The global econometric analysis software market is experiencing robust growth, driven by increasing demand for sophisticated data analysis tools across diverse sectors. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $7 billion by 2033. This expansion is fueled by several key factors. Firstly, the proliferation of big data necessitates advanced analytical capabilities for extracting meaningful insights. Secondly, the rising adoption of econometrics in business decision-making, particularly in finance, economics, and market research, is significantly contributing to market growth. Thirdly, continuous technological advancements leading to improved software functionality, user-friendliness, and accessibility are driving wider adoption across various user segments. Finally, the expanding educational sector and the growing need for specialized training in econometrics are also boosting market demand. Despite the favorable market outlook, certain restraints are present. The high cost of advanced software licenses and the need for specialized expertise can limit accessibility for smaller businesses and individual researchers. Furthermore, competition among established players like IBM, EViews, Microsoft, StataCorp, and SAS, alongside the emergence of open-source alternatives like R Project, creates a dynamic and potentially price-sensitive market. However, the continued development of user-friendly interfaces and cloud-based solutions is expected to mitigate these challenges, making econometric software more accessible and affordable. The market segmentation shows a clear preference for programmable software in business applications, indicating a focus on advanced analytical techniques in this segment. Growth will likely be strongest in North America and Asia-Pacific regions, driven by robust technological adoption and significant economic activity.

This research used econometric techniques to evaluate 73 relining jobs for large diameter steel pipe interiors to identify major cost drivers for such relining jobs and to specify a regression model for predicting future relining job costs. Reclamation’s inventory of 121 unrelined penstocks was evaluated with the final model to predict preliminary-level costs for future relining work. An app tool was developed using Microsoft Power Apps as an end-user interface for predicting relining costs based on the final regression model. A subsequent Microsoft Excel tool was developed to share with the study’s contribution partner agencies: Metropolitan Water District, Central Arizona Project, Denver Water, and BC Hydro.

Computers are now involved in many economic transactions and can capture data associated with these transactions, which can then be manipulated and analyzed. Conventional statistical and econometric techniques such as regression often work well, but there are issues unique to big datasets that may require different tools. First, the sheer size of the data involved may require more powerful data manipulation tools. Second, we may have more potential predictors than appropriate for estimation, so we need to do some kind of variable selection. Third, large datasets may allow for more flexible relationships than simple linear models. Machine learning techniques such as decision trees, support vector machines, neural nets, deep learning, and so on may allow for more effective ways to model complex relationships. In this essay, I will describe a few of these tools for manipulating and analyzing big data. I believe that these methods have a lot to offer and should be more widely known and used by economists.

Tables from econometric analysis with Swiss firm panel data. A detailed description can be found in the study on the EPO's homepage (title of the study: "Knowledge spillovers from product and process inventions and their impact on firm performance"): https://www.epo.org/learning-events/materials/academic-research-programme/research-project-grants.html Funding by the "European Office Academic Research Programme" is gratefully ackknowledged.

This data is used for examination of inflation- unemployment relationship for 18 countries after 1991. Inflation data is obtained from World Bank database (https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG) and unemployment data is obtained from International Labor Organization (http://www.ilo.org/wesodata/).

Analysis period is different for all countries because of structural breaks determined by single point change point detection algorithm included in changepoint package of Killick & Eckley (2014). Granger-causality is conducted with Toda&Yamamoto (1995) procedure. Integration levels are determined with 3 stationary tests. VAR models are run with vars package (Pfaff, Stigler & Pfaff; 2018) without trend and constant terms. Cointegration test is conducted with urca package (Pfaff, Zivot, Stigler & Pfaff; 2016).

All data files are .csv files. Analyst need to change country index (variable name: j) in order to see individual results. Findings can be seen in the article.

Killick, R., & Eckley, I. (2014). changepoint: An R package for changepoint analysis. Journal of statistical software, 58(3), 1-19.

Pfaff, B., Stigler, M., & Pfaff, M. B. (2018). Package ‘vars’. Online] https://cran. r-project. org/web/packages/vars/vars. pdf.

Pfaff, B., Zivot, E., Stigler, M., & Pfaff, M. B. (2016). Package ‘urca’. Unit root and cointegration tests for time series data. R package version, 1-2.

Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of econometrics, 66(1-2), 225-250.

Given that sexual attraction is a key driver of human mate choice and reproduction, we descriptively assess relative sex differences in the level of attraction individuals expect in the aesthetic, resource, and personality characteristics of potential mates. As a novelty we explore how male and female sexual attractiveness preference changes across age, using a dataset comprising online, cross-sectional survey data for over 7,000 respondents across a broad age distribution of individuals between 18 and 65 years.

In an ongoing evaluation of post-secondary financial aid, we use random assignment to assess the causal effects of large privately-funded aid awards. Here, we compare the unbiased causal effect estimates from our RCT with two types of non-experimental econometric estimates. The first applies a selection-on-observables assumption in data from an earlier, nonrandomized cohort; the second uses a regression discontinuity design. Selection-on-observables methods generate estimates well below the experimental benchmark. Regression discontinuity estimates are similar to experimental estimates for students near the cutoff, but sensitive to controlling for the running variable, which is unusually coarse.

This research was undertaking to find the linkage between the Cocoa Rehabilitation Project (CRP) introduced into the cocoa sector of Ghana from 1988 to 1993 and cocoa exports in Ghana over the last 3 decades. The research identifies and analyses Ghana cocoa exports as the response variable whiles Ghana cocoa production, world cocoa exports, world cocoa production, world cocoa prices, GDP growth rate and real exchange rate are analyzed as explanatory. The study concludes that in the long-run, none of the explanatory variables have any significant impact on the response variable. The data was gathered based on the parameters set out to achieve at the end of the study which was to identify the possible influence the CRP may have had on the success or failures of cocoa exports in Ghana for the period under study.

Project paper, Financial Econometrics Class

Harvest choice model estimates and fit statistics.

I examine replications of empirical papers in development economics published in the top-5 and next-5 general interest journals between the years 2000 through 2015. Of the 1,138 empirical papers, 71 papers (6.2 percent) were replicated in another published paper or working paper. The majority (77.5 percent) of replications involved reanalysis of the data using different econometric specifications to assess robustness. The strongest predictor of whether a paper is replicated or not is the paper's Google Scholar citation count, followed by year of publication. Papers based on randomized control trials (RCTs) appear to be replicated at a higher rate (12.5 percent).

Daily realised volatilities for the Dow Jones Index and 26 individual stocks.

The Realised Volatility data was used to evaluate different volatility forecasting methods. The Realised Volatility data was calculated using underlying high frequency prices obtained from Thomson Reuters Datascope.

This project provides codes and data necessary to replicate the results in "International Transmission of Inequality through Trade" by Sergey Nigai

Analysis of price formation in industries in the Netherlands, 1961-1979, with emphasis on the role of foreign competition and market structure.

DOI Data collected based on secondary sourcesThe use of panels where the number of time periods and cross section units varies across applications creates a number of challenges for statisticians and econometricians, as well as for economic theory where network interactions are of interest. One very common form of interaction is spatial. Closeness or geographical contiguity is observable and there is a well developed field of spatial econometrics that deals with these issues. When the interaction is unobservable it may be that there is a common factor at work-global warming, for example, or a world financial crisis with pervasive effects globally. But there can also be more local forms of interaction which in addition to spatial patterns could take place in more abstract spaces such as social or economic networks.These abstract interactions can be both strong and weak. Strong interactions do not die away as the number of agents increases or as we move away from a 'neighbourhood'. Weak interactions do.This project will address these issues by developing econometric techniques for taking account of these interactions in a wide range of applications in economics.

This project contains the publicly available data and code for the paper "Examining Chinese volume--volatility nexus: A regime-switching perspective" to ensure the reproducibility of the results.

Contains the code and data file used in the article, "Replicate and Extend: An Approach for Term Paper Assignments in Introductory Econometrics."

Reconciled historical estimates of monthly GDP, and its uncertainty, from 1960 to the present day produced from the model explained in Koop, G., McIntyre, S., Mitchell, J., & Poon, A. (2022). Reconciled Estimates of Monthly GDP in the United States. Journal of Business & Economic Statistics, 41(2), 563–577. https://doi.org/10.1080/07350015.2022.2044336The model used is a Bayesian mixed frequency vector autoregression (MF-VAR) involving GDP on the expenditure- (GDPE) and income-side (GDPI), unobserved "true" GDP, and monthly indicators of short-term economic activity. The Excel file includes an Info tab with the run date for each data vintage. The Time column is presented in DD/MM/YYYY.DisclaimerThese data are updated by the authors and are not an official product of the Federal Reserve Bank of Cleveland.

This fileset contains the supplement, the data and the replication code for the paper "The Link between Regional Temperature and Regional Incomes: Econometric Evidence with Sub-National Data".Abstract: We present a micro-based approach to evaluate the effect of water- and health-related development projects which complements established evaluation methods. We collect information from 1.8 million individuals from DHS clusters (Demographic and Health Surveys) in 38 developing economies between 1986 and 2017. By geocodes, we combine cluster information with over 14,000 sub-national projects from the World Bank. We then investigate the impact of the projects employing fixed effects estimation techniques. Our findings indicate that the time to gather water and child mortality tend to decrease when projects are realized. The quality of drinking water and sanitation facilities are also positively affected by projects. Our data allows us to account for cluster heterogeneity, which is an important extension to the cross-country literature. Various robustness checks support our findings.

Spatial econometric results of the SDM.