Monthly Consumer Price Index Research Articles

FNNGM: A neural-driven fractional-derivative multivariate fusion model for interpretable real-time CPI forecasts

Integrating models from diverse sources has attracted substantial interest in developing advanced time series forecasting technologies. However, current research lacks a comprehensive and deep fusion model to integrate multiple forecasting methodologies. To this end, this paper proposes a neural-driven fractional-derivative multivariate fusion model (FNNGM (p, n)) to assimilate the fractional-derivative dynamical system, the driving factor in grey multivariate models, and the neural network into a cohesive framework. Consequently, this fusion architecture can benefit from the synergy of the target system's dynamics, extensive exogenous information, and non-linear transformation. Additionally, FNNGM (p, n) fosters extra functionalities through its inherent memory layer and sequence decomposition, bolstering model interpretability with the visible memory mechanism and understandable model workflows. To showcase the utility of FNNGM (p, n), this paper conducts real-time monthly consumer price index (CPI) forecasts that span ten years (from 2013:08 to 2023:07), analyzing the interpretable results from FNNGM (p, n) and contrasting it against many prevailing benchmark models. The comparison results reveal FNNGM (p, n)’s highly concentrated error distributions and the minimum mean absolute percentage forecasting error (APFE), squared forecasting error (SFE), and absolute forecasting error (AFE) values of 0.22 %, 0.59, and 0.56, respectively. Furthermore, the ablation experiments are performed to explore the specific effects and compatibilities of the fusion components, validating the effectiveness of the proposed fusion approach.

Application of a single-equation SARIMA model for short-term conditional forecast (projection) of CPI price dynamics in Poland

The aim of the article is to construct an optimal SARIMA model for short-term conditional forecasting (projection) of the dynamics of prices expressed by the Consumer Price Index (CPI), as understood within the extended Box-Jenkins procedure. The construction of such a forecast aims to influence, through the expectations channel, the institutional trust of society in monetary authorities and assess the effectiveness of achieving the monetary policy goal within the framework of the democratic responsibility of the decision-making body of the National Bank of Poland – the Monetary Policy Council. The selection of the optimal SARIMA model was carried out using an iterative method within the Box-Jenkins procedure, with the goal of reducing the systematic bias of estimators – coherence with empirical data. The analysis was conducted on compiled secondary data of the monthly Consumer Price Index for goods and services from the Central Statistical Office for the years 2010-2023 (on a monthly basis). Results show that the short-term forecast demonstrated accuracy within a specified confidence interval. The application of the SARIMA model serves as a useful methodological tool for constructing elaborate DSGE models (for example, the NECMOD model) using procedures such as SEM (System for Averaging Models) from Norges Bank.

Analisis Pengaruh Indeks Harga Konsumen Bulanan Terhadap Inflasi Makanan Bulanan melalui Metode Analisis Regresis Sederhana

Economic stability created price stability, because a stable economy could result in low or affordable costs for the community. One indication of economic stability was inflation. Meanwhile, the Consumer Price Index was an important indicator of the financial market and was a general indicator of the level of inflation in Indonesia. For this reason, this research aimed to analyze the influence of the monthly consumer price index on monthly food inflation in Cilacap Regency. The data used in the research came from the Cilacap Regency Central Statistics Agency, which was then analyzed using a simple regression analysis method. The results of the analysis showed that there was no significant influence between the monthly consumer price index and monthly inflation. This was because the CPI was one of the indicators was used to measure the level of inflation. Changes in the CPI over time described the rate of increase (inflation) or rate of decline (deflation) of goods and services.

Cryptocurrency as an alternative inflation hedge?

AbstractWe examine the association of Bitcoin, and other cryptocurrency, returns with changes in inflation expectations, and form a comparison with gold, a traditional inflation hedge. We control for uncertainty in economic policy, cryptocurrency, and financial markets, and show that cryptocurrency returns are positively related to changes in US inflation expectations only for a limited set of circumstances. Unlike with gold, the identified relationship is only significant for short‐term inflation expectations, and when inflation or market‐implied inflation expectations are below 2% (the Fed's inflation target). Moreover, cryptocurrency returns tend to be lower on days with monthly consumer price index (CPI) announcements and respond negatively to CPI surprises. Our results suggest that cryptocurrencies do not currently offer investors a viable alternative to gold for hedging inflation.

Costs of Implementing Teen Dating Violence and Youth Violence Prevention Strategies: Evidence From 5 CDC-Funded Local Health Departments.

In 2016, the Centers for Disease Control and Prevention supported 5 local health departments (LHDs) to implement teen dating violence and youth violence primary prevention strategies across multiple levels of the social-ecological model and build capacity for the expansion of such prevention efforts at the local level. The objective of this study was to estimate the total cost of implementing primary prevention strategies for all LHDs across 3 years of program implementation. We used a microcosting analytic approach to identify resources and compute costs for all prevention strategies implemented by LHDs. We computed the total program cost, total and average cost per strategy by social-ecological model level, and average cost of implementation per participant served by the program. All costs were inflated via the monthly Consumer Price Index and reported in August 2020 dollars. For 3 years of program implementation, the total estimated cost of implementing teen dating violence and youth violence primary prevention strategies was >$7.1 million across all 5 LHDs. The largest shares of program-related costs were program staff (55.9%-57.0%) and contracts (22.4%-25.5%). Among prevention strategies, the largest share of total costs was for strategies implemented at the community level of the social-ecological model (42.8%). The findings from this analysis provide a first look at the total costs of implementing comprehensive teen dating violence and youth violence primary prevention strategies and serve as a foundation for investments in local violence prevention funding for young people.

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.

Forecasting CPI of restaurants and hotels in Korea using the seasonal autoregressive integrated moving average (SARIMA) model

Economic development is influenced by many factors, one of which is the inflation rate. The consumer price index (CPI) is a commonly used indicator for measuring the inflation rate. In recent years, CPI prediction has attracted the attention of many scholars due to its excellent measure of macroeconomic performance. It is very essential to develop an accurate and precise forecasting model. This research fitted and forecasted the CPI of restaurants and hotels in South Korea using the seasonal autoregressive integrated moving average (SARIMA) model. In contrast to other time series models, the SARIMA model incorporates the seasonal component of a time series to improve the accuracy of its forecasts. The time series data were obtained from the website of Statistics Korea (KOSTAT) for the monthly CPI of restaurants and hotels in South Korea from January 2010 to December 2022. Data were analyzed using R-Statistics software and EViews. In this paper, the best model was based on the results of mean absolute percentage error (MAPE) and R2. A detailed description of model selection and predictive accuracy is provided. The findings suggest that the proposed research approach achieves good prediction accuracy from a range of different SARIMA models. Therefore, the developed SARIMA model can be considered for forecasting of monthly CPI of restaurants and hotels in South Korea.

ARIMA modelling of economic variables in the COVID-19 era: A study of the consumer price index

In addition to being fatal to the lives of thousands of people, coronavirus disease (COVID-19) was also fatal for corporations, businesses, and the entire global economy. COVID-19 is a pandemic with a contagious medical character, and it can even be fatal for the infected person who may have severe blockage of the respiratory system. From its origin, this virus spread across the globe, infecting hundreds of people of various ages and nationalities. For future occurrences to be understood, mathematical or statistical models with the fewest errors are required. This research is based on the studies of Sahai et al. (2020) and Ribeiro et al. (2020). In this study, the autoregressive integrated moving average (ARIMA) model is used to forecast the monthly consumer price index (CPI) in Kosovo. Furthermore, ARIMA (3, 1, 2) projections revealed the potential values for monthly CPI are expected to be on the rise with related implications for Kosovo. The study suggested introducing health, political and economic policies, such as a secure political climate, incentives for indigenous industries, economic diversification, and development of healthcare facilities throughout the world. The relevance of this research lies in the mathematical treatment of this problem which has never been treated in the Kosovar environment.

Quantitative Evaluation Model of Stock Market Liquidity by Macroeconomic Factors

In order to further understand the effects of macroeconomic factors on the stock market volatility and liquidity and solve the problem that the traditional volatility measurement model loses high-frequency data information in the modeling of the influence of macroeconomic factors on stock market volatility, monthly consumer price index, daily exchange rate, and monthly money supply are taken as the main indicators to investigate the stock market liquidity in the research. Meanwhile, CARCH-MIDAS model is used to investigate the factors affecting stock market liquidity. Through the model test, it is found that the H value of the volatility effect model of the three factors is 0.0307, and the H value of the horizontal effect model is 0.0220, and the result of the horizontal effect model is closest to 1%. The results show that CARCH-MIDAS model is relatively accurate in quantitative evaluation and prediction of the stock market liquidity and volatility.

Mozambique Consumer Price Index Estimation through Time Series Models

Background: The Consumer Price Index (CPI) is the main indicator for determining the prices level of a certain economy. The above-mentioned rate refers to the average prices on an indicated period and it is calculated mainly on goods consumed basis by selected families through weighting criteria and therefore, identifying the inflation damages on waged workers as well as their life standards thus, providing precise information on their earnings improvement or compensating their previous or equivalent buying empowerment. The application of rates is based on statistics models whereby; the temporary series is identified as observation of different periods that relevant variable values stand to foresee the future hypothesis confirmation. Objective: To Estimate the Consumer Price Index in Mozambique using Time Series Models. Methods: The following study based on monthly CPI data of Mozambique dating January 2011 to July 2020 out of 115 observed analyses. The data was processed through free open codes Gretl and 4.02-R Version Statistic Packages. The data hereby used was provided by the Mozambican National Statistics Institute (INE). The study applied the Box and Jenkins approach claiming that each temporary series value is based on its previous values due to the temporarily correlation between the series values that generally exist. The method consists of adjusting the Auto-regressive integrated Moving Average Method (ARIMA) to a data group under four interactive stages cycles Results: From 103 observations taken into account, the highest registered index was 158,25 corresponding to December 2016. On the other hand, it is notable that 101,62 is the smallest rate corresponding to January 2011period. The 114,68 medium rate breaks apart the price series in half resulting 50% of rates below such price and consequently, the remaining 50% above the same price. The similar parameter indicates all distribution format ends. Positive numbers indicate the widest end on the right while the negative numbers indicate the widest end on the left. Consequently, the positive asymmetry (1, 5217) indicates that the distribution has a wide end on the right. For kurtosis, the series is platykurtic since its result is less than 3 (2,6904 <3). Conclusion: The CPI in Mozambique showed an increasing trend in the period analyzed, between January 2011 and July 2020, with a sharp rise in 2016 returning to the original level and the normal growth trend of the series. In the estimation stage, it was possible to adequately select (02) two ARIMA models (p,d,q) that presented good fit to the data, and among them, the model selected as the most appropriate for predictions according to performance measures, was ARIMA (0.1.0) with constant, because it presented lower values of absolute mean percentage error (AMPS) and mean quadratic error (REQM). According to the selected model, the previsions for the period from August 2020 to July 2021 indicate a slight growth in consumer price index.

Forecast of Consumer Price Index-Take Beijing as an example

The consumer price index (CPI) reflects the relationship between the price changes of goods and services related to the life of residents and is an important indicator to evaluate the level of inflation. Because of the high randomness and volatility of CPI under the infectious diseases, it is very difficult to predict its trend accurately.In this paper, we combine the monthly CPI data of Beijing from January 2020 to July 2022, and use the ARIMA model, GM (1,1) and BP neural network model as the basis of the combined model to forecast the CPI of Beijing under the infectious diseases using two methods: ultra-short-term forecasting and conventional forecasting. It is obtained that the combined model has better forecasting effect than the single model, and the ultra-short-term forecasting effect is better than the conventional forecasting. Among them, the combination model using ARIMA-GM-BP for ultra-short-term forecasting is the best. Finally, the model and method were applied to forecast the CPI of Beijing in August as 102.079.

A Study on the Dynamic Behaviour of Headline Versus Core Inflation: Evidence from India

This article attempts to understand the dynamic response of core inflation and headline inflation to macroeconomic variables in the context of India. First, core inflation is calculated using the conventional exclusion measure (excluding food and energy components) and the statistical measure (asymmetrical trimmed mean, weighted median and exponential smoothing approach) by taking the monthly Consumer Price Index y-o-y inflation data from January 2012 to June 2018. Next, the study performed the comparative analysis to understand the dynamic response of both core inflation and headline inflation to different demand and supply shocks through Structural Vector Autoregressive Technique, Impulse Response Function and the Forecast Error Variance Decomposition. The findings suggest that trimming 20 per cent of the highly volatile components from the overall inflation can serve as a better proxy for the underlying medium and long-term trend in the headline inflation. Furthermore, the comparison made between headline inflation and core inflation shows that the key drivers for core inflation is the demand-side components such as real money growth, whereas headline inflation is mostly driven by the supply-side components in the context of India. The variations in core inflation are mainly observed because of the change in monetary policy decisions, hence making core inflation an important part the policy decision making. Core inflation indicates the underlying demand pressure, which can help policymakers to set adequate key policy rates to target overall inflation.

Modeling Consumer Price Index in Zambia: A Comparative Study between Multicointegration and Arima Approach

Consumer Price Index (CPI) is an important indicator used to determine inflation. The main objective of this research was to compare the forecasting ability of two time-series models using Zambia Monthly Consumer Price Index. We used monthly CPI data which were collected from January 2003 to December 2017. The models that were compared are the Autoregressive Integrated Moving average (ARIMA) model and Multicointegration (ECM) model. Results show that the ECM was the best fit model of CPI in Zambia since it showed smallest errors measures. Lastly, a forecast was done using the ECM and results show an average growth rate for food CPI at 6.63% and an average growth rate for nonfood CPI at 7.41%. Forecasting CPI is an important factor for any economy because it is essential in economic planning for the future. Hence, identifying a more accurate forecasting model is a major contribution to the development of Zambia.

Analysing the spillover of inflation in selected Euro-area countries

We examined the spillover of inflation in selected Euro-area countries using monthly consumer price index (CPI) based inflation data covering the period 1955M1 to 2017M4. To achieve our objective, we used two recently developed methods of spillover: a time-domain spillover method, Diebold–Yilmaz (hereafter referred as DY method) and a frequency-domain method, Barunik–Krehlik (hereafter referred as BK method). We analysed 1–4 months and more than 4 months spillovers. The study has importance because the co-movement in the international inflation rates, among others, may be produced by common shocks, similarities in central bank reaction functions, international trade and the operation of purchasing power parity theory. However, to assess the synchronisation of inflation fluctuations across countries or regions, it is critical to understand the inflation behaviour and formulation of correct monetary policy.

Neuro Fuzzy Modelling for Prediction of Consumer Price Index

Economic indicators such as Consumer Price Index (CPI) have frequently used in predicting future economic wealth for financial policy makers of respective country. Most central banks, on guidelines of research studies, have recently adopted an inflation targeting monetary policy regime, which accounts for high requirement for effective prediction model of consumer price index. However, prediction accuracy by numerous studies is still low, which raises a need for improvement. This manuscript presents findings of study that use neuro fuzzy technique to design a machine-learning model that train and test data to predict a univariate time series CPI. The study establishes a matrix of monthly CPI data from secondary data source of Tanzania National Bureau of Statistics from January 2000 to December 2015 as case study and thereafter conducted simulation experiments on MATLAB whereby ninety five percent (95%) of data used to train the model and five percent (5%) for testing. Furthermore, the study use root mean square error (RMSE) and mean absolute percentage error (MAPE) as error metrics for model evaluation. The results show that the neuro fuzzy model have an architecture of 5:74:1 with Gaussian membership functions (2, 2, 2, 2, 2), provides RMSE of 0.44886 and MAPE 0.23384, which is far better compared to existing research studies.

Charting the cost of nutritionally-adequate diets in Uganda, 2000-2011

Although malnutrition rates have been on the decline in Uganda over the past two decades, they remain high. Challenges to achieving nutritional improvements result, in part, from high staple foods prices, which raise the cost of the food basket and increase the risk of food and nutrition insecurity, especially for poor households who are net buyers of staple foods. Nearly two-thirds of Ugandan households are net buyers of staples, a pattern that highlights the potential importance of food prices as a key driver of food insecurity. During 2007-2008 the country experienced particularly sharp increases in the prices of staple foods. This paper examines how price changes influenced the cost of obtaining a nutritionally-adequate diet in Uganda. Diet costs are measured across five representative locations over the period 2000 to 2011. A linear programming model and observed monthly food prices are used to compute the lowest-cost diets in five major markets for an adult male and adult female aged 19-50. The diet costs are computed under two scenarios: (i) subject to a range of nutrient-specific constraints (a basic diet), and (ii) with allowance for palatability constraints (a constrained diet). To compare food costs over time, prices are deflated using the monthly consumer price index\ (CPI). Food prices are converted to prices per 100 gram portions, so as to maintain consistency with units of nutrient composition for given food items. The diet cost is compared to the Ugandan poverty line over time. The real cost of obtaining a nutritionally-adequate diet with palatability constraints grew at a rate of three to nine percent per year per annum over the period 2000 to 2011. Diet costs (with palatability constraints) have exceeded the poverty line for most years since 2000, with the gap widening in the period 2007-2008. Results highlight the importance of food prices to overall nutrition, and document spatial heterogeneity in diet costs in Uganda. Findings underscore the importance of developing and supporting interventions that raise the purchasing power of the poor and increase nutrition education and outreach aimed at cost-effectively achieving dietary diversity. These results are limited by the fact that the researchers did not have data for vegetables, sweet potatoes, and sorghum (all commonly-consumed staples) as well as fats, oils, sugar and animal roducts. Key words : diets, food prices, linear programming, malnutrition, markets, poverty,staples, Uganda

Forecasting Inflation Rate of Zambia Using Holt’s Exponential Smoothing

In this paper, the Holt’s exponential smoothing and Auto-Regressive Integrated Moving Average (ARIMA) models were used to forecast inflation rate of Zambia using the monthly consumer price index (CPI) data from May 2010 to May 2014. Results show that the ARIMA ((12), 1, 0) is an adequate model which best fits the CPI time series data and is therefore suitable for forecasting CPI and subsequently the inflation rate. However, the choice of the Holt’s exponential smoothing is as good as an ARIMA model considering the smaller deviations in the mean absolute percentage error and mean square error. Moreover, the Holt’s exponential smoothing model is less complicated since you do not require specialised software to implement it as is the case for ARIMA models. The forecasted inflation rate for April and May, 2015 is 7.0 and 6.6 respectively.

Construction and Seasonal Patterns of Islamic Hijri Calendar Monthly Time Series : An Application to Consumer Price Index ( CPI ) in Pakistan

Time series data are compiled and analysed in accordance with Gregorian calendar, given its world-wise use. This paper presents a simple method of constructing time series in accordance with Hijri Calendar from an already compiled Gregorian time series. Preliminary seasonal analysis of Hijri time series for CPI in Pakistan provides new insights of price behavior that depends both on Gregorian and Hijri seasonality. A spliced series of monthly CPI from January 1976 to December 2008 spanning 33 Gregorian years (396 Gregorian months) is used to capture a full cycle of 34 Hijri years (408 Hijri months). Method presented is general and can be used to construct and analyse any variable of interest. Paper proposes that statistical agencies and central banks of Islamic countries should also compile data according to Hijri Calendar, in addition to existing compilation according to Gregorian calendar. This will add to a better understanding of socioeconomic behaviours in Islamic countries.

소비자물가지수의 시계열모형 연구

소비자물가지수는 국가의 중요한 경제 척도 중의 하나이다. 본 연구에서는 4개 도시, 서울, 부산, 대구, 광주지역의 소비자물가지수를 연구하였다. 자료는 모두 통계청에서 발췌하였고, 기간은 1998년-2011년 월별자료이며, 시계열분석 기법인 자기회귀오차모형으로 분석하였다. 소비자물가 분석을 위한 설명변수는 9가지 경제변수인 경기동행지수, 미국환욜, 생산자물가지수, 원유수입단가, 원유수입물량, 국제경상수지, 수입물가지수, 실업율, 화폐통화량을 사용하였다. 분석 결과, 자기회귀오차모형으로 각 지역별 소비자물가지수를 46%-52% 정도 설명할 수 있다. The consumer price index (CPI) data is one of the important economic measurement of the country. In this article, the Autoregressive Error (ARE) model has been considered for analyzing the monthly CPI data at Seoul, Pusan, Daegu, and Gwangju Cities in Korea, In the ARE model, nine economic variables are used as the explanatory variables for the CPI data set. The nine explanatory variables are CCI (coincident composite index), won-dollar rate, producer price index, oil import price, oil import volume, international current account, import price index, unemployment rate, and amount of currency. The result showed that the monthly ARE models explained about 46-52% for describing the CPI.

The Stochastic Structure of a Price Index and Inflation

In this paper, we examine the stochastic structure of a consumer price index and its inflation that ultimately provides the basis for monetary policy and other contracting fulfillments. The index is the broad monthly consumer price index, IPC-A of the Brazilian Institute of Geography and Statistics (IBGE) in the period January 2001 to February 2011. We find that, at the low frequency, aggregate factors contribute significantly to price level dispersion in the long run; but not to the mean price level. The effects differ conditional on the weights attached to each component of the index. At the high frequency, using instrumental variables, we find that inflation dispersion is strictly increasing and strictly convex on average inflation. We propose one index filtering procedure and show that each aggregate variable has a positive high frequency effect on the actual average inflation.