scholarly journals Leveraging Lotteries for School Value-Added: Testing and Estimation*

2017 ◽  
Vol 132 (2) ◽  
pp. 871-919 ◽  
Author(s):  
Joshua D. Angrist ◽  
Peter D. Hull ◽  
Parag A. Pathak ◽  
Christopher R. Walters
Keyword(s):  
Mean Squared Error ◽  
Value Added ◽  
Ordinary Least Squares ◽  
Test Results ◽  
Achievement Gains ◽  
Hybrid Estimation ◽  
Squared Error ◽  
Value Added Models ◽  
Quasi Experimental ◽  
Substantive Importance

Abstract Conventional value-added models (VAMs) compare average test scores across schools after regression-adjusting for students’ demographic characteristics and previous scores. This article tests for VAM bias using a procedure that asks whether VAM estimates accurately predict the achievement consequences of random assignment to specific schools. Test results from admissions lotteries in Boston suggest conventional VAM estimates are biased, a finding that motivates the development of a hierarchical model describing the joint distribution of school value-added, bias, and lottery compliance. We use this model to assess the substantive importance of bias in conventional VAM estimates and to construct hybrid value-added estimates that optimally combine ordinary least squares and lottery-based estimates of VAM parameters. The hybrid estimation strategy provides a general recipe for combining nonexperimental and quasi-experimental estimates. While still biased, hybrid school value-added estimates have lower mean squared error than conventional VAM estimates. Simulations calibrated to the Boston data show that, bias notwithstanding, policy decisions based on conventional VAMs that control for lagged achievement are likely to generate substantial achievement gains. Hybrid estimates that incorporate lotteries yield further gains.

THE IMPROVED BPNN-NAR AND BPNN-NARMA MODELS ON MALAYSIAN AGGREGATE COST INDICES WITH OUTLYING DATA

2016 ◽  
Vol 78 (12-3) ◽  
Author(s):  
Saadi Ahmad Kamaruddin ◽  
Nor Azura Md Ghani ◽  
Norazan Mohamed Ramli
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mean Squared Error ◽  
Ordinary Least Squares ◽  
Series Data ◽  
Backpropagation Neural Network ◽  
Data Set ◽  
Squared Error ◽  
The Impact ◽  
Nonlinear Autoregressive

Neurocomputing have been adapted in time series forecasting arena, but the presence of outliers that usually occur in data time series may be harmful to the data network training. This is because the ability to automatically find out any patterns without prior assumptions and loss of generality. In theory, the most common training algorithm for Backpropagation algorithms leans on reducing ordinary least squares estimator (OLS) or more specifically, the mean squared error (MSE). However, this algorithm is not fully robust when outliers exist in training data, and it will lead to false forecast future value. Therefore, in this paper, we present a new algorithm that manipulate algorithms firefly on least median squares estimator (FFA-LMedS) for  Backpropagation neural network nonlinear autoregressive (BPNN-NAR) and Backpropagation neural network nonlinear autoregressive moving (BPNN-NARMA) models to reduce the impact of outliers in time series data. The performances of the proposed enhanced models with comparison to the existing enhanced models using M-estimators, Iterative LMedS (ILMedS) and Particle Swarm Optimization on LMedS (PSO-LMedS) are done based on root mean squared error (RMSE) values which is the main highlight of this paper. In the meanwhile, the real-industrial monthly data of Malaysian Aggregate cost indices data set from January 1980 to December 2012 (base year 1980=100) with different degree of outliers problem is adapted in this research. At the end of this paper, it was found that the enhanced BPNN-NARMA models using M-estimators, ILMedS and FFA-LMedS performed very well with RMSE values almost zero errors. It is expected that the findings would assist the respected authorities involve in Malaysian construction projects to overcome cost overruns.

Enhanced BFGS Quasi-Newton Backpropagation Models on MCCI Data

2017 ◽  
Vol 8 (1) ◽  
pp. 101 ◽  
Author(s):  
Nor Azura Md. Ghani ◽  
Saadi Ahmad Kamaruddin ◽  
Norazan Mohammed Ramli ◽  
Ismail Musirin ◽  
Hishamuddin Hashim
Keyword(s):  
Mean Squared Error ◽  
Ordinary Least Squares ◽  
Cost Index ◽  
Monthly Data ◽  
Squared Error ◽  
Information Time ◽  
The Mean ◽  
Quasi Newton ◽  
Nonlinear Autoregressive ◽  
Mean Time

<p>Neurocomputing is widely implemented in time series area, however the nearness of exceptions that for the most part happen in information time arrangement might be hurtful to the information organize preparing. This is on the grounds that the capacity to consequently discover any examples without earlier suppositions and loss of all-inclusive statement. In principle, the most well-known preparing calculation for Backpropagation calculations inclines toward lessening ordinary least squares estimator (OLS) or all the more particularly, the mean squared error (MSE). In any case, this calculation is not completely hearty when exceptions exist in preparing information, and it will prompt false estimate future esteem. Along these lines, in this paper, we show another calculation that control calculations firefly on slightest middle squares estimator (FFA-LMedS) for BFGS quasi-newton backpropagation neural network nonlinear autoregressive moving (BPNN-NARMA) model to lessen the effect of exceptions in time arrangement information. In the in the mean time, the monthly data of Malaysian Roof Materials cost index from January 1980 to December 2012 (base year 1980=100) with various level of exceptions issue is adjusted in this examination. Toward the finish of this paper, it was found that the upgraded BPNN-NARMA models utilizing FFA-LMedS performed extremely well with RMSE values just about zero errors. It is expected that the finding would help the specialists in Malaysian development activities to handle cost indices data accordingly.</p>

scholarly journals Risk Comparison of Improved Estimators in a Linear Regression Model with MultivariatetErrors under Balanced Loss Function

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Guikai Hu ◽  
Qingguo Li ◽  
Shenghua Yu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Loss Function ◽  
Linear Regression Model ◽  
Mean Squared Error ◽  
Ordinary Least Squares ◽  
Balanced Loss Function ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Improved Estimators

Under a balanced loss function, we derive the explicit formulae of the risk of the Stein-rule (SR) estimator, the positive-part Stein-rule (PSR) estimator, the feasible minimum mean squared error (FMMSE) estimator, and the adjusted feasible minimum mean squared error (AFMMSE) estimator in a linear regression model with multivariateterrors. The results show that the PSR estimator dominates the SR estimator under the balanced loss and multivariateterrors. Also, our numerical results show that these estimators dominate the ordinary least squares (OLS) estimator when the weight of precision of estimation is larger than about half, and vice versa. Furthermore, the AFMMSE estimator dominates the PSR estimator in certain occasions.

scholarly journals Using School Choice Lotteries to Test Measures of School Effectiveness

2014 ◽  
Vol 104 (5) ◽  
pp. 406-411 ◽  
Author(s):  
David J. Deming
Keyword(s):  
School Choice ◽  
School Effectiveness ◽  
Predictive Power ◽  
School Attendance ◽  
Value Added ◽  
Causal Effects ◽  
Achievement Gains ◽  
Public School Choice ◽  
Value Added Models ◽  
Selection Of

Value-added models (VAMs) are increasingly used to measure school effectiveness. Yet, random variation in school attendance is necessary to test the validity of VAMs and to guide the selection of models for measuring causal effects of schools. In this paper, I use random assignment from a public school choice lottery to test the predictive power of VAM specifications. In VAMs with minimal controls and two or more years of prior data, I fail to reject the hypothesis that school effects are unbiased. Overall, many commonly used VAMs are accurate predictors of student achievement gains.

scholarly journals Uncertainty in Rank Estimation: Implications for Value-Added Modeling Accountability Systems

2002 ◽  
Vol 27 (3) ◽  
pp. 255-270 ◽  
Author(s):  
J.R. Lockwood ◽  
Thomas A. Louis ◽  
Daniel F. McCaffrey
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Decision Rules ◽  
Value Added ◽  
Operating Characteristics ◽  
Squared Error ◽  
Variance Ratios ◽  
Value Added Modeling ◽  
Optimal Procedures

Accountability for public education often requires estimating and ranking the quality of individual teachers or schools on the basis of student test scores. Although the properties of estimators of teacher-or-school effects are well established, less is known about the properties of rank estimators. We investigate performance of rank (percentile) estimators in a basic, two-stage hierarchical model capturing the essential features of the more complicated models that are commonly used to estimate effects. We use simulation to study mean squared error (MSE) performance of percentile estimates and to find the operating characteristics of decision rules based on estimated percentiles. Each depends on the signal-to-noise ratio (the ratio of the teacher or school variance component to the variance of the direct, teacher- or school-specific estimator) and only moderately on the number of teachers or schools. Results show that even when using optimal procedures, MSE is large for the commonly encountered variance ratios, with an unrealistically large ratio required for ideal performance. Percentile-specific MSE results reveal interesting interactions between variance ratios and estimators, especially for extreme percentiles, which are of considerable practical import. These interactions are apparent in the performance of decision rules for the identification of extreme percentiles, underscoring the statistical and practical complexity of the multiple goal inferences faced in value-added modeling. Our results highlight the need to assess whether even optimal percentile estimators perform sufficiently well to be used in evaluating teachers or schools.

scholarly journals Implementation multiple linear regresion in neural network predict gold price

2021 ◽  
Vol 22 (3) ◽  
pp. 1635
Author(s):  
Musli Yanto ◽  
Sigit Sanjaya ◽  
Yulasmi Yulasmi ◽  
Dodi Guswandi ◽  
Syafri Arlis
Keyword(s):  
Mean Squared Error ◽  
Gold Price ◽  
Predictor Variables ◽  
Test Results ◽  
Price Prediction ◽  
Calculation Technique ◽  
Squared Error ◽  
Mathematical Calculation ◽  
Price Movements ◽  
Network Pattern

<p>The movement of gold prices in the previous period was crucial for investors. However, fluctuations in gold price movements always occur. The problem in this study is how to apply multiple linear regression (MRL) in predicting artificial neural networks (ANN) of gold prices. MRL is mathematical calculation technique used to measure the correlation between variables. The results of the MRL analysis ensure that the network pattern that is formed can provide precise and accurate prediction results. In addition, this study aims to develop a predictive pattern model that already exists. The results of the correlation test obtained by MRL provide a correlation of 62% so that the test results are said to have a significant effect on gold price movements. Then the prediction results generated using an ANN has a mean squared error (MSE) value of 0.004264%. The benefits obtained in this study provide an overview of the gold price prediction pattern model by conducting learning and approaches in testing the accuracy of the use of predictor variables.</p>

Added Value of Subscores and Hypothesis Testing

2018 ◽  
Vol 44 (1) ◽  
pp. 25-44
Author(s):  
Sandip Sinharay
Keyword(s):  
Hypothesis Testing ◽  
Mean Squared Error ◽  
Statistical Tests ◽  
Value Added ◽  
Added Value ◽  
Data Sets ◽  
Squared Error ◽  
Operational Data ◽  
Proportional Reduction

The value-added method of Haberman is arguably one of the most popular methods to evaluate the quality of subscores. According to the method, a subscore has added value if the reliability of the subscore is larger than a quantity referred to as the proportional reduction in mean squared error of the total score. This article shows how well-known statistical tests can be used to determine the added value of subscores and augmented subscores. The usefulness of the suggested tests is demonstrated using two operational data sets.

Teacher Effects, Value-Added Models, and Accountability

2014 ◽  
Vol 116 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Spyros Konstantopoulos
Keyword(s):  
Student Achievement ◽  
Empirical Evidence ◽  
Statistical Models ◽  
Value Added ◽  
Unique Contribution ◽  
Teacher Effects ◽  
Achievement Gains ◽  
Value Added Models ◽  
The Stability ◽  
Value Added Measures

Background In the last decade, the effects of teachers on student performance (typically manifested as state-wide standardized tests) have been re-examined using statistical models that are known as value-added models. These statistical models aim to compute the unique contribution of the teachers in promoting student achievement gains from grade to grade, net of student background and prior ability. Value-added models are widely used nowadays and they are used by some states to rank teachers. These models are used to measure teacher performance or effectiveness (via student achievement gains), with the ultimate objective of rewarding or penalizing teachers. Such practices have resulted in a large amount of controversy in the education community about the role of value-added models in the process of making important decisions about teachers such as salary increases, promotion, or termination of employment. Purpose The purpose of this paper is to review the effects teachers have on student achievement, with an emphasis on value-added models. The paper also discusses whether value-added models are appropriately used as a sole indicator in evaluating teachers’ performance and making critical decisions about teachers’ futures in the profession. Research Design This is a narrative review of the literature on teacher effects that includes evidence about the stability of teacher effects using value-added models. Conclusions More comprehensive systems for teacher evaluation are needed. We need more research on value-added models and more work on evaluating value-added models. The strengths and weaknesses of these models should be clearly described. We also need much more empirical evidence with respect to the reliability and the stability of value-added measures across different states. The findings thus far do not seem robust and conclusive enough to warrant decisions about raises, tenure, or termination of employment. In other words, it is unclear that the value-added measures that inform the accountability system are adequate. It is not obvious that we are better equipped now to make such important decisions about teachers than we were 35 years ago. Good et al. have argued that we need well-thought-out and well-developed criteria that guide accountability decisions. Perhaps such criteria should be standardized across school districts and states. That would ensure that empirical evidence across different states is comparable and would help determine whether findings converge or diverge.

scholarly journals PMSE PERFORMANCE OF THE BIASED ESTIMATORS IN A LINEAR REGRESSION MODEL WHEN RELEVANT REGRESSORS ARE OMITTED

2002 ◽  
Vol 18 (5) ◽  
pp. 1086-1098 ◽  
Author(s):  
Akio Namba
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Mean Squared Error ◽  
Ordinary Least Squares ◽  
Positive Part ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Explicit Formulae ◽  
Biased Estimators

In this paper, we consider a linear regression model when relevant regressors are omitted. We derive the explicit formulae for the predictive mean squared errors (PMSEs) of the Stein-rule (SR) estimator, the positive-part Stein-rule (PSR) estimator, the minimum mean squared error (MMSE) estimator, and the adjusted minimum mean squared error (AMMSE) estimator. It is shown analytically that the PSR estimator dominates the SR estimator in terms of PMSE even when there are omitted relevant regressors. Also, our numerical results show that the PSR estimator and the AMMSE estimator have much smaller PMSEs than the ordinary least squares estimator even when the relevant regressors are omitted.

scholarly journals Memprediksi Harga Komoditas Cabe Menggunakan Metode Backpropagation di Wilayah Kota Payakumbuh

KOMTEKINFO ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 33-48
Author(s):  
Allans Prima Aulia ◽  
Yuhandri ◽  
Fhajri Arye Gemilang
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Prediction Accuracy ◽  
Mean Squared Error ◽  
Commodity Prices ◽  
Test Results ◽  
Agricultural Commodity ◽  
Data Format ◽  
Squared Error ◽  
Backpropagation Method

Chili is one of the spices needed by the majority of Indonesian people. These high needs have an impact on the price of this agricultural commodity which has become very fluctuated. This study, uses the backpropagation method to predict chilli prices in Payakumbuh City, with data sourced from the Badan Pusat Statistik Kota Payakumbuh. The data format are weekly chilli price data for the period 2014 to 2019. Data variables are arranged into time series forms with 4 input values from each week per year, and 1 target value. From the test results obtained the MSE value (Mean Squared Error) of 0.00118 with prediction accuracy of 98.56%. The results of this study can prove that Artificial Neural Networks using the backpropagation method can predict commodity prices for chilli in Payakumbuh City with a good level of accuracy, so that it can be used for the following year.

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