Macroeconomic impact of FDI inflows: an ARDL approach for the case of India
Bibliographic record
Abstract
This article investigates the dynamics of relationship between foreign direct investment (FDI) inflows and major macroeconomic variables, i.e. gross domestic product (GDP), exports and exchange rate for India. Using annual time series data, the empirical analysis has been carried out for the period 1980–2012. Using the most recent autoregressive distributed lag (ARDL) testing approach to cointegration proposed by Pesaran et al., 2001 Perron, P. (1989). The great crash, the oil price shock and the unit root hypothesis. Econometrica, 57, 1361–1401.doi:10.2307/1913712[Crossref], [Web of Science ®] , [Google Scholar], the study concludes that there is a strong evidence of long-run relationship between variables with GDP, FDI inflows and exports as the dependent variable. However, there is lack of evidence of long-run cointegration with exchange rate as a dependent variable. The results indicate bilateral positive and significant relationship between FDI inflows and GDP; and GDP and exports in the long run and short run. In contrast, the result analysis indicates negative impact of FDI inflows on exports in the long run. Moreover, exchange rate appreciation has a positive impact on FDI inflows and exports, but negative on GDP in the long run. The error-correction term (ECT) for the models signifies a fairly quick speed of adjustment to the equilibrium following a shock. Furthermore, the study also observes long-run causality running from the explanatory variables towards the respective dependent variables.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".