The Inflation Forecasting of Major Cities In East Kalimantan: A Comparison Of Holt-Winters And SARIMA Model

This research aims to compare the performance of Holt Winters and Seasonal Autoregressive Integrate Moving Average (SARIMA) models in predicting inflation in Balikpapan and Samarinda, two biggest cities in East Kalimantan province. The importance of East Kalimantan province cannot be overstated since it has been declared as the venue for the capital of Indonesia. Hence, inflation prediction of the two cities will give valuable insights about the economic nature of the province for the country’s new capital. The data used in this study extended from January 2015 to September 2021. The data were divided into training and test data. The training data were used to model the time series equation using Holt winters and SARIMA models. Later, the models derived from training data were employed to produce forecasts. The forecasts were compared to the actual inflation data to determine the appropriate model for forecasting. Test data were from January 2015 to December 2020 and test data extended from January 2021 to September 2021. The result showed that Holt-Winters performed better than SARIMA in prediction inflation. The Root Mean Squared Error (RMSE) values are lower for Holt-Winters Exponential Smoothing for both cities. It also predicts better timing of cyclicality than SARIMA model.

Whose Inflation Expectations Best Predict Inflation?

We examine the predictive relationship between various measures of inflation expectations and future inflation. We find that the expectations of professional economists and of businesses have tended to provide more accurate predictions of future inflation than the expectations of households and of financial market participants. However, the forecasts coming from a relatively simple and popular benchmark inflation forecasting model have historically been roughly as accurate as the expectations of businesses and professional economists.

Inflation Forecasting for East Kalimantan Province Using Hybrid Singular Spectrum Analysis- Autoregressive Integrated Moving Average Model

The Singular Spectrum Analysis (SSA)-Autoregressive Integrated Moving Average (ARIMA) hybrid method is a good combination of forecasting methods to improve forecasting accuracy and is suitable for economic data that tends to have trend and seasonal patterns, one of which is inflation data. The purpose of this study is to obtain the results of inflation forecasting for East Kalimantan Province in 2021 using the SSA-ARIMA hybrid model. The results of the inflation forecasting for East Kalimantan Province in 2021 using the SSA-ARIMA(1,1,1) hybrid model overall experienced an increase and the highest inflation in 2021 occurred in December of 0.92% with a forecasting accuracy level based on the Root Mean Square Error (RMSE) was 0.069399 and Mean Absolute Percentage Error (MAPE) was 32.61084%

Evaluation of the Inflation Forecasting Process of the Reserve Bank of India: A Text Analysis Approach

This study attempts to evaluate the monetary policy of the Reserve Bank of India (RBI) based on an investigation of the policy statements. The analysis based on text mining of the central bank’s monetary policy statements seeks to unravel the information considered by the central bank and the processes followed in making its inflation forecasts. The findings indicate that although the RBI examined high-frequency economic indicators, its inflation forecasts have generally been off the mark. Specifically, the monetary policy committee failed to foresee the sharp disinflation that followed the demonetization announced on November 8, 2016. This failure resulted in a high real interest rate regime that dealt a blow to the economy staggering under the effects of demonetization. Our research findings show that the monetary policy governance practices need to be refined and better aligned to economic realities, particularly under the RBI’s new monetary policy framework.

ТHE METHODOLOGY FOR INFLATION’ FORECASTING BY THE BANK OF RUSSIA IN THE MEDIUM TERM

The paper sets out to examine approaches to the forecasting of inflation by a macro market regulator. Various approaches to short-term inflation forecasting, inflation factors and their main channels of influence used by bank regulators in various countries are studied. The shortcomings of the used models for predicting inflation in the post-pandemic economy have been formulated. A comparative analysis of the use of various models has been conducted and solutions for building forecasting models in the medium term have been proposed. The approach has been tested for regional inflation forecasting; calculations of the indicators using VAR model, SARIMA, and dynamic method have been presented. It is proposed to use extended combined VAR models supplemented with exogenous factors for medium-term forecasting.

Forecasting Inflation in Latin American Countries Using a SARIMA-LSTM Combination

Inflation forecasting has been and continues to be an important issue for the world's economies. Governments, through their central banks, watch closely inflation indicators to make national decisions and policies. Controlling growth and contraction requires governments to keep a close eye on the rate of inflation. When planning strategic national investments, governments attempt to forecast inflation over longer periods of time. Getting the inflation forecast wrong, can result in significant economic hardships. However, even given its significance, there is limited new research that applies updated methodologies to forecast it, and even fewer studies in emerging economies where inflation may be drastically higher. This study proposes to forecast the inflation rate in emerging economies based on the commonly used Seasonal Autoregressive Integrated Moving Average (SARIMA) approach combined with Long Short Term Memory (LSTM). The results indicate that the proposed model based on the combination of SARIMA and LSTM, have a higher accuracy in inflation forecasts as measured by the Mean Square Error (MSE) of the proposed models over the SARIMA model and LSTM alone. The loss function used is Mean Squared Error (MSE), and the Model Confidence Set (MCS) is used to test the superiority of the models in the economies of Mexico, Colombia and Peru.

Inflation forecasting in an emerging economy: selecting variables with machine learning algorithms

PurposeThe main purpose of this study is to forecast inflation rates in the case of the Turkish economy with shrinkage methods of machine learning algorithms.Design/methodology/approachThis paper compares the predictive ability of a set of machine learning techniques (ridge, lasso, ada lasso and elastic net) and a group of benchmark specifications (autoregressive integrated moving average (ARIMA) and multivariate vector autoregression (VAR) models) on the extensive dataset.FindingsResults suggest that shrinkage methods perform better for variable selection. It is also seen that lasso and elastic net algorithms outperform conventional econometric methods in the case of Turkish inflation. These algorithms choose the energy production variables, construction-sector measure, reel effective exchange rate and money market indicators as the most relevant variables for inflation forecasting.Originality/valueTurkish economy that is a typical emerging country has experienced two digit and high volatile inflation regime starting with the year 2017. This study contributes to the literature by introducing the machine learning techniques to forecast inflation in the Turkish economy. The study also compares the relative performance of machine learning techniques and different conventional methods to predict inflation in the Turkish economy and provide the empirical methodology offering the best predictive performance among their counterparts.

Artificial Neural Networks in Inflation Forecasting at the Meso-Level

The aim of the article is to analyze inflation factors and their influence on the consumer price index in the regions. The article discusses the existing mathematical models for the forecasting of the regional inflation rate as an important national measure. Advantages, disadvantages and application fields of these models are presented. The appropriateness of recurrent neural network use for regional inflation forecasting is demonstrated. The article describes the process of neural network architecture creation, its training, inflation parameter forecasting.