Econometrics: Data Collection & Data Estimation Methodology eJournal最新文献

{"title":"The Effect of Media Coverage on Mass Shootings","authors":"Michael Jetter, Jay K. Walker","doi":"10.2139/ssrn.3286159","DOIUrl":"https://doi.org/10.2139/ssrn.3286159","url":null,"abstract":"Can media coverage of shooters encourage future mass shootings? We explore the link between the day-to-day prime time television news coverage of shootings on ABC World News Tonight and subsequent mass shootings in the US from January 1, 2013 to June 23, 2016. To circumvent latent endogeneity concerns, we employ an instrumental variable strategy: worldwide disaster deaths provide an exogenous variation that systematically crowds out shooting-related coverage. Our findings consistently suggest a positive and statistically significant effect of coverage on the number of subsequent shootings, lasting for 4-10 days. At its mean, news coverage is suggested to cause approximately three mass shootings in the following week, which would explain 55 percent of all mass shootings in our sample. Results are qualitatively consistent when using (i) additional keywords to capture shooting-related news coverage, (ii) alternative definitions of mass shootings, (iii) the number of injured or killed people as the dependent variable, and (iv) an alternative, longer data source for mass shootings from 2006-2016.","PeriodicalId":433005,"journal":{"name":"Econometrics: Data Collection & Data Estimation Methodology eJournal","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123718637","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}

{"title":"A Polarization Index for Overlapping Groups","authors":"C. Gigliarano, Daniel Nowak, K. Mosler","doi":"10.1111/roiw.12374","DOIUrl":"https://doi.org/10.1111/roiw.12374","url":null,"abstract":"The well‐known index of income bipolarization proposed by Wolfson (1994) requires two groups to be split according to the median income and, therefore, to be non‐overlapping. The aim of this paper is to propose a new polarization index in the spirit of the Wolfson index. It allows for any possible partition of the population in two or more (also overlapping) groups. The new index maintains the simplicity and immediate comprehension of the Wolfson index, though being much more flexible. An application is then provided for German and Italian income data.","PeriodicalId":433005,"journal":{"name":"Econometrics: Data Collection & Data Estimation Methodology eJournal","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133629059","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}

{"title":"Application of a Principal Component Analysis, (PCA), Based on the Macroeconomic Factors, Namely, Personal Consumption Expenditures, Gross Private Domestic Investment, Net Export of Goods and Services and Government Consumption Expenditures and Gross Investment that Constitute The US GDP.","authors":"Michel Guirguis","doi":"10.2139/ssrn.3253219","DOIUrl":"https://doi.org/10.2139/ssrn.3253219","url":null,"abstract":"In this article, we have tested the correlation and covariance relationships that the natural logarithmic yearly returns of the macroeconomic variables in terms of personal consumption expenditures, gross private domestic investment, net export of goods and services and government consumption expenditures and gross investment, have on the US Gross domestic product, (GDP). We have applied a principal component analysis, (PCA), in EViews 6 to check the eigenvalues, the eigenvectors loadings of the correlation matrix and the covariance matrix. The aim by using this methodology is twofold. Firstly, to identify the degree of correlation between the variables. Secondly to reduce the dimension of variation between the variables by eliminating the factors. We have found though the correlation matrix that most of the correlation coefficients of the macroeconomic variables are greater than 0.5 and they show very strong positive linear correlation. There is also linear weak negative and positive correlation between the macro variables. In terms of dimensionality reduction, we have found that factors 1 and 2 have an eigenvalues greater than 1. Specifically, factor 1 has a value of 3.14 and factor 2 has a value of 1.09. Thus, the factors that we will retain are two. Concerning, eigenvalues figures, we have found that the proportion for factor 1 is 62.73% and for factor 2 is 21.74% of the total variance. The first two components namely account for 84.47% of the total variation. Most of the residuals of the common covariance matrix are positive, which mean that the variables increase together. The orthonormal loadings biplot shows that the first component has the highest proportion of total variation, which is 62.7% and positive loadings for all five variables. The second component has a value of 21.7% of total variation. It has a positive variable loadings for government consumption expenditures and gross investment, (GCEGI) and negative variable loadings for gross private domestic investment, (GPDI), and net export of goods and services, (NEGS). The total dataset includes annual data starting from 1980 to 2012 and total to 33 observations. The total data of the logarithmic yearly returns account to 32 observations. The data was obtained from the US Bureau of Economic Analysis, (BEA).","PeriodicalId":433005,"journal":{"name":"Econometrics: Data Collection & Data Estimation Methodology eJournal","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128615698","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}

{"title":"Guidelines for asset pricing research using international equity data from Thomson Reuters Datastream","authors":"C. Landis, S. Skouras","doi":"10.2139/ssrn.3225371","DOIUrl":"https://doi.org/10.2139/ssrn.3225371","url":null,"abstract":"Abstract We provide detailed guidelines and code to derive high quality international equity data from Thomson Reuters Datastream (TDS) data. Our approach increases stock and country coverage (to 91 countries), improves data accuracy, filters problematic data and reduces survivorship bias and data staleness. We validate our approach by demonstrating that our U.S. TDS factors are statistically and economically indistinguishable to standard Fama-French CRSP factors. On the other hand, when we compare our international factors to other publicly available international factors, differences are significant, so we justify and detail every aspect of our proposed guidelines. Our guidelines and accompanying code and data should be especially useful for international research focused on wide coverage, equal weighted portfolios, small stocks and countries with a limited number of stocks and for researchers wishing to analyze the US market with access to only TDS but not CRSP-Compustat data.","PeriodicalId":433005,"journal":{"name":"Econometrics: Data Collection & Data Estimation Methodology eJournal","volume":"164 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121030906","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}

{"title":"Forecasting with Many Predictors: How Useful are National and International Confidence Data?","authors":"Kevin Moran, Nono Simplice Aime, Imad Rherrad","doi":"10.2139/ssrn.3240821","DOIUrl":"https://doi.org/10.2139/ssrn.3240821","url":null,"abstract":"This paper assesses the contribution of Canadian and International (US) confidence data, drawn from consumer and business sentiment surveys, for forecasting Canadian GDP growth. The targeting approaches of Bai and Ng (2008) and Bai and Ng (2009) are employed to extract promising predictors from large databases each containing between several dozen and several hundred time series. The databases are categorised between those containing macroeconomic (Canadian and US) and confidence (Canadian and US) data, allowing us to assess the specific value added of international and confidence data. We find that forecasting ability is consistently improved by considering information from national confidence data; by contrast, their US counterparts appear to be helpful only when combined with national time-series. Overall, most relevant gains in forecasting performance are observed for short-term (up to threequarters-ahead) horizons, perhaps reflecting the timing advantage in the releases of sentiment data.","PeriodicalId":433005,"journal":{"name":"Econometrics: Data Collection & Data Estimation Methodology eJournal","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125279431","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}

{"title":"An Exploration of Dynamical Relationships Between Macroeconomic Variables and Stock Prices in Korea","authors":"Jung Wan Lee, Tantatape Brahmasrene","doi":"10.13106/JAFEB.2018.VOL5.NO3.7","DOIUrl":"https://doi.org/10.13106/JAFEB.2018.VOL5.NO3.7","url":null,"abstract":"This paper examines short-run and long-run dynamic relationships between selected macroeconomic variables and stock prices in the Korea Stock Exchange. The data is restricted to the period for which monthly data are available from January 1986 to October 2016 (370 observations) retrieved from the Economic Statistics System database sponsored by the Bank of Korea. The study employs unit root test, cointegration test, vector error correction estimates, impulse response test, and structural break test. The results of the Johansen cointegration test indicate at least three cointegrating equations exist at the 0.05 level in the model, confirming that there is a long-run equilibrium relationship between stock prices and macroeconomic variables in Korea. The results of vector error correction model (VECM) estimates indicate that money supply and short-term interest rate are not related to stock prices in the short-run. However, exchange rate is positively related to stock prices while the industrial production index and inflation are negatively related to stock prices in the short-run. Furthermore, the VECM estimates indicate that the external shock, such as regional and global financial crisis shocks, neither affects changes in the endogenous variables nor causes instability in the cointegrating vector. This study finds that the endogenous variables are determined by their own dynamics in the model.","PeriodicalId":433005,"journal":{"name":"Econometrics: Data Collection & Data Estimation Methodology eJournal","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121142332","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}

{"title":"How Big Is China’s Digital Economy?","authors":"A. Garcia-Herrero, Jianwei Xu","doi":"10.2139/ssrn.3429741","DOIUrl":"https://doi.org/10.2139/ssrn.3429741","url":null,"abstract":"This paper reviews international measures of the digital economy with those developed by Chinese officials and private sources. Given their lack of comparability, we use China’s input and output and census data to come up with an internationally comparable estimate of China’s size of the Information and Communication Technology (ICT) sector (the core of digital economy), both in terms of value-added and employment. Based on the latest available statistics, our measurements indicate that China’s digital economy is not bigger relative to the size of the Chinese economy than the OECD average, especially in terms of ICT employment. This finding, which might look striking based on the current perception of China’s digital economy, masks large differences across regions (with Beijing, Guangdong and Shanghai ahead of the OECD average).","PeriodicalId":433005,"journal":{"name":"Econometrics: Data Collection & Data Estimation Methodology eJournal","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117343078","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}

{"title":"Different Contexts, Different Risk Preferences?","authors":"Levon Barseghyan, Joshua C. Teitelbaum, Lin Xu","doi":"10.2139/ssrn.3200657","DOIUrl":"https://doi.org/10.2139/ssrn.3200657","url":null,"abstract":"We examine the stability of risk preferences across contexts involving different stakes. Using data on households' deductible choices in three property insurance coverages and their limit choices in two liability insurance coverages, we assess the stability across the five contexts in the ordinal ranking of the households' willingness to bear risk. We find evidence of stability across contexts involving stakes of the same magnitude, but not across contexts involving stakes of very different magnitudes. Our results appear to be robust to heterogeneity in wealth and access to credit, complicating seemingly ready explanations.","PeriodicalId":433005,"journal":{"name":"Econometrics: Data Collection & Data Estimation Methodology eJournal","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133512958","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}

{"title":"Is the Technical Conversion Factor Informative About the Price Ratio of Processing Livestock?","authors":"Kris Boudt, Hong Anh Luu","doi":"10.2139/ssrn.3100894","DOIUrl":"https://doi.org/10.2139/ssrn.3100894","url":null,"abstract":"The technical conversion factor (TCF) is a survey-based estimate of the percentage of carcass weight obtained per unit of live weight. Practitioners and researchers have used it to predict the corresponding price ratio. We use both in-sample regressions and out-of-sample forecasting analysis to test the validity of this approach in case of predicting the price effects of processing livestock in Europe. By regressing the price ratio on the inverse value of the corresponding TCF for a large panel of European countries and animal types, we find a significant positive relation between these variables which also has economic value in terms of improving out-of-sample forecasting precision. This result is shown to be robust to animal type, year and country fixed effects. The technical conversion factor thus has predictive value about the corresponding price ratio.","PeriodicalId":433005,"journal":{"name":"Econometrics: Data Collection & Data Estimation Methodology eJournal","volume":"2 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120903042","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}

{"title":"Parameter Estimation With Out-of-Sample Objective","authors":"P. Hansen, E. Dumitrescu","doi":"10.2139/ssrn.3178896","DOIUrl":"https://doi.org/10.2139/ssrn.3178896","url":null,"abstract":"We study parameter estimation from the sample X, when the objective is to maximize the expected value of a criterion function, Q, for a distinct sample, Y. This is the situation that arises when a model is estimated for the purpose of describing other data than those used for estimation. The motivated for much estimation has this form, with forecasting problems being a prime example. A natural estimator is the innate estimator that maximizes Q(X;theta.) wrt. theta. While the innate estimator has certain advantages, we show that the asymptotically efficient estimator is defined from a likelihood function in conjunction with Q. The likelihood-based estimator is, however, fragile, as misspecification is harmful in two ways. First, the likelihood-based estimator may be inefficient under misspecification. Second, and more importantly, the likelihood approach requires a parameter transformation that depends on the truth, causing an improper mapping to be used under misspecification. The theoretical results are illustrated with two applications comprising asymmetric loss and multi-step forecasting, respectively.","PeriodicalId":433005,"journal":{"name":"Econometrics: Data Collection & Data Estimation Methodology eJournal","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126714659","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}