The Relationship between External and Internal Performance Measures of the Firm: A Panel Cointegration Approach
Bibliographic record
Abstract
This article uses the recent developments in the econometrics of non-stationary dynamic panel data to re-examine the relationship between external and internal performance measures of the firm. A sample of 420 U.S. firms over the period (1990-2004) is used in the empirical analysis. In addition, four sub-samples are specially designed according to two contextual factors, namely, size of the firm and its life cycle. The panel unit root test of Im, Pesaran and Shin (2003) and the panel cointegration test of Pedroni (2004) were applied on the overall sample and on the four sub-samples to verify the existence of a long term equilibrium between market value added per share (MVA), which is the external performance measure, and four internal performance measures, namely, earnings per share (EPS), cash flow from operations per share (CFO), residual income per share (RI) and economic value added per share (EVA). Our main results show that the cointegration relationship between MVA and EVA is the most powerful, compared to the other models. Also, regardless of the firm-size factor and the firm’s life cycle factor we found the same results. Several explanations are provided for the above findings supported by a robustness analysis using panel error-correction models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".