The Relationship between Foreign Direct Investment and Inflation: Econometric Analysis and Forecasts in the Case of Sri Lanka
Bibliographic record
Abstract
There are several reasons why the dynamic interaction between FDI and inflation must be studied. First, Foreign Direct Investment is found as one of the important determinants of the process of economic growth and development of Sri Lanka. Therefore, the literature empirically examining the causal relationship between the inflation and FDI is significant because the rate of high inflation affects the inflows of FDI inflows into the economy of Sri Lanka and slows down the process of economic growth and development. The main objective of this study is to examine the linkages between FDI and inflation in Sri Lanka for the time periods from year 1978 to year 2017. The dependent variable of the model used in this study is Inflation and the independent variable of the model is FDI (Foreign Direct Investment). The data used in the model are the annual time series collected from Annual Report of Central Bank of Sri Lanka. The tools to analyze the data are graphical representation, Johansen Co-integration test, simple regression model, Residual Analysis, Stability Test, and Granger Causality Test. A long run relationship is found between the variables. The dependent variable: INF – Inflation is inversely related with the independent variable: FDI – Foreign Direct Investment. One-way causal relationship from FDI to INF is ensured. The forecast sample is ranged from 2009 to 2017. The simple regression model affirms the significant impacts of the FDI – Foreign Direct Investment on the INF – Inflation. The forecasting model derived from the simple regression model is rather incompatible to forecast the value of dependent variable (Inflation).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".