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关键词： 计量经济学   会计专业   教学改革   Eviews编程   实验课  

摘要： 结合本科会计专业的教学实践,得出会计专业计量经济学教学的五点体会:在理论内容讲解方面,重点放在经典线性回归模型和时间序列的讲解上;在软件使用方面,综合使用Excel和Eviews,但以Excel为主;在实验安排方面,重点放在成本核算与盈利预测的实证分析方面;适当引导部分学生学习Eviews编程;注重培养学生们的动手能力。

关键词： 毕业论文   计量经济学   课程改革  

摘要： 大数据背景下,社会各领域对经济活动中的数量关系越发关注。在财经类专业高等教育教学过程中,往往开设计量经济学课程,以研究这种数量关系。财经类毕业论文常常涉及实证分析,其中利用了计量经济学的建模理论与方法。在对文献进行梳理的基础上,通过对问卷调查进行统计,分析该课程的教学现状及存在的问题,提出针对培养学生毕业论文设计能力的计量经济学课程改革建议。

关键词： 甲板驳运输   海上风电   市场规模   计量经济学   回归模型  

摘要： 甲板驳船运输市场规模是否稳定和有序不仅影响供应的稳定也会影响企业的收入水平与甲板驳船运输价格,以及海上风力发电场的建设成本,这些对中国的碳中和政策目标的实现有着深远的影响。自我国提出碳中和的国家政策以来,我国海上风电建设成为该政策实施的重要环节。对甲板驳船运输能力的需求逐渐增加,直接促进了甲板驳船运价的提高。然而,甲板驳运输市场价格的波动以及甲板驳运输服务的特殊性所带来的不确定性给国家宏观调控带来了困难,增加了海上风电建设的成本,对甲板驳运营公司的稳定运营产生了不利影响。因此,要了解我国甲板驳运输市场规模的变化规律,需要从根本上把握甲板驳运价形成的机理,准确把握具体船型甲板驳运价形成的原因。 本文主要收集、识别和处理现有的研究国内外甲板驳船运费率的数据,获得各种因素之间的关系和作用机制的形成过程和甲板驳船运价,并掌握主要的研究现状和进展。本文从西方经济学的角度,分析了各种甲板驳船运力的定价权和价格形成过程,研究和分析了运输能力的定价权,区分了不同市场条件下甲板驳船市场规模的形成过程,并把握甲板驳市场规模形成的主要因素。 本文将计量经济学模型引入到国内甲板驳运输市场的分析和研究中,揭示了中国海上风电装机容量与国内甲板驳船运输市场规模的相关性。根据中国海上风力发电行业的实际特点,本文研究和寻找国内甲板驳船运输市场规模的影响因素,并比较几种核心影响因素,如海上风力发电装机容量的变化,新建船舶的数量,购买外国船舶导致的运力增加,以及市场运力的调整,探讨各影响因素素对国内甲板驳运输市场的影响。通过以上研究,我们可以对国内甲板驳运输市场进行动态预测,分析国内甲板驳运输市场在某些规定范围内是否会出现过热,从而避免过度投资或错失市场机会。研究结果不仅可以为海上风电运输企业的管理提供建议,还可以为决策提供理论参考。除此以外,本文还研究了2019年实施的产业扶持政策对我国甲板驳运输市场规模的因果效应,并提出了相应的解决我国甲板驳运输市场规模问题的对策。

摘要： This dissertation is composed of three essays, two in international macroeconomics and one in climate econometrics. The former center on two main features of emerging market economies: financial dollarization of private portfolios, and sudden stops in capital flows. The first essay explores the reasons behind the high levels of financial dollarization in Latin American economies and suggests that dollarization can arise within a country from the interaction of households and domestic firms, de-emphasizing the role of international financial markets. The second examines how the debt choice from a social planner that makes a decision that is robust to a worst-case distribution of shocks, consistent with the data, results in higher optimal macroprudential taxes that reduce the probability and severity of a sudden stop, but do not necessarily improve welfare gains. The third essay, in climate econometrics, finds estimates of the transient climate response, a measure of the short-run impact of increasing carbon dioxide concentrations on mean global temperatures, by employing instrumental variables to address simultaneous causality bias from feedback effects of temperature on concentrations. Estimates of the TCR fall within the range in the IPCC-AR5 and provide new observational confirmation of model-based estimates.

摘要： This thesis considers identification, estimation, and inference in nonparametric settings. Special attention is given to identification with instrumental variables of small support, and in time series via the ergodic assumption. Chapter 1 considers a nonparametric instrumental regression model in which the regressor and instrument are discretely distributed. Here, we strengthen the conventional moment independence assumption between instrument and residual to include higher order moments. We give conditions under which a function of interest is partially identified when the regressor has more mass points than the instrument. We also show that the function is point identified as long as certain latent parameters corresponding to conditional moments of the residual lie outside of a set of measure zero. We use our results on identification to construct an estimator for the structural function. The estimator performs well in Monte Carlo simulations, and in applications to studies of Angrist and Evans (1998), Card (1995), and Acemoglu and Angrist (2000). Chapter 2 extends our analysis to a nonparametric instrumental variables setting with binary instrument and possibly continuous regressor. In this chapter, we give assumptions under which statistical independence between instrument and residual point identify a structural function up to some additive constant. When the structural function is confined to lie in a sigma-compact set of functions, the condition for point identification holds for a topologically generic set of joint density functions for the residual and regressor. Our method allows us to provide a completeness condition under which the nonparametric instrumental variables quantile regression framework studied by Chernozhuov and Hansen (2005) is identified. Chapter 3 examines the completeness condition of joint distributions at three levels of generality, and provides genericity results at each level. In its broadest sense, the completeness condition requires

关键词： 计量经济学   大数据   毕业论文设计  

摘要： 目前,大多数高校的计量经济学课程重理论轻应用,课程案例更新速度远不及经济社会发展,学生软件操作环节表现较为薄弱。大数据背景下,以毕业论文设计为导向来学习计量经济理论与方法,符合结果导向教育理念。利用层次分析法,构建计量经济学课程优化设计指标体系的判断矩阵,得出大数据应用于课程目标、课程评价、课程内容的选择与组织的重要性排在前三位,并提出大数据背景下计量经济学课程优化设计的对策。

ISBN: (纸本)9787543232990

ISBN: (纸本)9787502648657

摘要： This dissertation studies the estimation and statistical inference of a few methods that are commonly used in empirical studies of economics, when the data is dependent. Chapter 1 studies the linear IV models with clustering dependence, which are widely used in empirical studies. The common solution, the cluster covariance estimator, often produces undesirable inferential results, especially with weak instruments. I propose a method that is robust to both weak IV and (potentially heterogeneous) clustering dependence. The proposed method is based on the idea of Fama-MacBeth estimation, with group-level estimators being a truncated version of the unbiased IV estimator. Asymptotic validity is shown under both strong and weak IV sequences, as well as under general requirements. Simulation results indicate that the method has good finite-sample performance in both size and power. The proposed method is applied to study the effect of city compactness on population density. Chapter 2, coauthored with Damian Kozbur, Christian Hansen, and Lucciano Villacorta, presents and analyzes an approach to inference for dependent data. The primary setting considered here is with spatially indexed data in which the dependence structure of observed random variables is characterized by an observed dissimilarity measure over spatial indexes. Observations are partitioned into clusters with the use of an unsupervised clustering algorithm applied to the dissimilarity measure. Once the partition into clusters is learned, a cluster-based inference procedure is applied to a statistical hypothesis test. The procedure proposed in the paper allows the number of clusters to depend on the data, which gives re- searchers a principled method for choosing an appropriate clustering level. The paper gives conditions under which the proposed procedure asymptotically attains the correct size. Chapter 3, coauthored with Connor Dowd, studies the synthetic control methods in the presence of spillover effects.