Narrow Results

This paper demonstrates how the estimation of economic activities and inequality for the Indian economy based on nighttime lights data from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite outperforms those obtained using traditional Defense Meteorological Satellite Program Operational Line-Scan System (DMSP-OLS) sources. In the extant literature, nighttime lights data have been frequently used as a proxy for economic activities, especially at granular geographical levels where gross domestic product data are otherwise unavailable. Since the traditional DMSP-OLS-based nighttime lights data suffer from measurement errors due to blurring and top coding, VIIRS-based nighttime lights data, which are devoid of such limitations, are gaining popularity. We show that at the subnational or district level in India, VIIRS satellite-based nighttime lights data are indeed a better proxy for economic activities and inequality. Our robust findings imply that all future scholarly works that employ nighttime lights data for the Indian economy should use VIIRS-based data for greater accuracy compared to other similar data sources.

Damage identification using the sensitivity of the dynamic characteristics of the structure of concern has been studied considerably. Among the dynamic characteristics used to locate and quantify structural damages, the frequency response function (FRF) data has the advantage of avoiding modal analysis errors. Additionally, previous studies demonstrated that strains are more sensitive to localized damages compared to displacements. So, in this study, the strain frequency response function (SFRF) data is utilized to identify structural damages using a sensitivity-based model updating approach. A pseudo-linear sensitivity equation which removes the adverse effects of incomplete measurement data is proposed. The approximation used for the sensitivity equation utilizes measured natural frequencies to reconstruct the unmeasured SFRFs. Moreover, new approaches are proposed for selecting the excitation and measurement locations for effective model updating. The efficiency of the proposed method is validated numerically through 2D truss and frame examples using incomplete and noise polluted SFRF data. Results indicate that the method can be used to accurately locate and quantify the severity of damage.

Multivariate control charts are useful tools for monitoring the product quality in which the process outcome is expressed by several correlated variables. The multivariate homogeneously weighted moving average (MHWMA) control chart is one of the most efficient extensions of the multivariate exponentially weighted moving average (MEWMA) procedure. However, this control chart has been proposed under the assumption of precise measurements. This paper explores the impact of the measurement system inability on sensitivity of MHWMA control chart in Phase II monitoring of the mean vector of multivariate normally distributed quality characteristics. Through simulation studies, the run length (RL) properties of MHWMA chart in the presence of measurement errors are investigated and compared to without-error scenario. The results indicate that the imprecise measurements degrade the detection ability of MHWMA control chart in detecting different process disturbances. The undesired impact of gauge measurement errors increases as the error variance increases. Moreover, multiple measurements on each sampled item are utilized to compensate for the undesired effect of the measurement errors on run length characteristics of the MHWMA control chart. It is also concluded that, as the number of measurements on each sampled point increases, the detection capability of MHWMA chart reduces and approaches the without-error case. Finally, a realistic illustrative example from a healthcare system is utilized to elaborate on the impact of imprecise measurements on chart performance.

In this chapter we first discuss possibilities that the orthogonality condition E(ε_t|X_t) = 0 may fail, which will generally render inconsistent the OLS estimator for the true model parameter. We then introduce a consistent Two-Stage Least Squares (2SLS) estimator, investigating its statistical properties and providing intuitions for the nature of the 2SLS estimator. Hypothesis tests are constructed. We consider various test procedures corresponding to the cases for which the regression disturbance is an MDS with conditional homoskedasticity, an MDS with conditional heteroskedasticity, and a non-MDS process, respectively. The latter case will require consistent estimation of a long-run variance-covariance matrix. It is important to emphasize that the t-test and F-test statistics obtained from the second stage regression estimation cannot be used even for large samples. Finally, we conclude this chapter by presenting a summary of econometric theory for linear regression models developed in Chapters 2 to 7.

The following sections are included:

• INTRODUCTION

• TESTS OF THE SIGNIFICANCE OF α AND β
  • Hypothesis Testing and Confidence Interval for β and α
  • Two-Tailed z Test Versus Two-Tailed t Test for β
  • Two-Tailed t Test for α
  • One-Tailed t Test for β
  • Confidence interval for β
  • The F Test Versus the t Test
  • Procedure for Using the F Test
  • The Relationship Between the F Test and the t Test

• TEST OF THE SIGNIFICANCE OF ρ
  • t Test for Testing ρ = 0
  • z Test for Testing ρ = 0 or ρ = Constant

• CONFIDENCE INTERVAL FOR THE MEAN RESPONSE AND PREDICTION INTERVAL FOR THE INDIVIDUAL RESPONSE
  • Point Estimates of the Mean Response and the Individual Response
  • Interval Estimates of forecasts
  • Calculating Standard Errors
  • Confidence Interval for the Mean Response and Prediction Interval for the Individual Response
  • Using MINITAB to Calculate Confidence Interval and Prediction Interval

• BUSINESS APPLICATIONS
  • The Relationship Between Layoff Rate and the Unemployment Compensation Subsidy Rate
  • Market Model Estimation and Analysis
  • Relationship Between Audited and Book Inventory Value
  • Hamburger Sales: Predicting Profits⁵
  • The Impact of Cable TV Penetration on Network Share TV Revenues

• USING COMPUTER PROGRAMS TO DO SIMPLE REGRESSION ANALYSIS

• Summary

• Apiendix 14A Impact of Measurement Error and Proxy Error on Slope Estimates

• Appendix 14B The Relationship Between the F Test and the t Test

• Appendix 14C Derivation of Variance for Alternative Forecasts

• Questions and Problems