To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

This dataset is about book series. It has 1 row and is filtered where the books is Bayesian analysis in econometrics and statistics : the Zellner view and papers. It features 2 columns including publication dates.

This dataset contains panel data for a sample of 15 countries (Australia, Austria, Canada, China, Denmark, France, Germany, Israel, Italy, Japan, Republic of Korea, Spain, Sweden, Switzerland and United States) over the period 2006-2015. The series used are available for a small number of developed countries and for a relatively short time period. Solar PV module prices, imports of solar PV panels and public budget for R&D in PV are in real terms and were obtained by dividing them by the United States GDP deflator. The series are obtained from five main sources. Imports value of solar PV panels series are taken from Commodity Trade Statistics database (COMTRADE). PV panels (cells and modules) are a part of the category HS 854140, "Photosensitive Semiconductor Devices, Photovoltaic Cells and Light-Emitting Diodes". Solar PV module prices, cumulative installed PV capacity and public budget for R&D in PV series are constructed from the PVPS report Trends in Photovoltaic Applications of the International Energy Agency (IEA). Population density, political stability index, renewable energy consumption and per capita carbon dioxide emissions series are all obtained from the World Bank (WB). Real GDP per capita series is taken from Federal Reserve Bank of St. Louis (FRED). Technological development in PV and crude oil import price series are drawn from the Organisation for Economic Co-operation and Development (OECD) database. Since crude oil import price series are not available for China and Israel, we use the West Texas Intermediate spot crude oil price as a proxy. The dummy for presence of feed-in tariff is constructed from the OECD database.

Description

Subscriptions to economics journals at US libraries, for the year 2000.

Usage

data("Journals")

Format

A data frame containing 180 observations on 10 variables.

title

Journal title.

publisher

factor with publisher name.

society

factor. Is the journal published by a scholarly society?

price

Library subscription price.

pages

Number of pages.

charpp

Characters per page.

citations

Total number of citations.

foundingyear

Year journal was founded.

subs

Number of library subscriptions.

field

factor with field description.

Details

Data on 180 economic journals, collected in particular for analyzing journal pricing. See also https://econ.ucsb.edu/~tedb/Journals/jpricing.html for general information on this topic as well as a more up-to-date version of the data set. This version is taken from Stock and Watson (2007).

The data as obtained from the online complements for Stock and Watson (2007) contained two journals with title “World Development”. One of these (observation 80) seemed to be an error and was changed to “The World Economy”.

Source

Online complements to Stock and Watson (2007).

References

Bergstrom, T. (2001). Free Labor for Costly Journals? Journal of Economic Perspectives, 15, 183–198.

Stock, J.H. and Watson, M.W. (2007). Introduction to Econometrics, 2nd ed. Boston: Addison Wesley.

Examples

data and transformed variables

data("Journals") journals <- Journals[, c("subs", "price")] journals$citeprice <- Journals$price/Journals$citations journals$age <- 2000 - Journals$foundingyear journals$chars <- Journals$charpp*Journals$pages/10^6

Stock and Watson (2007)

Figure 8.9 (a) and (b)

plot(subs ~ citeprice, data = journals, pch = 19) plot(log(subs) ~ log(citeprice), data = journals, pch = 19) fm1 <- lm(log(subs) ~ log(citeprice), data = journals) abline(fm1)

Table 8.2, use HC1 for comparability with Stata

fm2 <- lm(subs ~ citeprice + age + chars, data = log(journals)) fm3 <- lm(subs ~ citeprice + I(citeprice^2) + I(citeprice^3) + age + I(age * citeprice) + chars, data = log(journals)) fm4 <- lm(subs ~ citeprice + age + I(age * citeprice) + chars, data = log(journals)) coeftest(fm1, vcov = vcovHC(fm1, type = "HC1")) coeftest(fm2, vcov = vcovHC(fm2, type = "HC1")) coeftest(fm3, vcov = vcovHC(fm3, type = "HC1")) coeftest(fm4, vcov = vcovHC(fm4, type = "HC1")) waldtest(fm3, fm4, vcov = vcovHC(fm3, type = "HC1"))

changes with respect to age

library("strucchange")

Nyblom-Hansen test

scus <- gefp(subs ~ citeprice, data = log(journals), fit = lm, order.by = ~ age) plot(scus, functional = meanL2BB)

estimate breakpoint(s)

journals <- journals[order(journals$age),] bp <- breakpoints(subs ~ citeprice, data = log(journals), h = 20) plot(bp) bp.age <- journals$age[bp$breakpoints]

visualization

plot(subs ~ citeprice, data = log(journals), pch = 19, col = (age > log(bp.age)) + 1) abline(coef(bp)[1,], col = 1) abline(coef(bp)[2,], col = 2) legend("bottomleft", legend = c("age > 18", "age < 18"), lty = 1, col = 2:1, bty = "n")

This panel dataset presents information on the impact of democracy and political stability on economic growth in 15 MENA countries for the period 1983-2022. The data are collected from five different sources; the World Bank Development Indicators (WDI), the World Bank Governance Indicators (WGI), the Penn World Table (PWT), Polity5 from the Integrated Network for Societal Conflict Research (INSCR), and the Varieties of Democracy (V-Dem). The dataset includes ten variables related to economic growth, democracy, and political stability. Data analysis was performed using statistical methods such as R in order to ensure data reliability through imputing missing data; hence, enabling future researchers to explore the impact of political factors on growth in various contexts. The data are presented in two sheets, before and after the imputation for missing values. The potential reuse of this dataset lies in the ability to examine the impact of different political factors on economic growth in the region.

The database collect and aggregate the database of Goods and Services at 2 digits. While the data of Goods is already reconciled and regularized, the data of Service has been reconstructed in the present data.

Bayesian updating is the dominant theory of learning. However, the theory is silent about how individuals react to events that were previously unforeseen. We study how decision makers update their beliefs if unforeseen events materialize, and under which conditions they revise their views about previously observed relationships. We base our analysis on the framework of “reverse Bayesianism”, under which the relative likelihoods of prior beliefs remain unchanged after an unforeseen event materializes. We find that participants do not systematically deviate from reverse Bayesianism when the unforeseen changes result in a new world that contains elements of the old world. In contrast, if a regime change is possible, decision makers eventually overhaul their model of the old world in favor of a completely different view of uncertainty.

The data used in this study are secondary data obtained from the Statistics of Indonesia in 2021. The data used are GRDP data at current prices, labor and labor wages, and investment data in the form of domestic investment and foreign investment for each of the main categories, namely the agriculture category, the manufacturing industry category, the construction category, and the wholesale and retail trade category

This paper explores a unique dataset of all the SET ratings provided by students of one university in Poland at the end of the winter semester of the 2020/2021 academic year. The SET questionnaire used by this university is presented in Appendix 1. The dataset is unique for several reasons. It covers all SET surveys filled by students in all fields and levels of study offered by the university. In the period analysed, the university was entirely in the online regime amid the Covid-19 pandemic. While the expected learning outcomes formally have not been changed, the online mode of study could have affected the grading policy and could have implications for some of the studied SET biases. This Covid-19 effect is captured by econometric models and discussed in the paper. The average SET scores were matched with the characteristics of the teacher for degree, seniority, gender, and SET scores in the past six semesters; the course characteristics for time of day, day of the week, course type, course breadth, class duration, and class size; the attributes of the SET survey responses as the percentage of students providing SET feedback; and the grades of the course for the mean, standard deviation, and percentage failed. Data on course grades are also available for the previous six semesters. This rich dataset allows many of the biases reported in the literature to be tested for and new hypotheses to be formulated, as presented in the introduction section. The unit of observation or the single row in the data set is identified by three parameters: teacher unique id (j), course unique id (k) and the question number in the SET questionnaire (n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9} ). It means that for each pair (j,k), we have nine rows, one for each SET survey question, or sometimes less when students did not answer one of the SET questions at all. For example, the dependent variable SET_score_avg(j,k,n) for the triplet (j=Calculus, k=John Smith, n=2) is calculated as the average of all Likert-scale answers to question nr 2 in the SET survey distributed to all students that took the Calculus course taught by John Smith. The data set has 8,015 such observations or rows. The full list of variables or columns in the data set included in the analysis is presented in the attached filesection. Their description refers to the triplet (teacher id = j, course id = k, question number = n). When the last value of the triplet (n) is dropped, it means that the variable takes the same values for all n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9}.Two attachments:- word file with variables description- Rdata file with the data set (for R language).Appendix 1. Appendix 1. The SET questionnaire was used for this paper. Evaluation survey of the teaching staff of [university name] Please, complete the following evaluation form, which aims to assess the lecturer’s performance. Only one answer should be indicated for each question. The answers are coded in the following way: 5- I strongly agree; 4- I agree; 3- Neutral; 2- I don’t agree; 1- I strongly don’t agree. Questions 1 2 3 4 5 I learnt a lot during the course. ○ ○ ○ ○ ○ I think that the knowledge acquired during the course is very useful. ○ ○ ○ ○ ○ The professor used activities to make the class more engaging. ○ ○ ○ ○ ○ If it was possible, I would enroll for the course conducted by this lecturer again. ○ ○ ○ ○ ○ The classes started on time. ○ ○ ○ ○ ○ The lecturer always used time efficiently. ○ ○ ○ ○ ○ The lecturer delivered the class content in an understandable and efficient way. ○ ○ ○ ○ ○ The lecturer was available when we had doubts. ○ ○ ○ ○ ○ The lecturer treated all students equally regardless of their race, background and ethnicity. ○ ○

Synthetic version of cost and effectiveness data from the SADD randomised clinical trial. Contains utility and health-care costs for 219 participants measured at baseline, 3 months and 9 months. This is a fictional (simulated) dataset with similar features and data distribution than the original dataset. Dataset in CSV and Stata (.dta) format.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for statistics and financial econometrics (us equivalent) in the U.S.

77 time series and R code used to experiment forecasting methods.

Dear colleagues!

This dataset was collected for my Bachelor Thesis in 2023. Further, it was used for the scientific paper: https://doi.org/10.24891/fc.29.12.2670.

It contains information about 76 largest public companies (according to their market capitalization) from 3 macro-regions: the USA, the EU and Russia. The data was collected using parcing techniques from Yahoo Finance. All features are valied for 2021. Columns Name and Region is for company name and the region its quarters are. Columns Fin1-Fin7 are financial indicators. Edu1-Edu5 - binary variables referred to education of a company's CEO, Edu6 - rating of the university a particular CEO attended.

Target metrics are Y1 - Q-Tobin coefficient, Y2 - ROA.

Main tools for my research was econometrics and statistics.

Context

The following dataset gives has a small sample of the prices of flats in Moscow.

Content

Inside you'll find the price and some variables such as the space, the distance to the center and the distance to the metro.

Acknowledgements

The following dataset was provided as a course material for Econometrics, taught by Boris Demeshev, professor at the Higher School of Economics Moscow. The origin is non specified. Feel free to check out the course (russian only) here.

Inspiration

Uploaded to be used in an introductory class of R for the purpose of data visualization and forecasting.

This data is used for article of macroeconomic of some Asian countries in long period which explained about four Asian countries, such as Indonesia, Malaysia, Singapore, and South Korea. This data has taken from World Bank Development Indicators (WDI) database and is formed by Vector Auto Regression (VAR) model, then empirical result is executed by Granger causality model on E-views 11 program to gauge the relationship between gross domestic product, exchange rate, inflation rate, foreign direct investment, net export, government expenditures, unemployment rate, and savings. The results showed that most of gross domestic product of sample and other macro-economy variables have not causality relationship.

The programs replicate tables and figures from "Identifying Prediction Mistakes in Observational Data," by Ashesh Rambachan. Please see the Readme file for additional details.

The dataset contains returns data for Baltic Dry Index and commodity spot prices

DESCRIPTION There are 2 files: -"Sintax1.txt" to run in Stata and obtain the simulated data and the regressions -"1.dta" The specific random dataset I obtained

Data sets for "Decoding Team and Individual Impact in Science and Invention". The file name indicates the corresponding figure in the paper. These files are tab-delimited text with variable names in the first row.

The daily frequency data on minimum, maximum, and optimal bitcoin annualized energy consumption from July 7, 2010 to December 4, 2021.