio in terms of undervalued or overvalued, MOS and accuracy can be concluded that there is no significant difference.

Background Predictive markers represent a solution for the proactive management of severe dengue. Despite the low mortality rate resulting from severe cases, dengue requires constant examination and round-the-clock nursing care due to the unpredictable progression of complications, posing a burden on clinical triage and material resources. Accordingly, identifying markers that allow for predicting disease prognosis from the initial diagnosis is needed. Given the improved pathogenesis understanding, myriad candidates have been proposed to be associated with severe dengue progression. Thus, we aim to review the relationship between the available biomarkers and severe dengue. Methodology We performed a systematic review and meta-analysis to compare the differences in host data collected within 72 hours of fever onset amongst the different disease severity levels. We searched nine bibliographic databases without restrictive criteria of language and publication date. We assessed risk of bias and graded robustness of evidence using NHLBI quality assessments and GRADE, respectively. This study protocol is registered in PROSPERO (CRD42018104495). Principal findings Of 4000 records found, 40 studies for qualitative synthesis, 19 for meta-analysis. We identified 108 host and viral markers collected within 72 hours of fever onset from 6160 laboratory-confirmed dengue cases, including hematopoietic parameters, biochemical substances, clinical symptoms, immune mediators, viral particles, and host genes. Overall, inconsistent case classifications explained substantial heterogeneity, and meta-analyses lacked statistical power. Still, moderate-certainty evidence indicated significantly lower platelet counts (SMD -0.65, 95% CI -0.97 to -0.32) and higher AST levels (SMD 0.87, 95% CI 0.36 to 1.38) in severe cases when compared to non-severe dengue during this time window. Conclusion The findings suggest that alterations of platelet count and AST level—in the first 72 hours of fever onset—are independent markers predicting the development of severe dengue., Author summary The major concern in dengue fever is the abrupt occurrence of severe complications, for which only close monitoring of patients is the treatment scheme. Thus, the markers managing to predict the subsequent progression of complications—in the early stage of disease course—could alleviate the clinical management burden. Ideally, the predictors foretell the outcomes before the severe complications occur—usually on days 4–7 following fever onset. In this study, therefore, we reviewed the available markers collected during the first 3 days of fever onset. We found robust evidence of significantly lower platelet counts and higher AST levels in those who subsequently developed severe dengue than those who did not. In this regard, platelet count could serve as an independent warning sign rather than combining with hematocrit—as seen in the current classification—which remains unaltered during this time window. Also, abdominal pain and vomiting could predict the outcomes, but using these signs is arduous when their manifestations vary as per the patient without cutoffs. Hepatomegaly rate is substantially higher in severe dengue, but likely yields a high false-negative prediction rate. There is a need for larger studies to confirm the relatedness of hyaluronan in severe dengue.

A large share of the world’s population is employed in manual labor. This paper estimates the effect of fine particulate matter (PM 2.5) on purely physical tasks analyzing half a million amateur track and field competition results, a setting that allows excluding productivity effects through the cog- nitive channel. Exploiting the panel nature of the data and high dimensional fixed effects, I find that a 10 µg/m3 increase in PM 2.5 reduces performance by 1% of a standard deviation. The effect grows with the duration of effort, indicating that occupations requiring low-intensity and sustained effort may be more affected by air pollution than occupations requiring occasional short but intense bursts of energy.

This paper studies the pairs trading strategy of No.1 soybean futures, soymeal futures, and soyoil futures on the DCE (Dalian Commodity Exchange). Pairs trading strategies are comprised of two stages. First, the Co-integration method (CA) is applied to form pairs and the formation period for pairs is divided into one year, two years and three years. Second, the criteria for opening and closing positions are established using two methods to give the trading rules applied in the decision-making process, the Simple Threshold Method (ST) and Deep Reinforcement Learning (DRL). In this paper, we adopt four models, including the ST (Simple Threshold Method), the CA-ST (Co-integration method and Simple Threshold Method), the DRL and the CA-DRL (Co-integration method and Deep Reinforcement Learning). The experimental results show that the CA-DRL model has the best performance, whether the pair’s formation period is one year, two years or three years, and China’s non-genetically modified soybean futures can implement the pairs trading strategy between commodities.

This research paper proposes a new indicator to evaluate the social response to the COVID-19 pandemic worldwide. This unique indicator is entitled "the society response to COVID-19 pandemic rate (SRCP rate)." The SRCP rate uses three steps: Step-I: the input table; Step-II: the calculation of indicators; Step-III: the graphical analysis. The step-I uses four variables: the total number of COVID-19 cases daily, the total number of COVID-19 recovered cases daily, the total number of COVID-19 active cases daily, and the total number of COVID-19 deaths daily. The step-II (the calculation of the indicators) is based on three indicators: the COVID-19 recovery rate, the COVID-19 damage rate, and the society response to COVID-19 pandemic rate. Step-III presents a 2-Dimensional graph and interactive maps to observe the three indicators' behavior. Finally, we applied the SRCP rate in thirty countries to analyze how any society is integrated and capable of going against the COVID-19 pandemic crisis.

Respondent biases introduce measurement error into household statistics. There are two components of respondent bias in household surveys: asymmetric information between proxy respondent and the individual on whom they report and aggregation bias when a proxy respondent reports on a household-level outcome across multiple individuals. We estimate the effects of respondent biases in a randomized control trial in Burkina Faso. Survey treatments vary who reports for the household including a self-report, the household head and a randomly chosen adult proxy. We find no effects of respondent type on total landholdings reported for the household, but statistically significant effects of area cultivated by random proxy reports relative to self-reported land data (11% of the standard deviation). Household heads over-report production of cereals, but under-report labor relative to self-reported labor. Female respondents report lower levels of fertilizer and higher frequencies of crops including legumes and vegetables that women traditionally produce relative to male respondents. Across all production variables, respondent biases are not solely attributable to asymmetric information. Aggregation bias is an important component of proxy misreporting for household production variables which can be addressed through survey design choices. These causal estimates of respondent biases underscore the importance of proxy reporting protocols in surveys and improved reporting of proxy descriptive statistics in empirical work.

Background: Early warnings of emerging infectious disease are crucial to prevent epidemics. However, in the early stage of the COVID-19 pandemic, traditional infectious disease surveillance failed to deliver a warning alert. The aim of this work is to develop search-engine-based surveillance methods for the early warning and prediction of COVID-19 outbreaks. Methods: By using more than 444 million Baidu search queries from China as training set, we collected 32 keywords from the Baidu Search Index that may related to COVID-19 outbreak from 18 December 2019 to 11 February 2020. The Beijing Xinfadi outbreak from 30 May 2020 to 30 July 2020 was used as independent test set. A multiple linear regression was applied to model the relationship between the daily query frequencies of keywords and the daily new cases. Findings: Our results show that 11 keywords in search queries were highly correlated to the daily numbers of confirmed cases (r =0.96, P

Risk measures, such as value-at-risk and expected shortfall, are widely used in finance. With the necessary sample size being computed using asymptotic expansions for relative errors, we propose a general framework to simulate these risk measures for a wide class of dependent data. The asymptotic expansions are new even for independent and identical data. An extensive numerical study is conducted to compare the proposed algorithm against existing algorithms, showing that the new algorithm is easy to implement, fast and accurate, even at the 0.001 quantile level. Applications to the estimation of intra-horizon risk and to a comparison of the relative errors of value-at-risk and expected shortfall are also given.

We introduce a robust regression estimator for time series factor models called the mOpt estimator. This estimator minimizes the maximum bias due to outlier generating distribution deviations from a standard normal errors distribution factor model, and at the same time has a high normal distribution efficiency. The efficacy of this estimator is demonstrated in applications to single factor and multifactor time series models. Extensive empirical investigation of the mOpt robust betas versus non-robust least square betas show that differences between the two estimates greater than 0.3 occur for about 18% of the stocks, and differences greater than 0.5 occur for about 7.5% of the stocks. We introduce and demonstrate the use of a robust statistical test for differences between mOpt and least squares factor model coefficients, and also a new robust model selection method that makes natural use of the mOpt regression estimator. It is highly recommend that practitioners and data service providers compute robust mOpt betas as a standard practice complement to least squares betas.

Computer age statistics, machine learning, data science and in general, data analytics, are having an ubiquitous impact on industry, business and services. Deploying a data transformation strategy requires a workforce which is up to the job in terms of knowledge, experience and capabilities. The application of analytics needs to address organizational needs, invoke proper methods, build on adequate infrastructures and ensure availability of the right skills to the right people. such upskilling requires a focus on conceptual understanding affecting both the pedagogical approach and the complementary learning assessment tools, This paper is about the application of advanced educational concepts to the teaching and evaluation of statistical and data science related concepts. Two educational elements will be included in the discussion: i) the use of simulations to facilitate problem based experiential learning and ii) an emphasis on information quality, as the overall objective of statistics and data science activity. We begin with an introduction to conceptual thinking and meaning equivalence and the application of Meaning Equivalence Reusable Learning Objects (MERLO) in the classroom. We then describe how MERLO and concept science can be applied in the domain of Statistics and Data Science. Section 3 is about the use of simulations in statistical education. In section 4 we discuss practical aspects of an education program focused on conceptual understanding. Section 5 is on the conceptual mapping of information quality as a MERLO infrastructure. The paper concludes with a discussion.

We investigate the reliability of land measurement modes on non-classical measurement error and empirical relationships. In our multi-country survey experiment, we find significant differences between GPS and remotely sensed data only in Viet Nam, where plot sizes are small relative to the other countries. The magnitude of farmers’ self-reporting bias relative to GPS measures is nonlinear, with the largest magnitude of self-reporting bias of 130% of a standard deviation (2.2-hectare bias) in the Lao People’s Democratic Republic relative to Viet Nam, which has 13.3% of a standard deviation (.008-hectare bias). In all countries except Viet Nam, the inverse land size–productivity relationship is upwardly biased for lower land area self-reported measures relative to GPS measures. In Viet Nam, the intensive margin of organic fertilizer use is negatively biased (30.4 percentage points) by self-reported measurement error. We conclude by considering sources of measurement error in implementation and costs.

A rich panel-data comprising 39,337 courses offered at UPR-Bayamon during 41 consecutive terms is used to analyze the distribution of course withdrawals (W). The proportion of W, its variance distribution, and the coefficients of skewness and kurtosis are estimated. Over time, and between and within academic fields, these parameters are significantly influenced by course, faculty and student characteristics, and particularly by unobservable faculty heterogeneity. Evidence points to the conclusion that faculty members and students are engaged in a shopping-around process where both parties improve their well-being at the expense of the academic standards and the quality of the education provided.

Background: The aim of this study was to examine the profile of, and outcomes for, all people hospitalised with COVID-19 across the first and second waves of the pandemic in England. Methods: This was a retrospective observational study that used data from the Hospital Episode Statistics dataset for England. All patients aged ≥ 18 years in England with a diagnosis of COVID-19 who had a hospital stay that was completed (discharged alive or died) between 1st March 2020 and 31st March 2021 were included. In-hospital mortality was the primary outcome of interest. The first wave was defined as 1st March-31st August 2020 and the second wave as 1st September 2020 to 31st March 2021. Multivariable logistic regression was used to model the relationship between mortality and the covariates: age, sex, deprivation, ethnicity, discharge/admission date and a number of comorbidities. Findings: Over the 13 months, 374,244 unique patients had a diagnosis of COVID-19 during a hospital stay, of whom 93,701 (25.0%) died in hospital. Adjusted mortality rates fell from 40-50% in March 2020 to 11% in August 2020 before rising to 21% in January 2021 and declining steadily to March 2021. Improvements in mortality rates were less apparent in older and comorbid patients. Although mortality rates fell for all ethnic groups from the first to the second wave, declines were less pronounced for Bangladeshi, Indian, Pakistani, Other Asian and Black African ethnic groups, with all having higher odds of mortality during the second wave than White ethnicity patients. Interpretation: There was a substantial decline in adjusted mortality rates during the early part of the first wave which was largely maintained during the second wave. The underlying reasons for consistently higher mortality risk in some ethnic groups merits further study. Funding Information: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Declaration of Interests: The authors declare that there is no conflict of interest. Ethics Approval Statement: Consent from individuals involved in this study was not required for this analysis of the Hospital Episodes Statistics (HES) administrative dataset. The analysis and presentation of data follows current NHS Digital guidance for the use of HES data for research purposes. Reported data are anonymised to the level required by ISB1523 Anonymisation Standard for Publishing Health and Social Care Data.

The focus of the study is to join together regression analysis and index numbers to get quality adjusted price change for rents of office and shop premises. First, we apply simple semilogarithmic linear price models, the fixed effects model (FE), and estimate them by the OLS method. We weight these estimated equations from observations into stratums for unweighted and weighted arithmetic and geometric averages such that the basic algebraic properties of the OLS method are satisfied. We show that the aggregation leads to reparameterization of the FE’s (here stratum effects) of the OLS method for the unweighted and weighted arithmetic and the weighted geometric averages. The solution for the unweighted geometric average is trivial and shown in any statistical textbooks. Second, we apply the Oaxaca decomposition (1973) for the aggregated price models (i.e. stratum aggregates) for these four basic averages and divide the actual price change of these averages into two parts: into quality correction and quality adjusted price changes. For these Oaxaca decompositions of different averages, we apply index number theory to estimate price changes for crude aggregation levels. All our analysis, including the basic and excellent index number formulas (see Vartia and Suopera, 2018), are based on their logarithmic representations. Our focus for the use of the logarithmic representations culminates into the theorem of additive decomposition of the value change by which we can compare the price change estimates based on unit values (i.e. weighted arithmetic averages) and especially Montgomery-Vartia price index. We show explicitly that inadequate control of rented squares leads to serious bias – the actual price change includes not only price changes but also quantity changes, which may dominate causing serious bias for price index numbers. Our test data comes from the KTI Property Information Ltd covering large municipals in Finland. The data do not include certain ID-key for the construction of bilateral price-links and thus a hedonic method is our approach to measure price index for rents of office and shop premises. The weighting into the population level is at the moment impossible because lack of data, we make all inference only for the conditional information set of our data. All statistical and mathematical methods in this study are programmed by the SAS and the user interface for the official production of statistics by the SAS EG.

This study assessed the empirical comparability of item calibration in the developed essay (DEVessay-MAT) and (NECOessay-MAT) mathematics achievement test under the Generalized Partial Credit Model (GPCM). The instrumentation research approach of counterbalance design was employed. The sample consisted of 1080 senior secondary school students (SSS3) of 36 schools, who were drawn randomly from the Osun East senatorial district of Osun State, Nigeria. Two instruments were used and data obtained were subjected to Parallel Analysis (PA), Generalized Partial Credit Model (GPCM) and Independent sample t-test. Results showed that the test does not violate unidimensionality with the first Eigenvalue (2.05) from the experimental data was greater than the first random Eigenvalue (1.17) from PA, while other Eigenvalues from the experimental data were less than the rest of Eigenvalues under PA. Also, there existed a significant difference between the step difficulties/overall item difficulty and discrimination/slope index of the two instruments with (t = 3.52, df = 8, p < 0.05) and (t = 3.26, df = 8, p < 0.05) respectively. The authors’ concluded that the developed essay test produced better item statistics estimates compared to NECO-MAT (essay) test. Consequently, it was recommended that public examining bodies in sub-Sahara Africa should embrace an apt polytomous model for the calibration of their test items.

The COVID-19 disease has already spread to more than 213 countries and territories with infected (confirmed) cases of more than 27 million people throughout the world so far, while the numbers keep increasing. In India, this deadly disease was first detected on January 30, 2020, in a student of Kerala who returned from Wuhan. Because of India’s high population density, different cultures, and diversity, it is a good idea to have a separate analysis of each state. Hence, this paper focuses on the comprehensive analysis of the effect of COVID-19 on Indian states and Union Territories and the development of a regression model to predict the number of discharge patients and deaths in each state. The performance of the proposed prediction framework is determined by using three machine learning regression algorithms, namely Polynomial Regression (PR), Decision Tree Regression, and Random Forest (RF) Regression. The results show a comparative analysis of the states and union territories having more than 1000 cases, and the trained model is validated by testing it on further dates. The performance is evaluated using the RMSE metrics. The results show that the Polynomial Regression with an RMSE value of 0.08, shows the best performance in the prediction of the discharged patients. In contrast, in the case of prediction of deaths, Random Forest with a value of 0.14, shows a better performance than other techniques.

Measurement of price change is based on averages - also, hedonic price index numbers. As NSIs obtain scannerdata having varying content, question arise: what is correct average to use? We compare in this paper price change estimates based on most known averages and index numbers including quality adjusted methods. All analysis is done in logarithmic scale. The statistical analysis of our data is based on graphical presentations and mathematical analysis is presented mostly in our earlier papers.

We propose a simple anomaly detection method that is applicable to unlabeled time series data and is sufficiently tractable, even for non-technical entities, by using the density ratio estimation based on the state space model. Our detection rule is based on the ratio of log-likelihoods estimated by the dynamic linear model, i.e. the ratio of log-likelihood in our model to that in an over-dispersed model that we will call the NULL model. Using the Yahoo S5 data set and the Numenta Anomaly Benchmark data set, publicly available and commonly used benchmark data sets, we find that our method achieves better or comparable performance compared to the existing methods. The result implies that it is essential in time series anomaly detection to incorporate the specific information on time series data into the model. In addition, we apply the proposed method to unlabeled Web time series data, specifically, daily page view and average session duration data on an electronic commerce site that deals in insurance goods to show the applicability of our method to unlabeled real-world data. We find that the increase in page view caused by e-mail newsletter deliveries is less likely to contribute to completing an insurance contract. The result also suggests the importance of the simultaneous monitoring of more than one time series.

Background: There have been numerous control strategies employed to prevent the very physiological and psychological impacts of noise exposure in occupational settings. The present study aims to use the Shannon's entropy – PROMEHTEE hybrid model to weigh and prioritize noise control solutions in an oil refinery. Methods: This study was performed in four stages. At first, the sound pressure levels were measured and determined according to the recommended standard of ISO 9612. Next, criteria and sound control strategies were determined using the Delphi technique. In the third stage, the weights of the criteria were determined using the Shannon's entropy method and in the last stage, the solutions were prioritized using the PROMEHTEE method. Result: According to Shannon's entropy method, criteria were sorted by the priority order of cost-effectiveness (0.2710), efficiency in reducing the noise (0.2531), executability (0.2435), safety (0.1120), process interference (0.1107) and up-to-dateness of the methods (0.091). Also, according to the PROMEHTEE method, the best solution was determined to be building a noise chamber with a weight of 0.5476, and modification or change of the work process was ranked last with a weight of -0.6905. Conclusion: The Shannon's entropy – PROMEHTEE hybrid method introduced in this study can be used as a credible scientific tool and solution to select the most appropriate noise control solution in the oil industry and other industries in which a choice of noise control technique is felt as a necessity.

Misclassification of a binary response variable and nonrandom sample selection are data issues frequently encountered by empirical researchers. For cases in which both issues feature simultaneously in a data set, we formulate a sample selection model for a misclassified binary outcome in which the conditional probabilities of misclassification are allowed to depend on covariates. Assuming the availability of validation data, the pseudo-maximum likelihood technique can be used to estimate the model. The performance of the estimator accounting for misclassification and sample selection is compared to that of estimators offering partial corrections. An empirical example illustrates the proposed framework.

Background and aimPreterm birth is one of the major cause of neonatal death in the developing countries and environmental factors are playing vital role in pre term birth. Nowadays Machine learning techniques are very useful for finding the hidden factors and classifications. The purpose of this study is to illustrate the importance of machine learning classification models and to identify the significant environmental factors behind pre-term birth.MethodWe have used 90 pregnant mothers, of whom 40 are preterm and 50 are full-term births. We have checked the model accuracy of the dataset through logistic regression and decision tree classifier model.ResultsThe comparative outcome of the logistic and decision tree model reveals that logistic regression is stronger in terms of metrics (precision = 0.92, F1-score = 0.96 and AUROC = 0.97), while the weak result shows by the decision tree (precision = 0.75, F1-score = 0.86 and AUROC = 0.87).ConclusionsThe conclusion shows that logistic regression is more appropriate as compare to decision tree classification model in the preterm birth data. The most influential factors for preterm birth are variables like α -HCH, total HCH and MDA (Malondialdehyde).

Research objectives: This study aims to determine whether there is an influence between compensation, work motives and work environment on employee performance at PT. Torabika Eka Semesta Tangerang. The research method used is quantitative, when viewed from the level of explanation, this is an associative study with a casual relationship model. The population in this study were employees of PT. Torabika Eka Semesta, amounting to 100 respondents. The sampling technique used is the Probability Sampling method with simple random sampling technique, namely taking members and the population is done randomly without paying attention to the strata in the population. The data analysis technique used SPSS 22.0 (2013) software using multiple regression model parameter estimates and hypothesis testing was carried out by using the t test. The results show that the variable that has a major influence on employee performance is the work environment. Meanwhile, the variable that has a low influence on employee performance is compensation.

This paper proposes a test for the signal-to-noise ratio applicable to a range of significance tests and model diagnostics in a linear regression. It is particularly useful under a large or massive sample size, where a conventional test frequently rejects an economically negligible deviation from the null hypothesis. The test is conducted in the context of the traditional $F$-test, with its critical values increasing with sample size. It maintains desirable size properties under a large or massive sample size, when the null hypothesis is violated by a practically negligible margin.

This paper studies the hedging effectiveness of interest rate swaps using different reference rates for eliminating interest rate risk from floating rate loans. Two different reference rates are studied. The first is a reference rate whose maturity, ∆, matches the payment interval of the floating rate loan. The second is a reference rate whose maturity is ∆/N. The prime examples are LIBOR and SOFR, respectively. We show that the ∆-based interest rate swap provides a good static hedge, but the ∆/N-based swap does not. Although dynamic hedging with the ∆-based interest rate swap is possible under some conditions, it both introduces model risk and increases transaction costs, making it a less practical alternative.

We devise a new high-frequency covariance matrix estimator based on price durations which is guaranteed to be positive-definite. Both non-parametric and parametric versions are proposed. A comprehensive Monte Carlo simulation shows that this class of estimators are less biased, more efficient, and generate lower RMSE as well as QLIKE errors. Empirically, we apply both estimators to a global minimum variance portfolio allocation problem and find they can generate comparably low portfolio variance, higher Sharpe ratios, but with considerably lower portfolio turnovers. This matrix estimator is also shown empirically to be more well-conditioned.

In this paper, we ask which ex-ante characteristics empirically predict the out-of-sample drop in risk-adjusted performance of published stock anomalies. Our sample is a large cross- section of anomalies published in finance and academic journals, and we define out-of-sample as the post-publication period. Our set of predictors of OOS decay is inspired by two standard hypotheses: arbitrage capital flowing into newly published strategies, and in-sample overfitting linked to multiple hypothesis testing. The year of publication alone – compatible with both hypotheses – explains 30% of the variance of Sharpe decay across predictors: Every year, the Sharpe decay of newly-published factors increases by 5ppt. The other important variables are directly related to overfitting. One of our two “complexity” variables, the number of operations required to calculate the signal, predicts a significant drop of performance. Also, both measures of in-sample “sensitivity to outliers” are shown to matter. Together, these three overfitting- related variables add another 15% of explanatory power. Some arbitrage-related variables are statistically significant, but their predictive power is marginal.

This study aims to prove empirically the effect of work motivation on work productivity. This study used a sample of 66 employees of PT MNC Sky Vision KPP Padang with a proportionate stratified random sampling technique. The data used are primary data obtained through distributing questionnaires to selected employees. This study has two categories of variables. The first is the independent variable, namely work motivation. The two dependent variables are work productivity. The analytical method used is descriptive and quantitative analysis. The results of hypothesis testing found that work motivation has a significant effect on the work productivity of the employees of PT MNC Sky Vision KPP Padang.

In this study, an index namely Carbon-Neutral Readiness Index (CNRI) is designed to compare the carbon-neutral readiness among China’s 30 provinces. The CNRI is decomposed into a hierarchical structure that combines four dimensions and associated 19 quantitative indicators. Several indicators are newly proposed such as ‘per capita carbon trading volume’, ‘maturity degree of carbon capture and storage deployment’, and ‘percentage of ocean cover’. To determine the importance weight of indicators, an improved Criteria Importance Though Intercriteria Correlation (CRITIC) approach is developed by considering the correlation between indicators. Then, the weighted averaging operator is used to evaluate the CNRI of China’s provinces. Our results indicate that: 1) the correlation between most indicators is significant, of which 3.51% has an extremely strong relationship; 2) the indicator ‘per capita steel production’ owns the largest weight (0.113); 3) Beijing provinces ranks first, followed by Sichuan and Yunnan province; 4) the spatial differences of CNPI show a pattern of ‘strong in the central and south provinces, week in north provinces’; 5) the sensitive analysis reveals that the ranking results are relatively robust.

I establish the equivalence between the two-way fixed effects (TWFE) estimator and an estimator obtained from a pooled ordinary least squares regression that includes unit-specific time averages and time-period specific cross-sectional averages, which I call the two-way Mundlak (TWM) regression. This equivalence furthers our understanding of the anatomy of TWFE, and has several applications. The equivalence between TWFE and TWM implies that various estimators used for intervention analysis – with a common entry time into treatment or staggered entry, with or without covariates – can be computed using TWFE or pooled OLS regressions that control for time-constant treatment intensities, covariates, and interactions between them. The approach allows considerable heterogeneity in treatment effects across treatment intensity, calendar time, and covariates. The equivalence implies that standard strategies for heterogeneous trends are available to relax the common trends assumption. Further, the two-way Mundlak regression is easily adapted to nonlinear models such as exponential models and logit and probit models.

This paper introduces a theory based robust regression estimator, called the mOpt estimator, that minimizes the maximum bias with respect to a Tukey-Huber mixture model that includes a standard linear regression model with normally distribution errors as a special case, but also allows for a small fraction of unrestricted fat-tailed and skewed non-normal distribution variations from the standard model. The estimator has a very intuitive weighted least squares interpretation based on a data-dependent weight function that is equal to zero for robustly scaled prediction residuals that are larger in magnitude than 3.0, and thereby rejects outliers. We apply the robust regression method to single factor and multiple factor cross-section models for Size, BM, Beta and EP factors, and find that the robust regression results reverse the Fama-French 1992 (FF92) conclusions concerning the significance of the Size, Beta and EP factors. The difference in our results and those of FF92 is that the robust regression rejects approximately 4% to 5% of outliers, most of which, but not all, occur for microcap stocks, with smallcap stocks also having some influential outliers, and even largecaps have a few. We strongly recommend standard use of the mOpt estimator as an important complement to least squares for empirical asset pricing research, as well as for quantitative finance applications in general.

In this paper, we construct SEIPA model for the acute period of HIV-AIDS with delay time, stability analysis and numerical simulation of the SEIPA model on the spread of HIV-AIDS. The method used to build the model is a model of the acute time delay of HIV-AIDS using psychological therapy as a parameter of the time delay model, the analysis of the model using the mathematical graph method to obtain the basic reproduction number and the global stability of the HIV-AIDS spread model with time delay. The numerical model simulation uses secondary data on the number of HIV-AIDS cases in South Sulawesi. The results obtained are SEIPA model of acute time delay in the spread of HIV-AIDS; Model analysis produces global stability and information on the status of the spread of HIV-AIDS in South Sulawesi, which depends on parameter values. Then the simulation results provide an overview of the prediction of the number of HIV-AIDS cases and also shown that psychological therapy can delay the acute period of the spread of HIV-AIDS in South Sulawesi. The results obtained can be used as a reference for early prevention of the spread of HIV-AIDS in South Sulawesi

Individual responses to a treatment D=0,1 differ, depending on covariates X. Averaging such a heterogeneous effect is usually done with the density of X, but the average with `overlap weight (OW)' is done "surreptitiously" with X controlled, where OW is the normalized version of PS×(1-PS) with PS denoting the propensity score. This paper brings OW "out of shadow" to advocate more use of OW. OW attains its maximum at PS=0.5, i.e., when subjects in one group have the best overlap with the other group, and OW accords several advantages to treatment effect estimators. First, matching with OW addresses the well-known non-overlapping support problem in a built-in way, without an arbitrary user intervention. Second, inverse probability weighting with OW overcomes the "too small denominator problem", and can become efficient as well. Third, regression adjustment with OW is robust to misspecified outcome regression models. Fourth, covariate balance holds exactly if OW estimated by the method of moment is used. In these advantages, PS residual `D-PS' plays a central role. We also show how seemingly unrelated estimators are in fact closely related through OW, and an empirical analysis illustrates how similar the estimates can be.

This study aims to determine how the influence of trust, preceived ease quality of information towards online shopping decision. The research was carried out for three months, starting in March to June 2020. The research method used was survey method with approach of causality. The population in this study is the millennial population totaling 200 respondents. Data collection techniques using literature techniques and questionnaires. Data were analyzed using IBM SPSS Statistics 22.0 and AMOS 22.0. All independent variables are influence the dependent variable with critical value ratio (CR), but only one independent variables that accepted with P value. H1 shows that the trust variable has a critical value ratio (CR) of 2.815> 1.96 and a P value of 0.005 1.96 and a P value of 0.000 1.96 and a P value of 0.567> 0.05 then has a positive relationship between trust and online shopping decisions but the variable cannot be accepted.

This study aims to determine the effect of Service Quality, Price and Facilities on Visitor Satisfaction. This research was conducted for approximately 3 months from November to January 2020. The method used in this study is a quantitative analysis method through multiple linear regression and hypothesis testing is carried out by the T test, F test and the coefficient of determination. Data analysis in this study was carried out using IBM SPSS Statistics 23. The data source in this study was primary data with a population of students at the Faculty of Economics, State University of Jakarta with a sample of 100 people. The sampling used is the Probability Sampling method with random sampling, namely taking members and the population is done randomly without paying attention to the existing strata in the population. The results showed that Service Quality, Price and Facilities had a positive effect on Visitor Satisfaction in Ragunan.

This paper proposes a measure of multidimensional inequality of opportunity - the Kullback Leibler divergence index, based on the opportunity equality definition of Roemer (1993, 1998, 2016). We prove theoretically and numerically that this measure satisfies a larger set of properties mainly the additive decomposability by dimensions. We show that the Kullback Leibler divergence index for multiple dimensions can be decomposed into Kullback Leibler divergence index for each of these dimensions along with a measure of association which is interpreted as the mutual information Sugiyama et al. (2013). We provide an empirical application of our proposed method using the Indian Demographic and Health Survey 2015/16 data for adult males.

Starting from China, novel coronavirus is now the most dangerous threat to humans, all over the world. India is also not an exception. Besides using mask and sanitizers, Indian Government has also decided to maintain proper social distancing or lockdown. Our sentiment during this lockdown period also varied from man to man. In this paper, we have collected twitter data of people across India, during March to June and then using NLP, the polarity is measured, i.e. positive, negative or neutral. After that. We have used SVM classifier and Logistic Regression analysis to classify the sentiments of people. Python programing language is used in Anaconda distribution to simulate the result. After simulation, we have found that SVM gives 91.50% accuracy, whereas Logistic Regression gives 87.75% accuracy. At last, a comparative study between these two results, is also represented.

In light of the ongoing pandemic, many countries have struggled to implement massive testings due to their lack of resources. To minimize the number of tests required to diagnose a population, some countries have resorted to the Dorfman's pool testing procedure, a process that consists of pooling samples of different patients together, and then testing the merged sample as though it originated from the same subject; if the merged sample tests positive for infection, then each of its subjects is tested individually. Through this procedure, laboratories avoid conducting individual tests whenever the test for the pooled sample does not accuse infection. In this paper we study a very simple criterion used to match patients to form the pools. The procedure, which we call ordered pooling, consists of ordering patients from lowest to highest probability of infection, and then matching together those with similar probability of infection into groups of equal size in order to implement the Dorfman's procedure. We show that, keeping the pool size constant, this matching criterion minimizes the expected number of tests required to diagnose the population, and minimizes the expected number of healthy patients who are incorrectly diagnosed as infected, as long as the dilution effect is sufficiently small. The tradeoff is that in this case ordered pooling may not necessarily minimize the expected number of false negatives, namely, the expected number of cases in which sick patients are incorrectly diagnosed as healthy.

This paper proposes a simple statistic for testing statistical significance of R squared. This statistic, called "comparative R squared," also tests statistical significance of the difference of R squared between null and alternative models with additional explanatory variables. The asymptotic distribution of the test statistic is a chi square distribution with the degree of freedom equal to the number of (additional) explanatory variables. Unlike the F test, this statistic is useful when dealing with non-normality. Simulations show that the test statistic behaves well and has no distortion of size, even in small samples.

This study was entitled Correlates of Television Weather Forecast to Community Awareness on Disaster Preparation in San Leonardo, Nueva Ecija Province, Philippines. This study was designed and conducted to determine if television weather forecast could give a relationship to boost the level of awareness of people in terms of disaster preparation. The study was used a descriptive type of research design to describe the communication characteristics and television viewership of the respondents. Simple Random Sampling and Slovin’s formula was used in collecting data. Frequency, percentage, mean, standard deviation, and the Spearman correlation coefficient were used to analyze the data gathered. Two-hundred eighty respondents from two selected Barangays in San Leonardo, Nueva Ecija namely Barangay Tambo Adorable and Barangay Diversion respondents were chosen. Findings reveal that the respondents’ level of awareness on disaster preparation before, during and after typhoons were described as “highly aware.” In addition, viewed the weather forecast 1-2 times on television is the majority preferred of the respondents. Correlation between frequency of watching the weather forecast on television and the awareness of the respondents on disaster preparation reveals that 1-2 times of watching the weather forecast on television a day was also preferred by the respondents. The variables also indicated a more highly significant relationship.

The purpose of the present paper is to introduce a method for forecasting the daily number of COVID-19 associated deaths. We apply the Gumbel distribution function for the analysis of time series data of the first-wave outbreak. The Gumbel distribution function FG (t) has the property of having a mode (peak) point b, where FG (b) = 1 / 2.718. The probability density function of FG (t) has a single-peaked and right-skewed form. Using this distribution, we can estimate the total number of deaths N, and forecast daily numbers in the decreasing phase. We study the early-stage data of Italy, Canada, Belgium, Switzerland, the Netherlands, Sweden, Germany, China, France, and the United Kingdom. The data of New York City and England are also analyzed. In general

A new method for estimating the ages of Egyptian pyramids using a data-driven approach is described. Measurements of eight pyramids (Meidum, Bent, Red, Khufu, Khafre, Menkaure, Sahure, and Neferirkare) reveal correlations that can be exploited and used to estimate the ages of older pyramids from more recent ones. Preliminary results using a linear regression model support the possibility that some pyramids could have been built over pre-existing structures several hundred to several thousand years older than the pyramids themselves.

Here we present a combined early warning system and malaria predictor that can predict the 13 week trajectory of malaria cases in an primary health facility in Burkina Faso. Using the extraordinarily high fidelity data taken from the Integrated e-Diagnostic Approach (IeDA) system that has been rolled out throughout Burkina Faso, we train a combination of Gaussian Processes and Random Forest Regressors to estimate the trajectory of malaria cases over a 13 week period. We calibrate and test our algorithm such that it can return robust 1 and 2σ one-tailed and two-tailed confidence bounds. Given our lowest threshold for an epidemic alert our algorithm has 30% precision with > 99% recall. This rises to > 99% precision and 5% recall for the high alert threshold. Our two-tailed predictions have an average 1σ and 2σ precision of 5 cases and 30 cases respectively. Funding Statement: This work was in part funded by Cloudera Foundation, the Marguerite Foundation and the Delta ITP institute, and technically supported by Cloudera Foundation and Tableau Foundation. Declaration of Interests: MISSING

The association of body height and various outcomes has been studied in detail. While most studies find positive relationships linking additional height to more positive outcomes like higher life satisfaction or increased wages, the overall contribution of height to the variance decomposition of these outcomes is often neglected. Even when studies report measures of model fit like R², due to the design of the analytical models it is often not possible to isolate the additional contribution of height. The inclusion of other independent variables that are only marginally associated with height further obstructs the contribution of height. Reported values of explained variance, which are often below ten percent in many studies, are thus to be seen as upper bounds and the pure contribution of height must be lower, at least in developed nations. This is a serious knowledge gap and deserves more attention. Using European Social Survey data including 20 countries, our analyses demonstrate that the overall contribution of height to outcomes like life satisfaction, household income, prestige of the position in the labour market and years of education is lower than 0.012 with similar findings for men and women. When cross-national comparisons are inspected, there are only small country differences and just a few countries display marginally larger contributions which reach up to about five percentage points. These findings are relevant when putting the total effects of height into context.