The Prediction for the Consumer Price Index of Residents in Perspective of Time Series Method in Case of Chongqing

The consumer price index (CPI) measures the relative number of changes in the price level of consumer goods and services over time, reflecting the trend and degree of changes in the price level of goods and services purchased by residents. This article uses the ARMA model to analyze the fluctuation trend of the CPI (taking Chongqing as an example) and make short-term predictions. To test the predictive performance of the model, the observation values from January to December 2023 were retained as the reference object for evaluating the predictive accuracy of the model. Finally, through trial predictions of the data from May to August 2023, it was found that the constructed model had good fitting performance.

The Response of Consumer Food Price Index(CFPI)due to the Impact of Pandemic COVID-19 on Indian Agriculture Sector

India is an agricultural country and a core source of income for the world population.The Indian economy is greatly depending on agriculture that is decrease day by day due to pandemic COVID-19.India is a major exporter of many crop foods.India,Thailand,and Vietnam are the major exports of rice if these stopped exports it reduces the economy up to 15%.A related circumstance is built up with diverse yields too like wheat,sunflower whose fare has been stationary by Kazakhstan,Serbia individually.In India,the end of April is the main source of income to farmers because they sell their rabi crops(wheat,mustard,maize,lentil,chilies,gram,tomatoes)in the market drastically decreases of CFPI may lead to the distress of Indian agricultural economy.The change over time in the price of options on wheat futures reveals increased price volatility in response to growing uncertainty about the COVID-19 impacts.

Research on the Change of GDP Growth,Monetary Growth and Price Index based on VAR and IRF

In this paper,we use the macro data from the first quarter of 2001 to the first quarter of 2015,through vector autoregressive(VAR)model,Granger causality analysis,impulse response function(RFI)and variance decomposition analysis of quantitative analysis methods,to research on the relationship among China’s real growth variation of gross domestic product(GDP),Money supply growth rate and consumer price index.We find that the money supply growth has impact on China’s real growth variation of GDP in short-term,but there is no long-term significant effect.Economic growth is the main factor to promote the consumer price index growth,money growth is not the main factor driving the change in the price index.China’s currency growth is affected significantly by the change of the economic growth.

The Relationship between the Ethiopian Economic Growth and the Decrease in Purchasing Power of Consumers

There is no clear theory which states fixed relationship between inflation and growth.Controversy by quantity and institutional inflation theories also confirm this.According to quantity theorists,there is a long-run trade-off between inflation and economic growth but the supporters of institutional theory of inflation,are less sure about presence of negative relationship about inflation and growth.Thus,the relationship between inflation and economic growth is debatable both in the world and specifically to Ethiopia.Therefore,the objective of this critical review is to determine the relationship between the current status of the Ethiopian economy and the consumer price index by considering the economic development indictors and consumer price index.The nexus of inflation and economic growth is one of the most important macroeconomic policy problems that take the attention of researchers,policy makers and different scholars.Conducting this review will benefit developing countries by discovering what their current status is,as far as a person with a higher level of consumption is regarded as having a higher level of economic wellbeing than someone with a lower level of consumption.This study falls within the ambit of the pragmatism philosophical stance and exploratory in nature.This study applied the inductive method of reasoning and used secondary data.The Study found that there is a negative relationship between the Ethiopian economic growth and the purchasing power of consumer(consumer price index)synonymously measured by the inflation-macroeconomic growth trade-off.The review reveals that Ethiopian economy is highly growing and the consumer price index(purchasing power of consumers)is decreasing.This shows that the purchasing power consumer(consumer price index)in Ethiopia is not solely determined by the macroeconomic development,which in turn requires further investigation.It is recommended therefore that future research works will explore more on the relationship between the Ethiopian economic growth and the purchasing power of consumer or clearly can explore the effect of economic growth on the purchasing power of consumers(consumer price index).

Demand for Money and Inflation in Ethiopia

One of the vital components of the macroeconomic model that helps policymaking is the demand for money function.Having reliable predictions on the money demand function helps in determining the optimum growth of money supply which is vital in controlling the inflation rate in the economy and also preventing monetary disturbances from affecting real output.In order to formulate and estimate the money demand function in Ethiopia,this study used quarterly data from 2000Q3 to 2021Q2 and employed the Ordinary Least Square method and Engle-Granger two-stage procedure for empirical analysis.The empirical result from the models indicates that,in the long run,all variables(real GDP,CPI inflation,real effective exchange rate,real interest rate and lagged real money balance)are significantly affecting the demand for money in Ethiopia.Whereas,the estimated coefficients of the short-run variable show that the real effective exchange rate,CPI inflation,and lagged real money balance are the main determinants of demand for money while the real GDP and real interest rate are insignificant.Another important finding is that absolute value of the coefficient of the error correction term implies that about 54.2%of the disequilibrium in real money demand is counter-balanced by short-run adjustment in each quarter.The study suggests that in conducting monetary policy,policymakers should consider not only the behavior of income and price but also the movement of exchange rates.The study also calls for appropriate formulation and estimation of the all-encompassing demand for money function that is capable of bringing stability to the growth of money coupled with sustainable economic growth.

Production Chain Length and PPI-CPI Divergence:Analysis Based on the Global Input-output Price Model

Productivity and international energy price shocks are reflected in PPI and CPI via industrial chains.China’s in-depth participation into the global value chains has increasingly lengthened its industrial production chains.The question is how the changing length of production chains will affect CPI and PPI,as well as CPI-PPI correlation?By constructing a global input-output price model,this paper offers a theoretical discussion on the impact of production chain length on the CPI-PPI divergence.Our findings suggest that the price shock of international bulk commodities has a greater impact on China’s PPI than that on CPI.The effects on both China’s PPI and CPI estimated by using the single-country input-output model are higher than the results estimated with the global input-output model.However,the difference between CPI and PPI variations estimated with the global input-output model is greater than the result estimated with the single-country input-output model,which supports the view that the lengthening of production chains,especially international production chains,leads to a divergence between CPI and PPI.Empirical results based on cross-national panel data also suggest that the lengthening of production chains has reduced the CPI-PPI correlation for countries,i.e.the lengthening of production chains has increased the PPI-CPI divergence.That is to say,policymakers should target not just CPI in maintaining price stability,but instead focus on the stability of both PPI and CPI.Efforts can be made to proactively adjust the price index system,and formulate the industrial chain price index.

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.

A Hybrid Neural Network and Box-Jenkins Models for Time Series Forecasting

Time series forecasting plays a significant role in numerous applications,including but not limited to,industrial planning,water consumption,medical domains,exchange rates and consumer price index.The main problem is insufficient forecasting accuracy.The present study proposes a hybrid forecastingmethods to address this need.The proposed method includes three models.The first model is based on the autoregressive integrated moving average(ARIMA)statistical model;the second model is a back propagation neural network(BPNN)with adaptive slope and momentum parameters;and the thirdmodel is a hybridization between ARIMA and BPNN(ARIMA/BPNN)and artificial neural networks and ARIMA(ARIMA/ANN)to gain the benefits of linear and nonlinearmodeling.The forecasting models proposed in this study are used to predict the indices of the consumer price index(CPI),and predict the expected number of cancer patients in the Ibb Province in Yemen.Statistical standard measures used to evaluate the proposed method include(i)mean square error,(ii)mean absolute error,(iii)root mean square error,and(iv)mean absolute percentage error.Based on the computational results,the improvement rate of forecasting the CPI dataset was 5%,71%,and 4%for ARIMA/BPNN model,ARIMA/ANN model,and BPNN model respectively;while the result for cancer patients’dataset was 7%,200%,and 19%for ARIMA/BPNNmodel,ARIMA/ANN model,and BPNNmodel respectively.Therefore,it is obvious that the proposed method reduced the randomness degree,and the alterations affected the time series with data non-linearity.The ARIMA/ANN model outperformed each of its components when it was applied separately in terms of increasing the accuracy of forecasting and decreasing the overall errors of forecasting.

Estimation of the True Cost of Living Index of food consumption for Chinese urban consumers

This paper studies the True Cost of Living Index(TCLI)of food consumption for all income groups of urban households in China from 1997 to 2012.Based on the Generalised Fechner-Thurstone direct utility function,this paper estimates the TCLI under variable preference assumptions,and also decomposes the total bias of the traditional Laspeyres Price Index(LPI)estimation of TCLI into substitution bias and preference variation bias.For all income group households,the estimated preference variation bias is found to be quite substantial.According to the finding,when it is necessary to apply the cost of living adjustment clause in any public welfare programmes,the TCLI calculation compatible with preference variation may be more appropriate than the usual Laspeyres Consumer Price Index estimation.

基于在线大数据的通货膨胀“现时”预测

新冠肺炎疫情冲击导致经济出现结构性变化,对通胀预测提出了新的挑战;而大数据时代的到来,则为提高通胀预测的时效性提供了新的机遇.本文据此围绕基于大数据的通胀“现时”预测(nowcasting)进行探索,提出一个基本的现时预测框架,其核心是引入新的大数据宏观实时变量或大数据预测方法.本文通过引入宏观实时变量--基于互联网在线大数据的居民消费价格指数(internet-based consumer price index,iCPI),包括总类和大类的iCPI日环比指数、周环比指数、旬同比指数和月同比指数,采用LASSO(the least absolute shrinkage and selection operator)降维法和混频数据抽样模型(mixed data sampling,MIDAS),有效地提高了通胀预测的时效性和准确性.研究发现:不同频率的iCPI均有利于提高通胀预测准确性,其表现优于基准模型和大部分的同频传统指标,当其与传统指标相结合时,可进一步降低预测误差,目前尚不能完全舍弃传统变量和方法;在不同频率下(日度除外),iCPI八大类的预测效果优于iCPI总类;不同频率的大数据指标在通胀预测的准确性和时效性上各有优势,这与其背后反映的信息结构有关,其中高频旬同比iCPI表现尤为突出、其能较好地兼顾预测时效性和准确性.本研究为数字经济时代利用大数据提高通胀预测的准确性和时效性、创新宏观经济监测与预测体系提供了有益参考.