Development of Composite Indicators of Cyclical Response in Business Surveys
Bibliographic record
Abstract
The article proposes a new set of composite indicators-predictors in business tendency surveys, which allow identifying early information signals of a cyclical nature in the economic behavior of business agents. The main criterion for the efficiency of such indicators is their sensitivity to a cyclical pattern and changes in the dynamics of statistical referents. Property such as a statistically significant lead in time series or earlier publication allows them to be combined into indicators of early response. The composite Business Activity Indicator (BAI) in the basic sectors of the Russian economy is calculated by the authors for the first time based on the results of regular (monthly and quarterly) business surveys of Rosstat for 1998–2020 with a large-scale coverage of sampling units. In 2020, the number of survey respondents averaged about 20,000 organizations of all sizes. The index reflects the «common» profile in the dynamics of short-term fluctuations of the key parameters of the economic environment, which consists of the «balances of opinions» of respondents to the questions unified for all sectoral surveys and connected with the reference quantitative statistics with cross-correlation coefficients that are statistically significantly different from zero, with a lead at least one quarter. This is its main difference from the well-known indices of economic sentiment and entrepreneurial confidence. The main components of the BAI are the new composite indices of real demand, current output, real employment, total profits and economic situation. They aggregate the relevant «order» statistics for the basic sectors of the national economy, including the main kinds of industrial activities, retail trade, construction, and services.The article provides a methodological substantiation and an extended procedure for identifying the BAI components; their composition is formed for the entire set of retrospective results of business tendency monitoring in Russia. A new Aggregate Economic Vulnerability Indicator with a counterdirectional profile and varying degrees of symmetry of its dynamics relative to the short-term movement of the BAI is being introduced as the main limitation of business activity. Proactive monitoring of emerging vulnerabilities in the business environment is necessary to warn their large-scale accumulation, prevent the risks of economic downturns and ensure the highest possible macroeconomic stability. This integrated approach makes it possible to determine the novelty of the proposed measurements of short-term cyclical fluctuations in economic development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".