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.

The classical paradigm of asymptotic theory employed in econometrics presumes that model dimensionality, p, is fixed as sample size, n, tends to inifinity. Is this a plausible meta-model of econometric model building? To investigate this question empirically, several meta-models of cross-sectional wage equation models are estimated and it is concluded that in the wage-equation literature at least that p increases with n roughly like n l/4, while that hypothesis of fixed model dimensionality of the classical asymptotic paradigm is decisively rejected. The recent theoretical literature on large-p asymptotics is then very briefly surveyed, and it is argued that a new paradigm for asymptotic theory has already emerged which explicitly permits p to grow with n. These results offer some guidance to econometric model builders in assessing the validity of standard asymptotic confidence regions and test statistics, and may eventually yield useful correction factors to conventional test procedures when p is non-negligible relative to n.

Econometric Analysis Software Market Outlook

The global econometric analysis software market size was valued at approximately USD 1.5 billion in 2023 and is expected to reach around USD 3.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.5% during the forecast period. One of the primary growth factors propelling this market is the increasing reliance on data-driven decision-making across various sectors, including finance, economics, and business, which has significantly amplified the demand for robust econometric tools.

The growth of the econometric analysis software market is largely fueled by the exponential increase in data generation and the corresponding need for sophisticated tools to analyze and interpret this data. As businesses and governments increasingly turn to big data analytics to inform their strategies and policies, the need for advanced econometric software has become more pronounced. The integration of artificial intelligence (AI) and machine learning (ML) with econometric analysis software has further enhanced its capabilities, enabling more accurate forecasting, trend analysis, and risk management. Additionally, the rise of cloud computing has made these tools more accessible and scalable, driving adoption across various sectors.

Another significant growth driver is the increasing complexity and volatility of global markets. With economies becoming more interconnected, the ability to model and predict economic outcomes has become crucial for financial institutions, corporations, and policymakers. Econometric analysis software provides these users with the analytical power to simulate different economic scenarios, assess the impact of policy changes, and make informed decisions. The rising importance of economic forecasting in strategic planning and risk management is expected to continue driving the demand for these tools in the coming years.

The growing emphasis on research and development (R&D) activities is also contributing to the expansion of the econometric analysis software market. Academic institutions and research organizations are increasingly adopting these tools to conduct advanced economic research and publish findings that influence both policy and practice. Government agencies are also leveraging econometric software to analyze economic indicators and assess the impact of various interventions. This widespread adoption across different user groups underscores the versatility and indispensability of econometric analysis software in modern economic analysis.

Regionally, North America holds a significant share of the global econometric analysis software market, driven by the presence of major software providers and a high concentration of financial institutions and research organizations. Europe and Asia Pacific are also key markets, with increasing investments in data analytics and economic research fueling growth in these regions. The Asia Pacific market, in particular, is expected to witness the highest growth rate, driven by rapid economic development, increasing digitalization, and a growing emphasis on data-driven decision-making across sectors.

In the realm of data analytics, Time Series Databases Software has emerged as a crucial component for managing and analyzing time-stamped data. These databases are specifically designed to handle large volumes of data generated over time, enabling users to efficiently store, query, and analyze temporal data patterns. The integration of Time Series Databases Software with econometric analysis tools can significantly enhance the ability to forecast economic trends and model complex economic scenarios. By leveraging the capabilities of these databases, organizations can gain deeper insights into time-dependent variables, improving the accuracy of economic predictions and strategic decision-making processes.

Component Analysis

The econometric analysis software market is segmented by component into software and services. The software segment dominates the market, driven by the increasing adoption of advanced econometric tools across various applications. These software solutions provide a wide range of functionalities, including statistical analysis, forecasting, and data visualization, which are essential for conducting comprehensive economic analysis. The integration of AI and ML technologies into these software solutions has further enhanced their analytical capabilities, making them indispensable tools for economists and anal

This dataset includes seven variables calculated based on econometric models for financial market analysis. These variables are: 1) adjusted conditional volatility from GARCH(1,1); 2) adjusted residuals from GARCH(1,1); 3) adjusted conditional volatility from EGARCH; 4) leverage effect from EGARCH; 5) leverage from EGARCH; 6) adjusted conditional volatility from EWMA; and 7) adjusted residuals from EWMA. These data are based on the works of Hyup Roh (2007) and H.Y. Kim & Won (2018). They are used for analyzing financial market volatility and predicting future market behaviors.

We develop attractive functional forms and simple quasi-likelihood estimation methods for regression models with a fractional dependent variable. Compared with log-odds type procedures, there is no difficulty in recovering the regression function for the fractional variable, and there is no need to use ad hoc transformations to handle data at the extreme values of zero and one. We also offer some new, robust specification tests by nesting the logit or probit function in a more general functional form. We apply these methods to a data set of employee participation rates in 401(k) pension plans.

This dataset is about books. It has 1 row and is filtered where the book is The use of econometric models by federal regulatory agencies. It features 7 columns including author, publication date, language, and book publisher.

R, an open-source programming environment for data analysis and graphics, has in only a decade grown to become a de-facto standard for statistical analysis against which many popular commercial programs may be measured. The use of R for the teaching of econometric methods is appealing. It provides cutting-edge statistical methods which are, by R's open-source nature, available immediately. The software is stable, available at no cost, and exists for a number of platforms, including various flavours of Unix and Linux, Windows (9x/NT/2000), and the MacOS. Manuals are also available for download at no cost, and there is extensive on-line information for the novice user. This review focuses on using R for teaching econometrics. Since R is an extremely powerful environment, this review should also be of interest to researchers.

This dataset is about book subjects. It has 1 row and is filtered where the books is Econometric analysis of cross section and panel data. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Regression results of a spatial econometric model of high-quality development level of various types of cities.

This dataset is about books. It has 3 rows and is filtered where the book subjects is Time-series analysis-Econometric models. It features 9 columns including author, publication date, language, and book publisher.

This study evaluates high-frequency financial market stability during major economic shocks and regulatory interventions using advanced nonlinear econometric models. Focusing on events like the Global Financial Crisis, Brexit, COVID-19 crisis, Quantitative Easing, and the Eurozone Debt Crisis, the analysis employs high-frequency datasets including enriched temporal and interaction-based variables. Comprehensive exploratory data analysis revealed significant variability in financial metrics showing the dynamic market responses to crises. Among the evaluated models—GARCH, ARIMA, LSTM, and Markov Switching—the GARCH model demonstrated superior performance with consistent error metrics (MAE: 0.72, RMSE: 0.68) across k-fold cross-validation. Results highlight heightened volatility and negative abnormal returns during geopolitical and economic shocks, with regulatory interventions like Quantitative Easing mitigating instability. The nonlinear patterns in stability indicators revealed persistent volatility clustering effects, particularly during crises. Methodological rigor ensured robust out-of-sample validation, confirming the reliability of GARCH in capturing market dynamics. The findings emphasize the importance of combining statistical and machine learning approaches for predictive modeling and informed decision-making in financial markets. This paper provides a foundational framework for understanding and forecasting market stability under varying economic conditions, contributing to the literature on nonlinear econometrics and financial risk analysis.

2023

This manual describes the input requirements and the installation procedures of the program for stochastic simulation of econometric models, announced in Econometrica, volume 46, number 1, (January 1978). This program is available on magnetic tape, including samples (Klein-I and Klein-Goldberger models) and installation procedures under the operating system VM-370/CMS; format and contents of the tape are briefly described. The input data sets and the final printed output of the stochastic simulation of the Klein-I model are displayed.

Through econometric tests, the thesis aims to identify significant variables that influence intermodal rail freight transport volume. By synthesizing theoretical insights with empirical findings, this research contributes to economics and logistics management, offering comprehensive guidance for policymakers, industry stakeholders, and academic researchers in optimizing intermodal transport systems for a sustainable and efficient future.

Dataset Card for "mmlu-econometrics-rule-neg-prepend"

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These are replication files for Beyond Policy Diffusion: Spatial Econometric Models of Public Administration, published in the Journal of Public Administration Research & Theory. The public health dataset was originally compiled and analyzed by Ling Zhu (Zhu, Ling. 2013. Panel data analysis in public administration: Substantive and statistical considerations. Journal of Public Administration Research and Theory 23: 395-4.).

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.

Recent software developments are reviewed from the vantage point of reproducible econometric research. We argue that the emergence of new tools, particularly in the open-source community, have greatly eased the burden of documenting and archiving both empirical and simulation work in econometrics. Some of these tools are highlighted in the discussion of two small replication exercises.

The dataset includes entrepreneurship development indicators: - Number of enterprises and organizations (at the end of the year) (2020), units and thousand units; - Turnover of organizations (2020), billion rubles; - Balanced financial result (profit minus loss) of organizations' activities (2020), million rubles and billion rubles; - Share of organizations using special software (2020), % The dataset also contains indicators such as Ыhare of organizations implementing technological innovations (2020), %; Gross regional product (GRP) (2019), million rubles. The values of indicators are given for 96 main territories of the Russian Federation allocated by Rosstat, including regions, federal districts and largest cities (federal centers). The data are given by regions for 2020, as well as for Russia as a whole for 2010, 2015 and 2020. Source of the data: Regions of Russia. Socio-economic indicators - 2021 [Electronic resource]. - Rosstat. – Access mode: https://rosstat.gov.ru/folder/210/document/13204 The dataset is available in Russian (на Русском) and English (in separate files).

The data and programs replicate tables and figures from "Visual Inference and Graphical Representation in Regression Discontinuity Designs", by Korting, Lieberman, Matsudaira, Pei, and Shen. Please see the readme file for additional details.