scholarly journals Statistical Capacity Building of Official Statisticians in Practice: Case of the Consumer Price Index

2016 ◽  
Vol 32 (4) ◽  
pp. 827-848
Author(s):  
Tomi Deutsch
Keyword(s):  
Capacity Building ◽  
Price Index ◽  
Inflation Rate ◽  
Consumer Price Index ◽  
Academic Research ◽  
Professional Community ◽  
Index Formula ◽  
Methodological Issues ◽  
Welfare Benefits ◽  
Research Findings

Abstract This article focuses on the issue of statistical capacity building of official statisticians using the case of the consumer price index (CPI) as an illustrative example. Although used for indexation of salaries, pensions, and social welfare benefits, but also as an approximation of the general inflation rate, there are several unresolved methodological issues associated with CPI’s calculation. Apart from the choice among two alternative concepts, the challenge of how to include owner-occupied housing (OOH) in CPI has also not been adequately resolved yet. Analysis in the article is based on Slovenian data. The results show that accuracy of the CPI significantly improves if it is calculated using one of the superlative and symmetric formulas, and that it makes sense to include OOH in CPI using the total acquisitions approach. The analysis further indicates that the choice of the index formula for calculating CPI has a much greater impact on the CPI value than inclusion of OOH. Academic research findings such as these should not remain unknown to the wide professional community of official statisticians. Formal channels for knowledge transfer from academia to official statistics providers should be established to facilitate continuous statistical capacity building of official statisticians.

scholarly journals SARIMA Model for Forecasting Price Indices Fluctuations

2021 ◽  
Vol 2 (6) ◽  
pp. 1-6
Author(s):  
N. Sonai Muthu ◽  
K. Senthamarai Kannan ◽  
V. Deneshkumar ◽  
P. Thangasamy
Keyword(s):  
Rural Areas ◽  
Price Index ◽  
Inflation Rate ◽  
Price Level ◽  
Consumer Price Index ◽  
Demand Side ◽  
Price Indices ◽  
Sarima Model ◽  
Food And Beverages ◽  
Urban And Rural Areas

In day-to-day life, the price level fluctuations in the Consumer Price Index (CPI) goods and service. So, the retail consumers are affecting by that price level changes, who are on the demand side of the economy. The main objective of this work is to forecast such selected factors of CPI in urban and rural areas of India, like: Food and Beverages, Pan, Tobacco and Intoxicants, Fuel and Light and Education and also compute the inflation rate for those four main variables in all India.

scholarly journals Index Number Issues in the Consumer Price Index

1998 ◽  
Vol 12 (1) ◽  
pp. 47-58 ◽  
Author(s):  
W. Erwin Diewert
Keyword(s):  
Price Index ◽  
Inflation Rate ◽  
Consumer Price Index ◽  
Index Number ◽  
Cost Of Living ◽  
Fixed Base ◽  
Statistical Agencies ◽  
Cost Of Living Index ◽  
Theoretical Cost

This paper addresses the following issues: what is an appropriate theoretical consumer price index that statistical agencies should attempt to measure; what are some of the possible sources of biases between the fixed base Laspeyres price index that statistical agencies produce and the theoretical cost-of-living index; and what factors will make the biases larger or smaller and how will the biases change as the general inflation rate changes? This paper addresses all of the issues mentioned above and discusses what statistical agencies can do to reduce the biases.

scholarly journals Analisis Perkembangan Indeks Harga Konsumen di Kota Medan

2020 ◽  
Vol 3 (2) ◽  
pp. 412-418
Author(s):  
Sari Wulandari ◽  
Muhammad Dani Habra
Keyword(s):  
Price Index ◽  
Inflation Rate ◽  
Consumer Price Index ◽  
Commodity Prices ◽  
Household Expenditure ◽  
The Public ◽  
Goods And Services ◽  
City Community ◽  
The Subject ◽  
The City

The Consumer Price Index (CPI) is one of the important economic indicators that can provide information about the development of prices of goods and services (commodities) paid by consumers or the public especially the city community. This study aims to analyze the Development of the Consumer Price Index in Medan City. The benefits of this research are a description of the fluctuations in commodity prices for basic needs of the community at the level of consumers or retail traders. This type of research is descriptive qualitative. The subject in this study is the Central Statistics Agency and the object in this study is the Consumer Price Index through seven groups of household expenditure in 2018-2019. The results showed that the development of price indices in Medan City tends to fluctuate from seven types of household expenditure groups. During the January-December 2019 period the highest inflation of the seven types of expenditure was foodstuffs. Keywords: Consumer Price Index, Inflation Rate

scholarly journals PERAMALAN LAJU INFLASI DENGAN METODE AUTO REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA)

2017 ◽  
Vol 14 (4) ◽  
pp. 524 ◽  
Author(s):  
Djawoto Djawoto
Keyword(s):  
Time Series ◽  
Price Index ◽  
Inflation Rate ◽  
Moving Average ◽  
Arima Model ◽  
Consumer Price Index ◽  
Stationary Time Series ◽  
Stationary Time ◽  
Auto Regressive ◽  
Auto Regression

Auto Regression Integrated Moving Average (ARIMA) or the combination model of Auto Regression with moving average, is a linier model which is able to represent the stationary time series or non stationary time series. The purpose of this research is to forecast the inflation rate in November 2010 with the Consumer Price Index (CPI) by using ARIMA. The inflation indicator is very important to anticipate in making the Government’s policy and decision as well as for the citizen is for the information to determine what to do in related with savings and investment. By looking at the existing criteria, it is determined that the best model is ARIMA (1,1,0) or AR (1). Model ARIMA (1,1,0), the coefficient value AR (1) is significant,which has the most minimum value of Akaike Info Criterion (AIC) and Schwars Criterion (SC) compare toARIMA (0,1,1) or MA (1) and ARIMA (1,1,1) or AR (1) MA (1). In summarize, the ARIMA model used to forecast the valueof IHK is ARIMA (1,1,0).

scholarly journals CPI Mismeasurements and Their Impacts on Economic Management in Korea

2010 ◽  
Vol 9 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Chul Chung ◽  
John Gibson ◽  
Bonggeun Kim
Keyword(s):  
Price Index ◽  
Inflation Rate ◽  
Panel Study ◽  
Consumer Price Index ◽  
The Elderly ◽  
Estimation Result ◽  
Sample Period ◽  
Economic Management ◽  
Real Gdp ◽  
Pension Payments

We estimate the consumer price index (CPI) bias in Korea by employing the approach of Engel's Law as suggested by Hamilton (2001). Using Korean panel data (Korean Labor and Income Panel Study) and following Hamilton's model with a non-linear specification correction, our estimation result shows that the CPI bias over the sample period (2000–05) averaged at least 0.7 percent annually, which implies that about 21 percent of the inflation rate during the sample period can be attributed to the bias. This CPI bias has caused a substantial understatement of the growth in real GDP and contributes to excessive transfers from younger taxpayers to the elderly through indexed pension payments. We discuss the implications of the CPI bias for economic management and policies in Korea.

scholarly journals ANALISIS INFLASI DI SUMATERA UTARA: SUATU MODEL ERROR CORRECTION (ECM)

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Hafsyah Aprillia
Keyword(s):  
Interest Rate ◽  
Price Index ◽  
Inflation Rate ◽  
Consumer Price Index ◽  
Model Error ◽  
Explanatory Variables ◽  
Interest Rate Spread ◽  
Optimal Value ◽  
Model Equations ◽  
The U.S

The research was conducted to determine the effect of economic variables that can explain the change or variation in the rate of inflation in the Consumer Price Index (CPI) as the dependent variable. The explanatory variables (independent) were used as controls are SBI, the nominal interest rate spread (SBI) and the value of the rupiah against the U.S. dollar. Based on these results, according to the specific purpose of the model equations II, suggested economic actors can use SBI interest rate spread as an indicator of variations in the CPI inflation rate at intervals of 8 and 12 months, with a note that the obtained level of explanation has not shown that the optimal value.

scholarly journals PERAMALAN LAJU INFLASI DENGAN METODE AUTO REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA)

2018 ◽  
Vol 14 (4) ◽  
pp. 524-538
Author(s):  
Djawoto Djawoto
Keyword(s):  
Time Series ◽  
Price Index ◽  
Inflation Rate ◽  
Moving Average ◽  
Arima Model ◽  
Consumer Price Index ◽  
Stationary Time Series ◽  
Stationary Time ◽  
Auto Regressive ◽  
Auto Regression

Auto Regression Integrated Moving Average (ARIMA) or the combination model of Auto Regression with moving average, is a linier model which is able to represent the stationary time series or non stationary time series. The purpose of this research is to forecast the inflation rate in November 2010 with the Consumer Price Index (CPI) by using ARIMA. The inflation indicator is very important to anticipate in making the Government’s policy and decision as well as for the citizen is for the information to determine what to do in related with savings and investment. By looking at the existing criteria, it is determined that the best model is ARIMA (1,1,0) or AR (1). Model ARIMA (1,1,0), the coefficient value AR (1) is significant,which has the most minimum value of Akaike Info Criterion (AIC) and Schwars Criterion (SC) compare toARIMA (0,1,1) or MA (1) and ARIMA (1,1,1) or AR (1) MA (1). In summarize, the ARIMA model used to forecast the valueof IHK is ARIMA (1,1,0).

scholarly journals Management of the Size of the Supply Under the Conditions of Inflation

2019 ◽  
Vol 2 (2) ◽  
pp. 279-288
Author(s):  
Paweł Ślaski
Keyword(s):  
Price Index ◽  
Inflation Rate ◽  
Consumer Price Index ◽  
Sales Price ◽  
Inventory Costs ◽  
First Case ◽  
Non Linear ◽  
Made In

Abstract The publication describes two ways of shopping, taking into account the CPI (Consumer Price Index) inflation rate. In the first case, changes in the sales price are made in a continuous manner in accordance with the inflation rate, and therefore it is better to make larger purchases. In the second case, it is better to carry out smaller purchases, because it is characterized by one-time adjustment of sales prices to the entire purchased a lot of goods. Both cases were verified based on the Solver tool, using non-linear, integer-based optimization. The final result was to determine the optimal purchase quantities with the minimum inventory costs.

scholarly journals Weighting bias and inflation in the time of COVID-19: evidence from Swiss transaction data

2020 ◽  
Vol 156 (1) ◽  
Author(s):  
Pascal Seiler
Keyword(s):  
Consumer Behavior ◽  
Price Index ◽  
Inflation Rate ◽  
Consumer Price Index ◽  
Consumer Expenditure ◽  
Consumer Spending ◽  
Transaction Data ◽  
Debit Card ◽  
Public Data

Abstract Sharp changes in consumer expenditure may bias inflation during the COVID-19 pandemic. Using public data from debit card transactions, I quantify these changes in consumer spending, update CPI basket weights and construct an alternative price index to measure the effect of the COVID-induced weighting bias on the Swiss consumer price index. I find that inflation was higher during the lock-down than suggested by CPI inflation. The annual inflation rate of the COVID price index was −0.4% by April 2020, compared to −1.1% of the equivalent CPI. Persistent “low-touch” consumer behavior can further lead to inflation being underestimated by more than a quarter of a percentage point until the end of 2020.

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