Forecasting Inflation Rate Research Articles

Application of Extreme Learning Machine Algorithm (ELM) in Forecasting Inflation Rate in Indonesia

Application of Extreme Learning Machine Algorithm (ELM) in Forecasting Inflation Rate in Indonesia

Forecasting Inflation Rates in Zambia: Time Series Analysis with the Holt Model

Forecasting Inflation Rates in Zambia: Time Series Analysis with the Holt Model

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

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.

Improved Breitung and Roling estimator for mixed-frequency models with application to forecasting inflation rates

Instead of applying the commonly used parametric Almon or Beta lag distribution of MIDAS, Breitung and Roling (J Forecast 34:588–603, 2015) suggested a nonparametric smoothed least-squares shrinkage estimator (henceforth SLS1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${SLS}_{1}$$\\end{document}) for estimating mixed-frequency models. This SLS1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${SLS}_{1}$$\\end{document} approach ensures a flexible smooth trending lag distribution. However, even if the biasing parameter in SLS1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${SLS}_{1}$$\\end{document} solves the overparameterization problem, the cost is a decreased goodness-of-fit. Therefore, we suggest a modification of this shrinkage regression into a two-parameter smoothed least-squares estimator (SLS2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${SLS}_{2}$$\\end{document}). This estimator solves the overparameterization problem, and it has superior properties since it ensures that the orthogonality assumption between residuals and the predicted dependent variable holds, which leads to an increased goodness-of-fit. Our theoretical comparisons, supported by simulations, demonstrate that the increase in goodness-of-fit of the proposed two-parameter estimator also leads to a decrease in the mean square error of SLS2,\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${SLS}_{2},$$\\end{document} compared to that of SLS1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${SLS}_{1}$$\\end{document}. Empirical results, where the inflation rate is forecasted based on the oil returns, demonstrate that our proposed SLS2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${SLS}_{2}$$\\end{document} estimator for mixed-frequency models provides better estimates in terms of decreased MSE and improved R2, which in turn leads to better forecasts.

Forecasting Inflation Rates: A Large Panel Micro Data Approach

Forecasting Inflation Rates: A Large Panel Micro Data Approach

Modeling of cash flows from foreign economic activity: Scenario analysis

The prerequisite for the scientific research on this topic is the need to form an economic-mathematical model that would make it possible to predict and simulate the financial flow of the company to provide construction services in time. The aim of the scientific article is to carry out economic and mathematical modeling of the financial flow for the provision of services in time on the example of the project of the company “STROITEL12” JSC. In the demonstrated example, the situation was considered, in which the organization performed design work for 2 years and construction work for 4 years in a foreign country in the framework of the creation of the subway (station + track + station improvement) in Turkey, in the city of Izmir. According to Rosstat, the forecast inflation rate amounted to 5%. The implementation of this project led to the fact that the staff of “STROITEL12” received a reward for the completion of the project ahead of schedule. After the application of the economic-mathematical model in the formation of the financial flow, the employees of the company were more diligent to their work, as their salaries increased. The novelty of scientific research is the application of economic-mathematical model for modeling the cash flows from foreign economic activity of the organization.

Modelling and Forecasting Inflation Rates in Kenya Using ARIMA-ANN Hybrid Model

Modelling and Forecasting Inflation Rates in Kenya Using ARIMA-ANN Hybrid Model

Forecasting Inflation Rate in Ghana using Seasonal Autoregressive Integrated Moving Average Model with Monthly Consumer Price Index, 2012 -2022

This study aimed at modeling and forecasting the inflation rate in Ghana using a seasonal autoregressive integrated moving average model with monthly consumer price index data from January 2012 to December 2022. Using the Philip-Parron unit root test, the result showed that the time series data was stationary in its first difference, showing that the consumer price index was integrated with the first order. Also, from the seasonal graph, seasonality was observed in the data. The correlogram of ACF and PACF helped to select the appropriate lag for p and q. Box-Jenkins procedure was applied to identify the appropriate model that fit the data. From the Box-Jenkins procedure, the SARIMA(1,1, 1)(1,1,1)12 model was identified as the best model to forecast the inflation rate. From the forecast graph, inflation will begin to rise in the second quarter of 2023. However, the forecast from January to March,, 2023 inflation rates were 54.9, 56.5 and 50.2, respectively. Therefore, it is highly likely that Ghanaian inflation will be rising in the subsequent months based on the 2012 to 2022 Consumer Price Index. The appropriate authorities should put monetary and fiscal policy measures in place to moderate the envisaged rise in inflation.

Time-dependent shrinkage of time-varying parameter regression models

This article studies the time-varying parameter (TVP) regression model in which the regression coefficients are random walk latent states with time-dependent conditional variances. This TVP model is flexible to accommodate a wide variety of time variation patterns but requires effective shrinkage on the state variances to avoid over-fitting. A Bayesian shrinkage prior is proposed based on reparameterization that translates the variance shrinkage problem into a variable shrinkage one in a conditionally linear regression with fixed coefficients. The proposed prior allows strong shrinkage for the state variances while maintaining the flexibility to accommodate local signals. A Bayesian estimation method is developed that employs the ancilarity-sufficiency interweaving strategy to boost sampling efficiency. Simulation study and an empirical application to forecast inflation rate illustrate the benefits of the proposed approach.

Machine learning approaches for forecasting inflation: empirical evidence from Sri Lanka

The aim of this study is to forecast the inflation rate using supervised machine learning models (SMLM). While SMLMs are widely used in various fields, they have also been widely applied to forecast 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 used: K-fold cross-validation method (KCV) and walk forward validation ( ) methods. These techniques were used to estimate the parameters and hyper-parameters for each machine learning model with the aid of 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 LR 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 demonstrates the effectiveness of SMLMs in forecasting inflation rates under the critical conditions and highlights the importance of employing appropriate cross-validation techniques when using these SMLM models. The findings of this study can provide valuable insights for policymakers, investors, and researchers who are interested in forecasting inflation rates.

REFINED MATHEMATICAL MODEL OF ECONOMIC DYNAMICS UNDER HIGH INFLATION AND UNSTABLE DEVELOPMENT

Previously, the authors proposed two mathematical models that describe the dynamics of economic development rates and forecasting inflation rates. The events that took place after February 2022 introduced an element of significant turbulence into the processes of macroeconomic dynamics in Russia, which required some correction of the previous models. This is reflected in this article. The processes simulated on the basis of new models show that in order to restore economic growth and reduce inflation in the medium term, a significant reduction in the interest rate and a steady increase in the money supply are required. It is shown that in the current era of geopolitical changes and the collapse of many liberal market dogmas, the role of the state and planning in managing economic processes is increasing.

Forecasting Inflation Rate in the Philippines Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Model

The Philippines has experienced a long period of rapid economic growth. However, inflation has now become a major concern, with the Philippines currently experiencing high inflation. High inflation is a threat to strong economic growth, particularly to people's purchasing power. This study aims to predict the inflation rate in the Philippines using Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The historical data on the inflation rate of the Philippines, comprising 135 observations from January 2012 to March 2023, was obtained from the official website of the Philippine Statistic Authority (PSA). The study followed the Box Jenkins Methodology, which consists of 4 stages: Identification, Estimation, Diagnostic Checking, and Forecasting to develop a SARIMA model. It was then revealed that the best SARIMA model for predicting the inflation rate in the Philippines is SARIMA (1,1,1) (0,0,1)_12 based on the following criteria: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Hannan-Quinn Information Criterion (HQIC), σ^2, and the Highest Log Likelihood. This chosen model has a Mean Absolute Percentage Error (MAPE) of 8.17% which is within the acceptable range of 25%. This shows an indication that the forecast is acceptably accurate. The best SARIMA model was then used to forecast the inflation rate in the Philippines from April 2023 to March 2024, and based on the result of the forecasting, it appears that the inflation rate in the Philippines is expected to decline gradually over the next 12 months, from 6.93% in April 2023 to 4.85% in March 2024. These findings could provide insights that the government can use to make decisions about monetary policies and help the economy of the Philippines to improve.

Modelling and Forecasting Inflation Rates in Kenya Using ARIMA Model

This research aimed to develop an ARIMA(1,0,11) model for forecasting inflation rates in Kenya. The research utilized historical inflation data from January 2005 to August 2022 to develop the model and evaluated its performance on a test set spanning from September 2022 to August 2023. The results demonstrated that the ARIMA model provided accurate forecasts, with low forecast errors in terms of MSE, RMSE, MAE, and MAPE. These forecasts have practical utility for various stakeholders, including policymakers, businesses, and financial institutions, as they can use the information to inform pricing strategies, interest rate policies, and other economic decisions. Additionally, the study highlighted the importance of data quality, continuous monitoring of economic factors, and periodic model refinement to ensure the effectiveness of inflation forecasting in a dynamic economic environment.

Forecasting inflation rates be extreme gradient boosting with the genetic algorithm

Forecasting inflation rates be extreme gradient boosting with the genetic algorithm

PERBANDINGAN METODE ANN BACKPROPAGATION DAN ARMA UNTUK PERAMALAN INFLASI DI INDONESIA

A country's development progress can be measured by good economic growth. If economic growth experiences rapid growth, it will usually trigger price increases. The occurrence of an uncontrolled increase in the price of goods or services for the needs of the community can cause inflation. inflation rate for a country is an inflation rate that has a low and stable value. One alternative is to provide an overview of the inflation in Indonesia by using forecasting analysis techniques. In this study, inflation forecasting analysis in Indonesia was carried out using the ANN Backpropagation and ARMA methods. The purpose of this research is to compare the performance results of the two methods and look at the best method for forecasting results. Based on the results of the analysis with the ANN Backpropagation method, the best network architecture model was ANN(7-4-1) using an epoch value of 400 and a learning rate of 0,1 with a value of MSE = 0,0112 and RMSE = 0,1065. While the results of the analysis using the ARMA method, the best model was obtained, namely ARMA(2,0,1) with the value MSE = 0,0648 and RMSE = 0,2545. So that the most optimal method used to predict inflation for the next period is the ANN Backpropagation method because it has a smaller error value. From this model, the results of forecasting inflation rates for the months of May to December 2022 are also obtained with a range of 0,01% to 0,5%.

Application of Machine Learning Algorithms for Sustainable Business Management Based on Macro-Economic Data: Supervised Learning Techniques Approach

Macroeconomic indicators are the key to success in the development of any country and are very much important for the overall economy of any country in the world. In the past, researchers used the traditional methods of regression for estimating macroeconomic variables. However, the advent of efficient machine learning (ML) methods has led to the improvement of intelligent mechanisms for solving time series forecasting problems of various economies around the globe. This study focuses on forecasting the data of the inflation rate and the exchange rate of Pakistan from January 1989 to December 2020. In this study, we used different ML algorithms like k-nearest neighbor (KNN), polynomial regression, artificial neural networks (ANNs), and support vector machine (SVM). The data set was split into two sets: the training set consisted of data from January 1989 to December 2018 for the training of machine algorithms, and the remaining data from January 2019 to December 2020 were used as a test set for ML testing. To find the accuracy of the algorithms used in the study, we used root mean square error (RMSE) and mean absolute error (MAE). The experimental results showed that ANNs archives the least RMSE and MAE compared to all the other algorithms used in the study. While using the ML method for analyzing and forecasting inflation rates based on error prediction, the test set showed that the polynomial regression (degree 1) and ANN methods outperformed SVM and KNN. However, on the other hand, forecasting the exchange rate, SVM RBF outperformed KNN, polynomial regression, and ANNs.

Forecasting Inflation Rate in Padang City Using Backpropagation Method

Desire to maximize economic growth in the Padang City, so it need to know the minimum estimate of inflation, that matter is done so that can anticipate the value of inflation outside the desired rate. This research was conducted to obtain forecasting inflation rates in the Padang City using the Backpropagation method. The data of this research is secondary data. Data obtained through the West Sumatra BPS website about inflation of Padang City in January 2013-December 2019. The results of research obtained a forecasting inflation rates in the Padang City with MSE value is 0.010689.

Structural Breaks in Grouped Heterogeneity

Generating accurate forecasts in the presence of structural breaks requires careful management of bias-variance tradeoffs. Forecasting panel data under breaks offers the possibility to reduce parameter estimation error without inducing any bias if there exists a regime-specific pattern of grouped heterogeneity. To this end, we develop a new Bayesian methodology to estimate and formally test panel regression models in the presence of multiple breaks and unobserved regime-specific grouped heterogeneity. In an empirical application to forecasting inflation rates across 20 U.S. industries, our method generates significantly more accurate forecasts relative to a range of popular methods.

Forecasting inflation rates with multi-level international dependence

We analyze inflation rates in high-income OECD countries employing a multi-level factor structure that is estimated based on canonical correlation analysis (CCA) and sequential least squares (SLS). We show that inflation has a global component, mainly driven by G7 countries, explaining 77% of the variation on average, and a local component that accounts for substantial comovements in a subset of the countries. We demonstrate that this combination of global and local components has outstanding predictive ability, and can improve forecast performance significantly over a global-component-only specification for different policy horizons thus constituting a new benchmark for inflation forecasting.

An Analysis on Forecasting Inflation Rate in the Philippines: A Recurrent Neural Network Method Approach

Inflation rate is the proceeding rise within the common level of costs of products and services in an economy over a certain span of time. In 2018, the Philippines has the highest inflation rate among the 10 South East Asian countries. The objective of this research is to forecast the inflation rate of the Philippines for the next five years (2019-2023). Also, the researchers compared the results obtained from the Multiple Linear Regression and Recurrent Neural Network (RNN) performed in MATLAB to determine which of these two models will be the better model in forecasting inflation rate. In this study, the researchers observed the behavior of the Inflation Rate(y) and its economic factors such as Import (x1), Export (x2), Money Supply (x3), Gross Domestic Product (x4), Gross National Product (x5), Expenditure (x6) and Exchange Rate (x7). Using Multiple Linear Regression, this study determined that the significant predictors are Money Supply (x3) and Expenditures (x6). By evaluating the forecast efficiency of the two methods, the researchers concluded that Multilayered Recurrent Neural Network outperforms Multiple Linear Regression in predicting inflation rate of the Philippines. This paper can be useful to the Philippine Government on their decisions about monetary policy making since forecasting the inflation rate has a huge importance and impact in conducting monetary policy.