ERN: Econometric Software (Topic)最新文献

Iterated and Exponentially Weighted Moving Principal Component Analysis 迭代与指数加权移动主成分分析

ERN: Econometric Software (Topic) Pub Date : 2021-08-30 DOI: 10.2139/ssrn.3913940

Paul Bilokon, David Finkelstein

引用次数: 0

Instrumental Variable Estimation of Large Panel Data Models with Common Factors 具有共同因子的大面板数据模型的工具变量估计

ERN: Econometric Software (Topic) Pub Date : 2020-08-06 DOI: 10.2139/ssrn.3668588

Sebastian Kripfganz, Vasilis Sarafidis

{"title":"Instrumental Variable Estimation of Large Panel Data Models with Common Factors","authors":"Sebastian Kripfganz, Vasilis Sarafidis","doi":"10.2139/ssrn.3668588","DOIUrl":"https://doi.org/10.2139/ssrn.3668588","url":null,"abstract":"This article introduces the xtivdfreg command in Stata, which implements a general Instrumental Variables (IV) approach for estimating large panel data models with unobserved common factors or interactive effects, as developed by Norkute et al. (2020) and Cui et al. (2020a). The underlying idea of this approach is to project out the common factors from exogenous co-variates using principal components analysis, and run IV regression using de-factored co-variates as instruments. The resulting \"IVDF\" method is valid for models with homogeneous or heterogeneous slope coefficients, and has several advantages relative to existing popular approaches. \u0000 \u0000In addition, the xtivdfreg command extends the IVDF approach in two major ways. Firstly, the algorithm accommodates estimation of unbalanced panels. Secondly, the algorithm permits highly \u0000flexible instrumentation strategies. \u0000 \u0000It is shown that when one imposes zero factors, the xtivdfreg command can replicate the results of the popular ivregress Stata command. Notably, xtivdfreg also permits estimation of the two-way error components panel data model with heterogeneous slope coefficients.","PeriodicalId":438593,"journal":{"name":"ERN: Econometric Software (Topic)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122404647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

lpirfs: An R Package to Estimate Impulse Response Functions by Local Projections 一个用局部投影估计脉冲响应函数的R包

ERN: Econometric Software (Topic) Pub Date : 2019-11-19 DOI: 10.32614/RJ-2019-052

P. Adämmer

引用次数: 21

A Practical Method to Reduce Privacy Loss When Disclosing Statistics Based on Small Samples 一种减少小样本统计信息披露时隐私损失的实用方法

ERN: Econometric Software (Topic) Pub Date : 2019-03-01 DOI: 10.1257/PANDP.20191109

Raj Chetty, John N Friedman

{"title":"A Practical Method to Reduce Privacy Loss When Disclosing Statistics Based on Small Samples","authors":"Raj Chetty, John N Friedman","doi":"10.1257/PANDP.20191109","DOIUrl":"https://doi.org/10.1257/PANDP.20191109","url":null,"abstract":"We develop a simple method to reduce privacy loss when disclosing statistics such as OLS regression estimates based on samples with small numbers of observations. We focus on the case where the dataset can be broken into many groups (“cells”) and one is interested in releasing statistics for one or more of these cells. Building on ideas from the differential privacy literature, we add noise to the statistic of interest in proportion to the statistic's maximum observed sensitivity, defined as the maximum change in the statistic from adding or removing a single observation across all the cells in the data. Intuitively, our approach permits the release of statistics in arbitrarily small samples by adding sufficient noise to the estimates to protect privacy. Although our method does not offer a formal privacy guarantee, it generally outperforms widely used methods of disclosure limitation such as count-based cell suppression both in terms of privacy loss and statistical bias. We illustrate how the method can be implemented by discussing how it was used to release estimates of social mobility by Census tract in the Opportunity Atlas. We also provide a step-by-step guide and illustrative Stata code to implement our approach.","PeriodicalId":438593,"journal":{"name":"ERN: Econometric Software (Topic)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117036699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 29

Network-Constrained Covariate Coefficient and Connection Sign Estimation 网络约束协变量系数与连接符号估计

ERN: Econometric Software (Topic) Pub Date : 2018-06-11 DOI: 10.2139/ssrn.3211163

Matthias Weber, Jonas Striaukas, M. Schumacher, H. Binder

引用次数: 3

Accessing Financial Reports and Corporate Events with GetDFPdata 使用GetDFPdata访问财务报告和公司活动

ERN: Econometric Software (Topic) Pub Date : 2018-02-22 DOI: 10.2139/ssrn.3128252

M. Perlin, Guilherme Kirch, D. Vancin

引用次数: 15

A Statistical Analysis of Industrial Penetration and Internet Intensity in Taiwan 台湾工业渗透与网路密集度之统计分析

ERN: Econometric Software (Topic) Pub Date : 2018-01-07 DOI: 10.2139/ssrn.3100203

Chia‐Lin Chang, M. McAleer, Yu-Chieh Wu

引用次数: 1

Consumption Structure Repercussions 消费结构影响

ERN: Econometric Software (Topic) Pub Date : 2017-12-03 DOI: 10.2139/ssrn.3081675

M. Georgiou

引用次数: 0

Modelos Macroeconómicos Básicos En Matlab (Basic Macroeconomic Models in Matlab) Modelos Macroeconómicos Básicos En Matlab (Matlab中的基本宏观经济模型)

ERN: Econometric Software (Topic) Pub Date : 2017-05-15 DOI: 10.2139/ssrn.3621904

Alfonso Ayala

引用次数: 0

An Efficient Algorithm Based on Eigenfunction Expansions for Some Optimal Timing Problems in Finance 基于特征函数展开的金融最优时序问题的高效算法

ERN: Econometric Software (Topic) Pub Date : 2015-05-13 DOI: 10.2139/ssrn.2605742

Lingfei Li, X. Qu, Gongqiu Zhang

{"title":"An Efficient Algorithm Based on Eigenfunction Expansions for Some Optimal Timing Problems in Finance","authors":"Lingfei Li, X. Qu, Gongqiu Zhang","doi":"10.2139/ssrn.2605742","DOIUrl":"https://doi.org/10.2139/ssrn.2605742","url":null,"abstract":"This paper considers the optimal switching problem and the optimal multiple stopping problem for one-dimensional Markov processes in a finite horizon discrete time framework. We develop a dynamic programming procedure to solve these problems and provide easy-to-verify conditions to characterize connectedness of switching and exercise regions. When the transition or Feynman-Kac semigroup of the Markov process has discrete spectrum, we develop an efficient algorithm based on eigenfunction expansions that explicitly solves the dynamic programming problem. We also prove that the algorithm converges exponentially in the series truncation level. Our method is applicable to a rich family of Markov processes which are widely used in financial applications, including many diffusions as well as jump-diffusions and pure jump processes that are constructed from diffusion through time change. In particular, many of these processes are often used to model mean-reversion. We illustrate the versatility of our method by considering three applications: valuation of combination shipping carriers, interest-rate chooser flexible caps and commodity swing options. Numerical examples show that our method is highly efficient and has significant computational advantages over standard numerical PDE methods that are typically used to solve such problems.","PeriodicalId":438593,"journal":{"name":"ERN: Econometric Software (Topic)","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133217034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14