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Record W4312589491 · doi:10.47509/ijfe.2022.v03i01.07

THE EFFECT OF EXCHANGE RATE UNCERTAINTY ON DOMESTIC INVESTMENT IN ETHIOPIA

2022· article· en· W4312589491 on OpenAlexaboutno aff
Naser Yenus Nuru, Hiluf Techane Gidey

Bibliographic record

VenueINDIAN JOURNAL OF FINANCE AND ECONOMICS · 2022
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsnot available
Fundersnot available
KeywordsExchange rateEconomicsShock (circulatory)Investment (military)EconometricsStandard deviationMonetary economicsInflation (cosmology)Quarter (Canadian coin)Impulse responseStatisticsMathematicsGeographyPhysics

Abstract

fetched live from OpenAlex

There is no yet clear theoretical and empirical consensus on the relationship between exchange rate uncertainty and domestic investment. The main purpose of this study, therefore, is to examine the effect of real effective exchange rate uncertainty on domestic investment for the Ethiopian economy over the sample period 1992Q1- 2016Q1. To address this objective, Jordà’s (2005) local projection method is employed and generalized impulse response functions are generated in this study. The impulse response functions exhibit that one standard deviation shock in exchange rate uncertainty stimulates domestic investment for the Ethiopian economy. In response to one standard deviation shock in exchange rate uncertainty, domestic investment increases to around 4 percent at the second quarter. This may indicate the existence of risk neutral or insensitive domestic investors to exchange rate uncertainty in Ethiopia. As to the effects of other control variables, domestic investment also increases in response to real income and real effective exchange rate shocks. The effect of inflation shock on domestic investment is positive and statistically significant up to the eighth quarter, and negative and significant afterwards.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.237

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.206
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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