The Determinants of Inflation in Sri Lanka

Bandara, Ranjith

doi:10.1177/139156141101200204

Cited by 10 publications

(3 citation statements)

References 20 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Applied economic research, since the last three decades, has increasingly been giving emphasis to the role of fiscal variables in determining inflation in countries concerned. In the case of Sri Lanka, few studies have examined the role of monetary factors in inflation (Bandara, 2011; Cooray, 2008; Kulatunge, 2017; Maitra & Debnath, 2015; Ratnasiri, 2011). However, the role of fiscal factors in inflation is scantly documented.…”

Section: Introductionmentioning

confidence: 99%

Potency of Fiscal Variables in Inf lation Variations in Sri Lanka

Maitra

Mondal

2022

The Indian Economic Journal

View full text Add to dashboard Cite

Although the monetary policy impact on inflation is deliberated intensively in economic literature, the fiscal policy impact has not gained much interest, particularly in the developing countries. The later issue is theorised as the fiscal theory of price level. This article examines the potency of fiscal factors relative to the money growth in inflation in Sri Lanka for an extended period 1965–2018, and also for the post-reform period 1977–2018. The inflationary impacts of public debt, budget deficit and public expenditure, in association with money growth and a few control variables are assessed rigorously. The results of the vector autoregression model estimations involving different combinations of these variables reveal that fiscal policy instruments are, in general, inflationary while no significant impact of money growth on inflation is found. Besides, the analyses of impulse response functions and variance decomposition of inflation corroborate that the external debt, budget deficit and public expenditure are potent factors amplifying inflation. The impacts of money and control variables are trivial. These findings are invariant over the samples and justify that an active fiscal and a passive monetary policy are operative. Managing the budget deficit and growing dependence on public debt is a critical policy issue in Sri Lanka. JEL Codes: E31, H63, C32

show abstract

Section: Introductionmentioning

confidence: 99%

Potency of Fiscal Variables in Inf lation Variations in Sri Lanka

Maitra

Mondal

2022

The Indian Economic Journal

View full text Add to dashboard Cite

show abstract

“…In this study, the univariate ARIMA(1, 1, 2) was selected as the best model using adjusted R-squared statistics. (Bandara, 2011)used the VAR models to forecast the inflation rate of Sri Lanka using the monthly mean historical data . (Jere, 2016) used the univariate time series models to forecast the inflation rate of Zambia by using Holt's Exponential Smoothing.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating the Efficacy of Supervised Machine Learning Models in Inflation Forecasting in Sri Lanka

Bandara,

De Mel

2024

Am. J. Appl. Stat. Econ.

View full text Add to dashboard Cite

This study aims to forecast the inflation rate using supervised machine learning models (SMLM). While SMLMs are widely used in various fields, they have not been widely applied in forecasting inflation rates. Therefore, the main objective of this study is to identify the best model for forecasting inflation among four different SMLMs: LASSO regression (LR), Bayesian Ridge Regression (BRR), Support Vector Machine Regression (SVR), and Random Forest Regression (RFR) models. To achieve this objective, two different types of cross-validation techniques were employed: The k-fold cross-validation method (CVK) and walk forward validation (WFV) methods. These techniques were used to estimate the parameters and hyper-parameters for each machine learning model with root mean square error. The mean absolute percentage error (MAPE) was used to compare the performance of the different SMLMs. Empirical evidence from Sri Lanka between 1988 and 2021 was used to test the performance of the SMLMs in forecasting inflation rates. The results show that the SVR model with walk-forward validation is the best method for forecasting the future inflation rate of Sri Lanka based on the MAPE value. Overall, this study showcases the effectiveness of Supervised Machine Learning Models (SMLMs) in forecasting inflation rates, emphasizing the critical role of precise cross-validation techniques. These findings are invaluable for policymakers and investors, offering advanced tools for more informed economic decision-making and highlighting the potential of machine learning in enhancing macroeconomic stability and forecasting accuracy.

show abstract

“…Based on several research findings concerning the factors influencing inflation, the following are identified as follows. Bandara (2011) shows the factors influencing inflation in Sri Lanka from 1993 to 2008 using VAR models and the Granger causality method. The findings show that money supply, exchange rate and GDP exhibit significant information to explain the inflationary trends in Sri Lanka.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating Factors Influencing Inflation in the USA

COSKUN YILMAZ

2023

Ekinoks

View full text Add to dashboard Cite

The determination of inflation has been extensively explored in economic literature and it remains an ongoing and developing field of study. Researchers have examined diverse econometric methodologies and analyzed data from varying time spans to identify the factors influencing inflation. In this research paper, we explore the factors influencing inflation in the USA by utilizing the monthly data from 2002 to 2020. To achieve our aim, we employ error correction model (ECM) and then granger causality method. The findings indicate that certain lagged changes in inflation, consumer price index (CPI) for fuel and utilities, CPI all city average and production price index play a significant role in accounting for the variations in inflation in the USA but the lagged changes in money supply are not a significant determinant in accounting for the fluctuations in inflation in the USA. Moreover, the results of causality analysis suggest that all factors have significant Granger causality on inflation, except money supply.

show abstract

The Determinants of Inflation in Sri Lanka

Cited by 10 publications

References 20 publications

Potency of Fiscal Variables in Inf lation Variations in Sri Lanka

Potency of Fiscal Variables in Inf lation Variations in Sri Lanka

Evaluating the Efficacy of Supervised Machine Learning Models in Inflation Forecasting in Sri Lanka

Investigating Factors Influencing Inflation in the USA

Contact Info

Product

Resources

About