Monthly Inflation Research Articles

An empirical investigation of Bangladesh’s inflation dynamics: evaluating persistence and identifying structural breaks

We quantify two hypotheses: the “Friedman-Ball Hypothesis” and the “Cukierman-Meltzer Hypothesis,” in the context of Bangladesh. The first posits that an increase in inflation leads to greater inflation uncertainty, while the second asserts that inflation uncertainty causes an increase in inflation. Using monthly inflation data from June 1997 to July 2021, we find evidence supporting both hypotheses. Our analysis of inflation persistence and structural breaks reveals that inflation in Bangladesh exhibits high persistence throughout the entire sample period, but there is a decrease in persistence in two of the sub-samples after the breaks in March 2007 and April 2012. Moreover, Bangladesh’s inflation persistence is comparable to, and at times even lower than, that of its South and Southeast Asian neighboring countries. As policy implications of our findings, we emphasize the need for a more nuanced approach to monetary policy, including proactive interest rate management, implementation of a Medium-Term Fiscal Framework, and the development of a flexible monetary policy strategy tailored to Bangladesh’s unique economic circumstances.

IMPACTOS ECONÔMICO-FINANCEIROS DA PANDEMIA NOS NEGÓCIOS NO BRASIL

This article aims to analyze the economic and financial impacts of the pandemic on the number of opening and closing businesses in Brazil. For the development of this study, the data was collected from the Ministry of Economy on the history of companies that opened and closed from 2010 to 2020; monthly sales for 2019 and 2020 with NF-e published by the Internal Revenue Service; IBGE fortnightly survey on the effects of the pandemic on sales in June to August 2020. In addition, an analysis of the relationship between the monthly flow of companies closed in 2020 and the monthly inflation index was carried out. The results point to an increase in the opening of companies “out of necessity” in periods of economic crisis; decrease in NF-e sales in the third quarter, followed by growth from the second half of the year; the greater negative impact of the pandemic on companies with few employees and linked to Construction and Commerce. The analysis of the relationship between the monthly flow of the companies closed in 2020 and the monthly inflation rate was low (r = 0,4231).

Inflation forecasting using autoregressive distributed lag (ARDL) models

This study attempts to evaluate and compare the inflation-predicting performance of several ARDL models. Since there was no cointegration, the ARDL model does not employ an error correction term. Subsequently, model development showed that ARDL(2,2) should be used. Besides the formally developed model, some other more arbitrarily chosen ARDL models were also included, i.e., ARDL(1,1), ARDL(2,0), ARDL(1,0), ARDL(0,1), and ARDL(0,2). This research measures forecasting performance with inflation as the forecasting object. The duration of the monthly inflation statistics ranged from January 2011 to July 2022. The data were separated into two categories. The training data ranged between January 2011 and December 2021. After getting the appropriate parameters from the training data, the models generated projections from January 2022 to July 2022. The research determined that ARDL (1,0) was the most accurate inflation forecasting model, followed by ARDL (0,2) and formally constructed ARDL(2,2) finished in fourth place. This study suggests that the formal development of ARDL for forecasting purposes is unnecessary. Formal ARDL development is more appropriate for root cause analysis. In addition, the single autoregressive component indicates that most of the inflation value's information originated from the prior period. This suggests that the previous period's value is Indonesia's most significant predictor of inflation. The impact of greater period lags on inflation forecasting diminishes immediately.

Impact of COVID-19 and Russia-Ukraine war on the inflation rate of South and Southeast Asia

The financial value of money and the overall stability of an economy are significantly impacted by inflation. To investigate the effect of the COVID-19 pandemic and the Russia-Ukraine war on constant inflation rates in South and Southeast Asian countries between January 2019 and December 2022, this research utilizes monthly inflation rate data from the International Monetary Fund. Autoregressive integrated moving average modeling and prediction of inflation rates and the Bai-Perron method to estimate the structural breakdown of monthly inflation rate series are employed. Graphical methods and theoretical tests are used to diagnose the models, and the Chow test is utilized to assess structural breakdown in the monthly inflation rate series. The findings indicate that the Russia-Ukraine war since February 2022 has a more significant impact on the selected countries than the COVID-19 pandemic during January 2020 to December 2021.
 Dhaka Univ. J. Sci. 71(2): 169-175, 2023 (July)

Inflation Rate Prediction based on SARIMA-RF

Accurate prediction of the inflation rate can help governments prevent social problems caused by inflation, maintain social stability, and is crucial for the economy and society. Considering the nonlinear characteristics of China's inflation rate series, this paper uses the month-on-month Consumer Price Index (CPI) data from January 2011 to March 2023 to measure the inflation rate in China. Firstly, the monthly inflation rate series from January 2011 to December 2021 is analyzed to identify significant and complex seasonal effects. Therefore, suitable SARIMA models and Random Forest (RF) models are separately constructed. In order to better capture the nonlinear features of the inflation rate series, this paper proposes a SARIMA-RF fusion model based on simple linear weighting. Using monthly data from December 2022 to March 2023, the mean absolute error and root mean square error are used as evaluation metrics for forecasting errors. The results indicate that the prediction error of the SARIMA-RF fusion model is significantly lower than that of the SARIMA model and the RF model. The proposed model can more effectively extract the nonlinear features of China's inflation rate series and accurately predict its changes. It has valuable implications for the government, society, and individuals in China.

Time Series Analysis and Forecasting of Household Products’ Prices (A Case Study of Nyeri County)

Inflation has a significant impact on both consumable and non-consumable products and plays a critical role in determining the cost of living. The study aimed to investigate the trend of household consumable and non-consumable prices over the past three years and identify the best ARIMA model for future price predictions. The results showed that consumable goods played a greater role in determining the national inflation compared to non-consumable goods. A relationship was found between the changes in local-level prices and national monthly inflation rates, with consumable goods being fitted to an ARIMA (1,2,2) model and national inflation rates to ARIMA (3,1,0). Non-consumable goods were found to be a white noise. The models were found to be adequate in forecasting changes in prices, with their validity confirmed by the Box-Ljung test and autocorrelation coefficients of model residuals. This study demonstrated the importance of analyzing changes in products’ prices at a local level and how it affects the national inflation rate. In future, similar studies can be carried out in different counties and with a more comprehensive model to investigate the impact of the COVID-19 pandemic on the prices of household consumable and non-consumable goods at the local level.

Testing the Response of the Annual Well-Being Index to Monthly Inflation in Iran: An Application of a Mixed-Data Sampling (MIDAS) Approach

Testing the Response of the Annual Well-Being Index to Monthly Inflation in Iran: An Application of a Mixed-Data Sampling (MIDAS) Approach

FORECASTING CONSUMER PRICE INDEX AND EXCHANGE RATE USING ARIMA MODELS: EMPIRICAL EVIDENCE FROM NIGERIA

Considering the high level of uncertainty in the foreign exchange market and the adverse effects of inflation in Nigeria, the need to utilize current data to work on appropriate models capable of predicting the future values of exchange rates and CPI has become necessary to guide monetary policy makers. This paper applies the techniques of Autoregressive Integrated Moving Average models to forecast the CPI and exchange rates of Nigeria using the dataset of monthly CPI and Exchange rates of naira against US Dollar from January 2010 to August 2022 obtained from National Bureau of Statistics. After the original series were appropriately differenced to attain stationarity, autocorrelation function (ACF) and partial autocorrelation function (PACF) were used to select a number of tentative models for parameter estimation. Based on selection criteria such as AIC, SBIC, HQC, R2 and Durbin Watson Statistics, ARIMA(1, 2, 0) was chosen as the best model for forecasting Nigeria’s monthly Inflation (CPI) and ARIMA(1, 1, 1) was selected as the most ideal model for forecasting monthly foreign exchange rates of Nigeria. The portmanteau tests carried out show that the residuals from both models are white noise which further confirms the adequacy of the fitted models. The results reveal that both inflation and Exchange rates of Naira against the Dollar will continue to rise. However, the rise in exchange rates for the short time is relatively steady. The findings from this study will furnish the monetary and policy makers with necessary information needed to reverse the expected trend

The Impact of Fuel Prices Increasing on Inflation in South Sulawesi using Pulse Function Intervention Analysis

South Sulawesi Province is considered capable of controlling inflation, as seen from its not very large volatility. However, this does not mean that the increase in fuel prices does not impact inflation in South Sulawesi. This study aims to obtain the best model to analyze the impact of rising fuel prices on inflation in South Sulawesi using Pulse Function Intervention Analysis and to find out how significant the impact of rising fuel prices on inflation in South Sulawesi is. The data in this study were obtained from the Central Statistics Agency (BPS) of South Sulawesi Province, namely the monthly inflation of South Sulawesi Province for the period January 2014 – September 2022, with a total of 105 observations. The best model in this study is the MA ([12]) bbm1, bbm5, and bbm6 models with AIC 104.09. Statistical test results show that 3 of 6 times increases in fuel prices since 2014 still influence the rising inflation, namely in November 2014, April 2022, and September 2022. The increase in fuel prices in 2014 impacted rising inflation by 1.97 percent, while in 2022, it increased inflation by 0.92 and 0.99 percent.

Estimating Indonesian Complete Life Table and Fair Annual Pure Premium Range from Abridged Life Table with Bayesian Method and Bootstrapping

Several Indonesian life insurance companies recently faced financial problems due to inadequate pricing and idealistic investment expectation. Growing market and insurtech implementation might lead to worse conditions in the future. The current mortality table and investment return assumption are too ideal, so more conservative assumptions are required to get a more reasonable annual pure premium range. This research estimated complete life tables from abridged life tables by truncated Heligman-Pollard and Makeham model, when a lognormal stochastic process estimated annual investment return. Parameters for mortality models and return distribution are estimated using Bayesian method with Metropolis-Hasting’s algorithm. Data from the abridged life table was bootstrapped due to insufficient number for statistical parametric modeling. Good accuracy for estimated abridged mortality rates was reached by referring to the Mean Absolute Percentage Error (MAPE) metric for both males and females, also for the young ages group (new-born to twenty-nine years old) and old ages group (thirty to eighty-four years old). The parameters were satisfactory to estimate the complete life table and extrapolate annual mortality rates calculation until age ninety-nine. A log-normal distribution was found to fit the monthly inflation rates satisfactorily. Assuming that investment return is close to the inflation rates, the annual investment return is anticipated for both profitable and losing situations. Therefore, insurance companies can win the customers’ decisions without compromising their financial stability.

Economic Activity Forecasting Based on the Sentiment Analysis of News

The outbreak of war and the earlier and ongoing COVID-19 pandemic determined the need for real-time monitoring of economic activity. The economic activity of a country can be defined in different ways. Most often, the country’s economic activity is characterized by various indicators such as the gross domestic product, the level of employment or unemployment of the population, the price level in the country, inflation, and other frequently used economic indicators. The most popular were the gross domestic product (GDP) and industrial production. However, such traditional tools have started to decline in modern times (as the timely knowledge of information becomes a critical factor in decision making in a rapidly changing environment) as they are published with significant delays. This work aims to use the information in the Lithuanian mass media and machine learning methods to assess whether these data can be used to assess economic activity. The aim of using these data is to determine the correlation between the usual indicators of economic activity assessment and media sentiments and to forecast traditional indicators. When evaluating consumer confidence, it is observed that the forecasting of this economic activity indicator is better based on the general index of negative sentiment (comparisons with univariate time series). In this case, the average absolute percentage error is 1.3% lower. However, if all sentiments are included in the forecasting instead of the best one, the forecasting is worse and in this case the MAPE is 5.9% higher. It is noticeable that forecasting the monthly and annual inflation rate is thus best when the overall negative sentiment is used. The MAPE of the monthly inflation rate is as much as8.5% lower, while the MAPE of the annual inflation rate is 1.5% lower.

THE FORECASTING OF MONTHLY INFLATION IN YOGYAKARTA CITY USES AN EXPONENTIAL SMOOTHING-STATE SPACE MODEL

Yogyakarta is known as a student city, tourist city, and also a city of culture. Yogyakarta is an interesting tourist and cultural place with many beautiful tourist attractions in the city of Yogyakarta. Public transportation in the city of Yogyakarta is also varied, ranging from conventional and online-based. Access to the city of Yogyakarta varies, namely trains, buses, and planes. Thus, the economic growth in the city of Yogyakarta is getting better, this can be seen from the economic activity in the city of Yogyakarta which is getting busier. A good economy is usually always followed by stable inflation. This study aims to predict inflation in the future period using the Exponential Smoothing-State Space (ETS) model. Secondary monthly inflation data was obtained from BPS Yogyakarta City. From this research, the Exponential Smoothing-State Space Model / ETS (A, N, A) is obtained, which means that the monthly inflation data for the city of Yogyakarta does not contain trends, but contains additive seasonality and has additive errors. The results of this study indicate that inflation in the next three months is relatively stable, namely, the decline in inflation and the increase in inflation is still below 10%. Keywords: BPS Yogyakarta City, Monthly Inflation Forecasting, Exponential Smoothing-State Space ETS

A study of inflation persistence in Kazakhstan: what has changed?

ABSTRACT The paper analyzes the persistence properties of the monthly inflation and its components in Kazakhstan using a fractionally integrated approach. The results suggest the presence of long memory behaviour in the inflation series. The general inflation, food inflation, and non-food inflation experienced a break in late 2015 when the National Bank of Kazakhstan announced a shift in the monetary policy regime. Non-food inflation is fractionally cointegrated with the nominal depreciation rate. The estimates of the Bayesian DSGE model reveal shifts in the sources of inflation persistence. The evidence for the effect of the policy change on inflation persistence and its sources is mixed. Finally, the estimation of the persistence parameter is sensitive to the choice of data frequency.

Inference in mixed causal and noncausal models with generalized Student’s t-distributions

The properties of Maximum Likelihood estimator in mixed causal and noncausal models with a generalized Student’s t error process are reviewed. Several known existing methods are typically not applicable in the heavy-tailed framework. To this end, a new approach to make inference on causal and noncausal parameters in finite sample sizes is proposed. It exploits the empirical variance of the generalized Student’s t, without the existence of population variance. Monte Carlo simulations show a good performance of the new variance construction for fat tail series. Finally, different existing approaches are compared using three empirical applications: the variation of daily COVID-19 deaths in Belgium, the monthly wheat prices, and the monthly inflation rate in Brazil.

Seeking Improved Yield: How to Make SEC Yield More Suitable for TIPS

Inconsistent application of the SEC Yield formula to calculate TIPS bond mutual fund and TIPS bond ETF yields can lead to misleading yield reporting. This article recommends two adjustments to make the SEC Yield calculation more suitable for comparing TIPS bond fund performance. First, apply the break-even inflation rate to re-express each TIPS bond’s real yield as an equivalent nominal yield. Second, use the nominal yield to maturity to calculate 30 days’ interest income, but do not add the monthly inflation adjustment to the TIPS bond’s principal amount.

ПРОГНОЗИРОВАНИЕ БАНКРОТСТВА СЕЛЬСКОХОЗЯЙСТВЕННЫХ ОРГАНИЗАЦИЙ: УЧЕТ ВЛИЯНИЯ МАКРОЭКОНОМИЧЕСКИХ ФАКТОРОВ

This paper discusses the impact of macroeconomic factors on the activities of agricultural organizations. In particular, this paper examines the issues of forecasting the bankruptcy of agricultural organizations in modern conditions (for example, sanctions) of the Russian Federation. In the course of their activities, agricultural organizations in modern conditions may face the problem of bankruptcy. In order to avoid such a problem, agricultural organizations take into account many factors in their activities. To achieve results in this work, the following indicators were analyzed: the structure of exports of the Russian Federation for January 2021–January 2022; the monthly inflation rate in 2022; the population of the Russian Federation for 2018-2021 and June 2022; the number of agricultural organizations by value for 2021. The analysis showed that agricultural organizations need to take into account macroeconomic indicators in their activities, since such factors can lead to bankruptcy

One‐ and two‐sided monitoring schemes for BINARCH(1) processes

AbstractIn this article we propose and study one‐ and two‐sided control charts for monitoring a first order binomial integer‐valued ARCH process. This model can be used for modeling integer‐valued time series of counts with a finite support, which also exhibit overdispersion. We consider Shewhart as well as exponentially weighted moving average control charts. The performance of both schemes is investigated for various shifts in process parameters, which result in an upward or a downward shift in process mean level. Practical guidelines concerning the statistical design of the proposed charts are given as well. The case of two‐sided control charts is also considered. Finally, we describe the implementation of the proposed schemes by using a real dataset about the monthly inflation of the Eurozone members.

Identification and comparison of characteristics of inflation rate in cities in Indonesia in the period of 2009–2014 with the period of 2014–2019

This study aims to identify the characteristics of the MoM inflation rate in cities in Indonesia. In addition, it also compares the distribution of the monthly inflation rate for the 2009–2014 period with the 2014-2019 period in cities in Indonesia. The method used to identify the characteristics of the MoM inflation rate is by using summary of statistics, while to compare the distribution of the monthly inflation rate for the 2009–2014 period and the 2014–2019 period, the two-sample Kolmogorov-Smirnov test is used. Characteristics of the month to month (MoM) inflation rate in cities in Indonesia for the period October 2009 to March 2019 was presented. Furthermore, the comparison of the characteristics of the MoM inflation rate from October 2009 to September 2014 (period 1) and the period from October 2014 to March 2019 (period 2) was also shown. The cities of Banda Aceh, Kendari, Yogyakarta, Jakarta and Sorong had an average characteristic of inflation each month which tended to be low in February, March, April and September.Six cities had significantly different characteristics during period 2 inflation rate compared to period 1.

THE FORECASTING OF MONTHLY INFLATION IN MALANG CITY USING AN AUTOREGRESSIVE INTEGRATED MOVING AVERAGE

Abstract: Malang is known as a student city since there are a lot of schools and universities that can be found in Malang Indonesia. Malang is also an attractive tourist place with many tourist attractions in the city of Malang. Public transportation in the city of Malang is also very varied, ranging from conventional and based online. Access to the city of Malang is varied, namely trains, buses, and planes. Thus economic growth in the city of Malang is getting better, this can be seen from the economic activity in the increasingly crowded city of Malang. A good economy is usually followed by stable inflation. For this reason, it is necessary to examine how the monthly inflation rate in Malang city. This study aims to forecast inflation in the coming periods using the Autoregressive Integrated Moving Average (ARIMA) model. Secondary monthly inflation data is obtained from BPS Malang. From this research, the ARIMA model (2,0,3) is obtained. The accuracy model is used in this research namely root means square error (RMSE), mean absolute error (MAE), and mean absolute square error (MASE). The accuracy value is RMSE equal 0.2645467, MAE equal 0.2013898, and MASE equal 0.6047399. Keywords: Monthly inflation forecasting, BPS Malang city, ARIMA model.

Прогнозирование валютной инфляции для анализа инфляционного риска финансовых вложений

The need for improving methods of financial investment analysis in order to reduce risks leads researchers to exploit the modern scientific advancements especially in IT domain. Financial analytics require the ability to model and forecast future value of investigated financial parameters like currency inflation. In this paper, we analyzed the monthly inflation rate of Kazakhstan currency, using historical data from 1995 to 2020 by applying wide spread statistical and machine learning methods. The results show that the proposed research approach generates a solid forecasting accuracy and can be proposed to be included into financial investment analysis methods that could reduce inflation risk.