Simple Regression Estimator Research Articles

A machine learning oracle for parameter estimation

AbstractCompeting procedures, involving data smoothing, weighting, imputation, outlier removal, etc., may be available to prepare data for parametric model estimation. Often, however, little is known about the best choice of preparatory procedure for the planned estimation and the observed data. A machine learning‐based decision rule, an “oracle,” can be constructed in such cases to decide the best procedure from a set of available preparatory procedures. The oracle learns the decision regions associated with based on training data synthesized solely from the given data using model parameters with high posterior probability. An estimator in combination with an oracle to guide data preparation is called an oracle estimator. Oracle estimator performance is studied in two estimation problems: slope estimation in simple linear regression (SLR) and changepoint estimation in continuous two‐linear‐segments regression (CTLSR). In both examples, the regression response is given to be increasing, and the oracle must decide whether to isotonically smooth the response data preparatory to fitting the regression model. A measure of performance called headroom is proposed to assess the oracle's potential for reducing estimation error. Experiments with SLR and CTLSR find for important ranges of problem configurations that the headroom is high, the oracle's empirical performance is near the headroom, and the oracle estimator offers clear benefit.

ANALYSES OF BANK PERFORMANCE ON FINANCIAL SYSTEM STABILITY IN INDONESIA

Institutions with high risk in their business. Large banks have a close relationship with bank risk. This study aimed to determine the effect of Commercial Bank Business Group with a minimum core capital of Rp. 30 trillion (the 4th CBBG) banks' performance in Indonesia on Indonesia's financial system stability using secondary data for 2005-2020. This study uses a simple panel regression estimation, namely the Common Effect Model, Fixed Effect Model, and Random Effect Model. The results showed that the NPL, LDR and NII, positively affected financial system stability, while the BOPO negative affected.

Robust estimation of dementia prevalence from two-phase surveys with non-responders via propensity score stratification

BackgroundMissing diagnoses are common in cross-sectional studies of dementia, and this missingness is usually related to whether the respondent has dementia or not. Failure to properly address this issue can lead to underestimation of prevalence. To obtain accurate prevalence estimates, we propose different estimation methods within the framework of propensity score stratification (PSS), which can significantly reduce the negative impact of non-response on prevalence estimates.MethodsTo obtain accurate estimates of dementia prevalence, we calculated the propensity score (PS) of each participant to be a non-responder using logistic regression with demographic information, cognitive tests and physical function variables as covariates. We then divided all participants into five equal-sized strata based on their PS. The stratum-specific prevalence of dementia was estimated using simple estimation (SE), regression estimation (RE), and regression estimation with multiple imputation (REMI). These stratum-specific estimates were integrated to obtain an overall estimate of dementia prevalence.ResultsThe estimated prevalence of dementia using SE, RE, and REMI with PSS was 12.24%, 12.28%, and 12.20%, respectively. These estimates showed higher consistency than the estimates obtained without PSS, which were 11.64%, 12.33%, and 11.98%, respectively. Furthermore, considering only the observed diagnoses, the prevalence in the same group was found to be 9.95%, which is significantly lower than the prevalence estimated by our proposed method. This suggested that prevalence estimates obtained without properly accounting for missing data might underestimate the true prevalence.ConclusionEstimating the prevalence of dementia using the PSS provides a more robust and less biased estimate.

A dual risk perspective of China's resources market: Geopolitical risk and political risk

Currently, literature is exploring the factors affecting the economic growth of countries and regions. However, there is still a gap in empirical research regarding the connection between risk factors, natural resources, and economic growth, particularly in emerging economies. This study examines the influence of geopolitical risk, political risk, natural resources, and exports on China's economic development during 1983–2021. The Bayer-Hanck combined cointegration results verified the cointegration association between the variables. Using a novel method of movement quantile regression, this study found that geopolitical risk, mineral rents, and exports are positive, while political risk and oil rents adversely affect China's economic growth. These results are robust and consistent as validated by parametric (dynamic ordinary least square, canonical cointegration regression, and fully modified ordinary least square) and non-parametric (bootstrap quantile regression and simple quantile regression) estimators. Based on the empirical results, this study suggested lowering the risk factors and efficiently exploiting and utilizing natural resources for sustainable development.

Triangle Sampling Based Estimation of Average Degree of Network Using Different Estimator

Graph are use to represent complex relationship which includes online social network, pandemic spread network and other real world networks. In recent time, graph sampling is used for study of different parameters of network, such includes many sampling algorithms like Random node, Random edge sampling, Rank degree, etc. which are used for collection of subset of network, but efficient estimation methods are not discussed much for parameter estimation. This paper presents a comparison of different estimators comprises Simple random sampling, Ratio and Regression estimator to estimate the average degree of network. To collect sample technique of triangles are collected using seed nodes. A comparative procedure is used to obtain the lower and upper limit of confidence intervals with the help of multiple samples. Ogive based simulation is also used for single value computation of both limits of confidence interval(CI). The results obtained from simulation, show that Regression estimator is more efficient than the Ratio and Simple random sampling based estimators.

Sampling-Based Approaches to Estimating Two-Dimensional Large Wood Area from UAS Imagery

Large in-stream wood (LW) is a critical component of riparian systems that increases heterogeneity of flow regimes and provides high habitat for salmonids and other fishes. We present four sampling-based methods to estimate two-dimensional LW for a 61-hectare river restoration project on the South Fork McKenzie River near Rainbow, OR (USA). We manually delineated LW area, from unoccupied aircraft systems (UAS) multispectral imagery for 40 randomly selected 51.46 m2 hexagonal plots. Seven auxiliary variables were extracted from the imagery and imagery derivatives to be incorporated in four estimators by summarizing spectral statistics for each plot including Random forest (RF) classification of segmented imagery (Cohen’s kappa = 0.75, balanced accuracy = 0.86). The four estimators were: difference estimator, simple linear regression estimator with one auxiliary variable, general regression estimator with seven auxiliary variables, and simple random sample without replacement. We assessed variance of the estimators and found that the simple random sample without replacement produced the largest estimate for LW area and widest confidence interval (17,283 m2, 95% CI 10,613 - 23,952 m2) while the generalized regression approach resulted in the smallest estimate and narrowest confidence interval (16,593 m2, 95% CI 13,054 - 20,133 m2). These methods facilitate efficient estimates of critical habitat components, that are especially suited to efforts that seek to quantify large amounts of these components through time. When combined with traditional sampling methods, classified imagery acquired via UAS promises to enhance the temporal resolution of the data products associated with restoration efforts while minimizing the necessity for potentially hazardous field work.

New Evidence on the Jensen Hypothesis: A Note for Social Scientists

A recent paper by Lasker et al. provides a gold standard test for genetic causes of the US Black/white test score gap by simple regression estimation linking high-quality intelligence test scores to African/European genetic admixture proportions precisely measured with modern DNA technology. The paper gives new, empirically powerful evidence for a large genetic component to the gap. This note critically reviews some key findings of the paper from a social science perspective.

Ln-Type Variance Estimators in Simple Random Sampling

Until now, various types of estimators have been used for estimating the population variance in simple random sampling studies, including ratio, product, regression and exponential-type estimators. In this article, we propose a family of -type estimators for the first time in the simple random sampling and show that they are more efficient than the other types of estimators under certain conditions obtained theoretically. Numerical illustrations and a simulation study support our findings in theory. In addition, it has been shown how to determine the optimal points in order to reach the minimum MSE values with the properties of the ln-type estimators in the different data sets.

Quantile treatment effects and bootstrap inference under covariate‐adaptive randomization

In this paper, we study the estimation and inference of the quantile treatment effect under covariate‐adaptive randomization. We propose two estimation methods: (1) the simple quantile regression and (2) the inverse propensity score weighted quantile regression. For the two estimators, we derive their asymptotic distributions uniformly over a compact set of quantile indexes, and show that, when the treatment assignment rule does not achieve strong balance, the inverse propensity score weighted estimator has a smaller asymptotic variance than the simple quantile regression estimator. For the inference of method (1), we show that the Wald test using a weighted bootstrap standard error underrejects. But for method (2), its asymptotic size equals the nominal level. We also show that, for both methods, the asymptotic size of the Wald test using a covariate‐adaptive bootstrap standard error equals the nominal level. We illustrate the finite sample performance of the new estimation and inference methods using both simulated and real datasets.

Estimation and hypotheses testing in boundary regression models

Consider a nonparametric regression model with one-sided errors and regression function in a general Hölder class. We estimate the regression function via minimization of the local integral of a polynomial approximation. We show uniform rates of convergence for the simple regression estimator as well as for a smooth version. These rates carry over to mean regression models with a symmetric and bounded error distribution. In such a setting, one obtains faster rates for irregular error distributions concentrating sufficient mass near the endpoints than for the usual regular distributions. The results are applied to prove asymptotic $\sqrt{n}$-equivalence of a residual-based (sequential) empirical distribution function to the (sequential) empirical distribution function of unobserved errors in the case of irregular error distributions. This result is remarkably different from corresponding results in mean regression with regular errors. It can readily be applied to develop goodness-of-fit tests for the error distribution. We present some examples and investigate the small sample performance in a simulation study. We further discuss asymptotically distribution-free hypotheses tests for independence of the error distribution from the points of measurement and for monotonicity of the boundary function as well.

Japanese R&D Profitability

This article examines the contribution to profit from research and development (R&D) using industry-level accounting panel data for eight industries in Japan from 1986 to 2012. Problematically, simple least-squares regression estimation of production functions, where the authors specify sales or value-added as the explained variable and investment in R&D as the explanatory variable, involve endogeneity. Two possible ways of addressing this are the instrumental variables method and another that utilizes the orthogonality between error terms and appropriately time-lagged explanatory variables. The authors compare how both these methods eliminate endogeneity in the estimated production function and thus improve the accuracy of estimates of the rate of return on R&D. These findings thus contribute to the managerial decision-making process on R&D strategy by providing insights into the precise contribution of firm R&D to profit.

A critical examination of government budgeting and public funds management in Nigeria

PurposeThe purpose of this paper is to attempt an empirical examination of government budgeting and expenditure processes in Nigeria, a developing country. It examines the current state of budgeting and public funds management (PFM) in Nigeria. It also examines the extent to which the government has used the budgetary mechanism to effectively manage the nation’s economy.Design/methodology/approachThe paper employed simple regression estimation technique for data analysis. Time series data set of budgetary information was constructed from different archival sources over a 16-years period (2000-2015), majorly the national Appropriation Acts, press releases, regulatory and governmental reports, reports of Transparency International, World Bank and Central Intelligence Agency.FindingsThe findings confirm that the nation’s annual budgeting approach is defective and lags in achieving its fiscal objectives. The budget indicates a state of poor accountability and transparency in PFM. Findings also suggest that the level of economic development in Nigeria is not commensurate with the size of government expenditure.Practical implicationsThe paper draws the attention of the government to the need to restructure its approach to budgeting and adopt a more resilient approach that suits its environment and economic peculiarities in effort to ensure efficient management and accountability of public funds. The paper also offers value to other developing countries. It provides empirical evidence that explains an aspect why the African continent remains underdeveloped hitherto.Originality/valueThis paper lends a voice to the call for a restructuring of the Nigerian budgetary system and its implementation strategy. It advocates for the adoption of an alternative budgeting approach that matches Nigeria economic realities. The paper demonstrated that the traditional budgetary approach being used by many developing countries is limited in certain ways and could hinder sustainable development.

Strategic management accounting and decision making: A survey of the Nigerian Banks

This paper attempts an empirical examination of the practicality of strategic management accounting (SMA) adoption in banks in Nigeria, a developing country. It investigates the extent of practice and contributions of SMA to strategic decision making. Survey data were obtained from 71 bank managers across 20 registered banks in Nigeria. Simple regression estimation technique and Pearson Chi-square test were used for data analysis. The results suggest that SMA is distinct in its features and orientation towards the practice of management accounting. The study found out that banks in Nigeria practice SMA; not as a concept, but as a principle of operation, and that SMA contributes significantly to strategic decision making in the area of competitive advantage and increased market share. The study offers value for banks in other developing economies in that it supports the argument that they can benefit from SMA adoption as part of banking strategies.

The Impact of the Small Business Lending Fund on Community Bank Lending to Small Businesses

Following the financial crisis, total outstanding loans to businesses by commercial banks dropped off substantially. Large loans outstanding began to rebound by the third quarter of 2010 and essentially returned to their previous growth trajectory while small loans outstanding continued to decline. Furthermore, much of the drop in small business loans outstanding was evident at community banks. To address this perceived lack of supply of credit to small businesses, the Small Business Lending Fund (SBLF) was created as part of the 2010 Small Business Jobs Act. The fund was intended to provide community banks with low-cost funding that they could then lend to their small business customers. As of December 31, 2013, the U.S. Department of the Treasury reports that SBLF participants had increased their small business lending by $12.5 billion over their baseline numbers. The current paper uses Call Report data from community banks an d thrift institutions to look at the impact of receiving funds from SBLF on their small business lending. The analysis controls for economic and demographic conditions, market structure and competition. Simple regression estimates indicate that participants in the SBLF program increased their small business lending by about 10 percent more than their non-participating counterparts, in line with numbers reported by Treasury. However, estimates that control for the ongoing growth path in small business lending indicate no statistically significant impact of SBLF participation on small business lending.

The Impact of the Small Business Lending Fund on Community Bank Lending to Small Businesses

Following the financial crisis, total outstanding loans to businesses by commercial banks dropped off substantially. Large loans outstanding began to rebound by the third quarter of 2010 and essentially returned to their previous growth trajectory while small loans outstanding continued to decline. Furthermore, much of the drop in small business loans outstanding was evident at community banks. To address this perceived lack of supply of credit to small businesses, the Small Business Lending Fund (SBLF) was created as part of the 2010 Small Business Jobs Act. The fund was intended to provide community banks with low‐cost funding that they could then lend to their small business customers. As of 31 December, 2013, the US Department of the Treasury reports that SBLF participants had increased their small business lending by $12.5 billion over their baseline numbers. The current paper uses Call Report data from community banks and thrift institutions to look at the impact of receiving funds from SBLF on their small business lending. The analysis controls for economic and demographic conditions, market structure and competition. Simple regression estimates indicate that participants in the SBLF program increased their small business lending by about 10 percent more than their non‐participating counterparts, in line with numbers reported by Treasury. However, estimates that control for the ongoing growth path in small business lending indicate no statistically significant impact of SBLF participation on small business lending.

An Assessment of Valuation Methods of Stock Initial Public offerings on Tehran Stock Exchange

Every day hundreds of companies all over the world are entering capital market for the first time by issuing stocks. By doing so, they decide to invest capital necessary for continuing activity and expanding operations accordingly. For this reason, it is important to the companies that price specified for their stock demonstrate real value of assets and their growth and development opportunities in the future. The purpose of this research is to study valuation methods of stock initial public offerings at the Tehran Stock Exchange. Population and study sample consisted of firms publicly offered their stocks for the first time at Tehran Stock Exchange during 2009-2014, and experienced no trading halt, i.e. 45 companies of which seven companies were eliminated due to lack of trading on the stock exchange and 38 firms were chosen. The research method is correlational-descriptive and the research is an applied research by purpose. The research is based on cross-sectional time regression analysis, for which simple multivariate regression estimation based on ordinary least square method was used, and models under consideration were examined by Eviews and SPSS. After analysis of results, the research findings showed that some variables such as firm size have no influence on the price of initial public offering but variables such as risk, size of initial offerings, amount of firm capital, etc. have significant effect on the price of stock initial public offering. In the end, it is recommended that companies price their stocks in the way that it paves the way for growth and development of their assets, given that there are a lot of factors and variables that affect the price of stock initial offering.

Regression Approach to Parameter Estimation of an Exponential Software Reliability Model

Mathematical studies about the likelihood of failures of software systems have been advanced by various researchers. These studies have modeled the behavior of software systems by using failure times and time between failures in the past. The Goel-Okumoto software reliability model is amongst the many software reliability models proposed to model the failure behavior of software systems. To be able to use the model in software reliability assessment, it is important to estimate its parameters α and β and the intensity function λ(t). In this paper, classical parametric regression methods have been utilized in the estimation of the parameters α and β, the intensity function and the mean time between failures of the Goel-Okumoto software reliability model. The parameters α and β and the mean time between failures (MTBF) of the Goel-Okumoto software model have been estimated using the maximum likelihood estimation (MLE) method, regression approach applied to the model and simple linear regression model without assuming the Goel-Okumoto model. When these three estimation methods were validated using root mean squared error (RMSE) and mean absolute value difference (MAVD), which are the common error measurement criteria, regression approach applied to the Goel-Okumoto model outperformed MLE and simple linear regression estimation methods.

Financial Policies between Chaebol and Non-Chaebol Firms

In this research, we investigate whether chaebol affiliations have a different financial policy compared with non-chaebol firms, in debt level, cash-holding level, and investment decision policy. Our results indicate that 8 variables out of 10 show significant differences in terms of the directions and magnitudes of the coefficients of independent variables between chaebol and non-chaebol firms for debt level policy. For the cash-holding policy, 3 variables show differences; and for investment policy, only 1 variable shows a significant difference between chaebol and non-chaebol firms. In this paper, we use regression models and the T-test to confirm the existence of differences in financial polices, mainly between chaebol and non-chaebol firms. When comparing the coefficients from two different regression estimators, by using the T-test, we find more compelling evidence that chaebol and non-chaebol firms have different financial policies than prior research. This is because, by using the T-test, we can obtain more solid evidence of whether firms’ financial policies are influenced by firms’ chaebol group membership; and by using this method, we often find inconsistent results with previous research. We believe that a lot of previous research should be revised using our method, as our T-test often disagrees with the results found by previous research that only use simple regression estimators.

Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods

In this paper, simple regression estimates and factor-based models are utilised to produce forecasts for Bahrain quarterly gross domestic product growth. Using simulated out-of-sample experiments, we assess and compare the performance of the simple regression estimates, which exploit the available information on selected indicator variables, with factor-based estimates. These estimates use up to 65 variables to obtain new factors that embody most of the potential information and handle it in a systematic way following the Stock–Watson approach. Additionally, we compare the performance of the nowcast factor MIDAS with the quarterly factor (static-SW) models based on time-aggregated data, which neglect the most recent information. Our empirical findings can be summarised as follows. First, using more information does not help to produce more accurate results. Preselected indicator variables can clearly improve the forecast performance in comparison with the use of large dataset. Second, quarterly factor models are in general outperformed by the nowcast factor models that directly relate low-frequency data to those of high frequency. Third, the best forecasting performance can be reached using simple regression estimates with a handful of variables. However, it fails in density forecast evaluation tests. Thus, the alternative factor-MIDAS model is considered as an optimal model that passes all the performance evaluation tests. Fourth, concerning the difference between MIDAS projection methods, the results indicate that MIDAS with exponential distributed lag functions outperforms the MIDAS with unrestricted lag polynomials. The best performing projection based on the number of factors is the model with three factors. They can pick up the rapid switch in the utility of the indicators automatically. Finally, although the most accurate Flash estimates are obtained at 84 days, nowcasting using industrial production into a bridge equation witnessed insignificant loss in accuracy at 54 days only.

The Impact of the Small Business Lending Fund on Community Bank Lending to Small Businesses

Following the financial crisis, total outstanding loans to businesses by commercial banks dropped off substantially. Large loans outstanding began to rebound by the third quarter of 2010 and essentially returned to their previous growth trajectory while small loans outstanding continued to decline. Furthermore, much of the drop in small business loans outstanding was evident at community banks. To address this perceived lack of supply of credit to small businesses, the Small Business Lending Fund (SBLF) was created as part of the 2010 Small Business Jobs Act. The fund was intended to provide community banks with low-cost funding that they could then lend to their small business customers. As of December 31, 2013, the U.S. Department of the Treasury reports that SBLF participants had increased their small business lending by $12.5 billion over their baseline numbers. The current paper uses Call Report data from community banks an d thrift institutions to look at the impact of receiving funds from SBLF on their small business lending. The analysis controls for economic and demographic conditions, market structure and competition. Simple regression estimates indicate that participants in the SBLF program increased their small business lending by about 10 percent more than their non-participating counterparts, in line with numbers reported by Treasury. However, estimates that control for the ongoing growth path in small business lending indicate no statistically significant impact of SBLF participation on small business lending.