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我们提供的发展经济学Development Economics及其相关学科的代写,服务范围广, 其中包括但不限于:

  • Statistical Inference 统计推断
  • Statistical Computing 统计计算
  • Advanced Probability Theory 高等概率论
  • Advanced Mathematical Statistics 高等数理统计学
  • (Generalized) Linear Models 广义线性模型
  • Statistical Machine Learning 统计机器学习
  • Longitudinal Data Analysis 纵向数据分析
  • Foundations of Data Science 数据科学基础
经济代写|发展经济学代写Development Economics代考|ECON4915

经济代写|发展经济学代写Development Economics代考|The Machine Learning Revolution

Evaluators have used a wide range of tools to analyse qualitative and quantitative data, from Excel-based quantitative analysis to qualitative comparative analysis (QCA). Software tools such as ATLAS.ti and NVivo, among others, have digitized qualitative analysis. With the emergence of machine learning, the paradigm of qualitative analysis is rapidly undergoing another change. In the realm of qualitative data analysis, the initial set of tools focused on word count (text analysis), then relationships between concepts, and finally understanding grammar. In recent years, computer-assisted data analysis has seen a surge in efficacy and potential. Leaps in computing power, distributed computing and complex algorithms have enabled applications that incorporate machine learning and natural language processing to interpret vast amounts of qualitative data from unlimited sources in different formats (Evers, 2018). They also have the potential to automate a part of the analysis and increase the efficiency of the analysis (see Case Study 4).
Machine learning is also having a profound impact on the way quantitative data is analysed (Boelaert and Ollion, 2018). New computer language environments such as $\mathrm{R}$ and Python have increased the scope and speed of quantitative analysis that can be carried out on an ordinary desktop computer (Mullainathan and Spiess, 2017), and the availability of large amounts of data to train machine learning algorithms has fuelled the growth of predictive modelling with an unprecedented level of rigour (see Case Study 5). The ability to train the algorithms on data derived from a wide variety of sources, including the Internet, remole sensing and primary surveys, has made machine learning a tool that can be implemented in a wide variety of contexts. The emergence of powerful yet accessible programmes such as Python and the availability of application program interfaces (APIs) mean that quantitative data from any source can be used in combination with other sources to build predictive models.

经济代写|发展经济学代写Development Economics代考|Dissemination and Learning: Reaching a Global Audience

The achievement of the SDGs will mainly take place at the country level. Evaluations can contribute to improving the quality of decision-making (evidence-based policymaking) by providing timely feedback and rigorous evidence on effectiveness of development programmes. In doing so, the approach to the dissemination of evaluation findings, conclusions and recommendations needs to be adjusted so that particular experiences under evaluation can serve as lessons learned more broadly. For evaluators, dissemination and learning tend to raise two major questions: how to disseminate the results of evaluation more broadly? who should the results be disseminated to? Historically, dissemination has meant sharing evaluation reports through websites and mailing groups, but evaluators can no longer be restricted to this paradigm alone.

The advent of social media, the blogosphere, the Internet and the network effects of such media have meant that a few billion people are now on a common platform. This provides vast potential for evaluators to communicate with a wide range of stakeholders, including policymakers, national institutions and donors. Social media also requires a fresh communication outlook because of the need for short and impactful messages, and because it is overloaded with information from myriad sources (Holton and Chyi, 2012). The emergence of virtual reality is also changing the way in which audiences are engaged and influenced. Virtual reality lets users transcend physical and temporal barriers to experiense rather than read stories. The United Nations system has only just started experimenting on this front, with the recent production of the virtual reality film Clouds Over Sidra.
In terms of the audiences to whom they should be disseminating their findings, evaluators interact with numerous stakeholders and audiences. Each audience may require a different means of communication. One of the most difficult tasks in the process of dissemination is communicating with the public at large, or what in the development jargon is conceived as end beneficiaries. This inherent difficulty has led to criticism of evaluation as an extractive process that fails to inform beneficiaries about the results of the data taken from them. However, proliferation of technology holds the promise of solving this persistent problem (see Case Study 7).

经济代写|发展经济学代写Development Economics代考|ECON4915


经济代写|发展经济学代写Development Economics代考|The Machine Learning Revolution

评估人员使用了广泛的工具来分析定性和定量数据,从基于 Excel 的定量分析到定性比较分析 (QCA)。ATLAS.ti 和 NVivo 等软件工具已将定性分析数字化。随着机器学习的出现,定性分析的范式正在迅速发生另一次变化。在定性数据分析领域,最初的工具集专注于字数(文本分析),然后是概念之间的关系,最后是理解语法。近年来,计算机辅助数据分析的功效和潜力激增。计算能力的飞跃,分布式计算和复杂算法使结合机器学习和自然语言处理的应用程序能够解释来自不同格式的无限来源的大量定性数据(Evers,2018 年)。它们还具有使部分分析自动化并提高分析效率的潜力(参见案例研究 4)。
机器学习也对分析定量数据的方式产生了深远的影响(Boelaert 和 Ollion,2018 年)。新的计算机语言环境,例如R和 Python 增加了可以在普通台式计算机上执行的定量分析的范围和速度(Mullainathan 和 Spiess,2017 年),并且大量数据可用于训练机器学习算法推动了预测建模的发展前所未有的严格程度(见案例研究 5)。利用来自各种来源(包括互联网、远程感应和初级调查)的数据训练算法的能力,使机器学习成为一种可以在各种环境中实施的工具。Python 等功能强大但易于访问的程序的出现以及应用程序接口 (API) 的可用性意味着来自任何来源的定量数据都可以与其他来源结合使用来构建预测模型。

经济代写|发展经济学代写Development Economics代考|Dissemination and Learning: Reaching a Global Audience


社交媒体、博客圈、互联网的出现以及这些媒体的网络效应意味着现在有数十亿人在一个共同的平台上。这为评估人员与广泛的利益相关者(包括决策者、国家机构和捐助者)进行沟通提供了巨大的潜力。社交媒体也需要全新的传播视角,因为它需要简短而有影响力的信息,并且因为它充斥着来自无数来源的信息(Holton 和 Chyi,2012 年)。虚拟现实的出现也在改变观众参与和影响的方式。虚拟现实让用户超越物理和时间障碍去体验而不是阅读故事。联合国系统在这方面才刚刚开始试验,
就他们应该向其传播调查结果的受众而言,评估人员与众多利益相关者和受众互动。每个观众可能需要不同的交流方式。传播过程中最困难的任务之一是与广大公众进行交流,或者在发展术语中被视为最终受益者。这种固有的困难导致批评评估是一种提取过程,无法告知受益人从他们那里获取的数据的结果。然而,技术的普及有望解决这个长期存在的问题(参见案例研究 7)。

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术语 广义线性模型(GLM)通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归,以及方差分析和方差分析(仅含固定效应)。



有限元是一种通用的数值方法,用于解决两个或三个空间变量的偏微分方程(即一些边界值问题)。为了解决一个问题,有限元将一个大系统细分为更小、更简单的部分,称为有限元。这是通过在空间维度上的特定空间离散化来实现的,它是通过构建对象的网格来实现的:用于求解的数值域,它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统,以模拟整个问题。然后,有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。





随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。


多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。


MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中,其中问题和解决方案以熟悉的数学符号表示。典型用途包括:数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发,包括图形用户界面构建MATLAB 是一个交互式系统,其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题,尤其是那些具有矩阵和向量公式的问题,而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问,这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展,得到了许多用户的投入。在大学环境中,它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域,MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要,工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数(M 文件)的综合集合,可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。