Total Offset and Medical Net Discount Rates: 1981–2012

2013 ◽  
Vol 24 (2) ◽  
pp. 191-204 ◽  
Author(s):  
David Schap ◽  
Lauren Guest ◽  
Andrew Kraynak
Keyword(s):  
Time Series ◽  
Price Index ◽  
Consumer Price Index ◽  
Discount Rates ◽  
Monthly Data ◽  
Treasury Securities ◽  
Research Findings ◽  
Published Research

Abstract Medical net discount rates (MNDRs) are calculated using monthly data for the period 1981:01–2012:06 based on the medical consumer price index and annual percentage yields based on 3-month, 6-month and 1-year U.S. Treasury Securities. Stationarity is tested for each series and the results of time-series analytics through 2000:05 are compared to previously published results (Ewing, Payne and Piette, 2001) that omitted Treasury Securities of shortest duration. The various series are extended to 2012:06 and the time series properties are examined. Although the results are mixed, they are more supportive of total offset (i.e., a zero MNDR) than previously published research findings have been.

scholarly journals A forecasting the consumer price index using time series model

2021 ◽  
Vol 107 ◽  
pp. 10002
Author(s):  
Volodymyr Shinkarenko ◽  
Alexey Hostryk ◽  
Larysa Shynkarenko ◽  
Leonid Dolinskyi
Keyword(s):  
Time Series ◽  
Price Index ◽  
Consumer Price Index ◽  
Exponential Smoothing ◽  
Time Series Model ◽  
Initial Time ◽  
Autocorrelation Functions ◽  
Minimum Error ◽  
Initial Time Series

This article examines the behavior of the consumer price index in Ukraine for the period from January 2010 to September 2020. The characteristics of the initial time series, the analysis of autocorrelation functions made it possible to reveal the tendency of their development and the presence of annual seasonality. To model the behavior of the consumer price index and forecast for the next months, two types of models were used: the additive ARIMA*ARIMAS model, better known as the model of Box-Jenkins and the exponential smoothing model with the seasonality estimate of Holt-Winters. As a result of using the STATISTICA package, the most adequate models were built, reflecting the monthly dynamics of the consumer price index in Ukraine. The inflation forecast was carried out on the basis of the Holt-Winters model, which has a minimum error.

scholarly journals Analisis Indeks Harga Konsumen di Indonesia Melalui Pendekatan Kointegrasi

2019 ◽  
Vol 4 (2) ◽  
pp. 110-118
Author(s):  
Muhamad Muin ◽  
Keyword(s):  
Exchange Rate ◽  
Empirical Study ◽  
Money Supply ◽  
Price Index ◽  
Consumer Price Index ◽  
Data Series ◽  
Short Term ◽  
Monthly Data ◽  
The Relationship

This study aims to analyze the relationship between the rupiah exchange rate (RER) and the money supply (M1) on the outgrowth of the consumer price index (CPI) in Indonesia. The data used in this study are monthly data series from January 2005 to January 2019. The results of this empirical study shows that there is a relationship between RER and M1 on CPI in the long term and there is a correction in the short term balance (ECM) which is influenced by M1. All of these variables are significant at α = 5% and partly significant at α = 1%.

The Salaries of Officials and Officers in the Russian Empire

2021 ◽  
Vol 47 (3) ◽  
pp. 224-237
Author(s):  
Boris N. Mironov ◽  
Jan Surer
Keyword(s):  
Time Series ◽  
Price Index ◽  
Consumer Price Index ◽  
Russian Empire ◽  
Officer Corps ◽  
The Real ◽  
Regulatory Documents ◽  
The Russian Empire

Abstract This article analyzes changes in both the nominal and real salaries of Russian officials and officers. The study draws upon data concerning provincial administrations, which employed a significant portion of officials, and the infantry, in which most of the officer corps served, from the introduction of monetary salaries in 1763 (for officials) and in 1711 (for officers) to 1913. A table of the changes in nominal salaries was compiled from legislative and regulatory documents, and, with the use of a consumer price index constructed by the author, time series of the real salaries of officials and officers of various ranks were obtained by decades over 150 years.

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 UNDERSTANDING INDONESIA’S CITY-LEVEL CONSUMER PRICE FORMATION: IMPLICATIONS FOR PRICE STABILITY

2019 ◽  
Vol 22 (4) ◽  
pp. 405-422
Author(s):  
Paresh Kumar Narayan
Keyword(s):  
Price Index ◽  
Price Discovery ◽  
Consumer Price Index ◽  
Price Formation ◽  
Price Stability ◽  
Price Changes ◽  
Monthly Data

Using the Consumer Price Index (CPI) data of 82 Indonesian cities, we propose thehypothesis of heterogeneity in the cities’ contribution to the aggregate IndonesianCPI. Using a price discovery model fitted to monthly data, we discover that (1) of the23 cities in the province of Sumatera, five contribute 44% and nine contribute 66.7%to price changes, and (2) of the 26 cities in Java, four alone contribute 41.6% to pricechanges. Even in smaller provinces, such as Bali and Nusa Tenggara, one city alonedominates the change in aggregate CPI. From these results, we draw implications formaintaining price stability.

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 HE IMPACT OF INFLATION, CERTIFICATE OF BANK INDONESIA, AND EXCHANGE RATE OF IDR/USD ON THE INDONESIA COMPOSITE STOCK PRICE INDEX

2017 ◽  
Vol 1 (1) ◽  
pp. 42
Author(s):  
Margarita Ekadjaja ◽  
Daisy Dianasari
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Stock Price ◽  
Price Index ◽  
Inflation Rate ◽  
Stock Exchange ◽  
Monthly Data ◽  
Macroeconomic Variables ◽  
Research Population ◽  
Stock Price Index

This research is done with the aim to know whether some macroeconomic variables, which are inflation rate, certificate of Bank Indonesia (SBI) rate, and exchange rate of IDR/USD have an impact on the movement of the composite stock price index (IHSG) at the Indonesia stock exchange (BEI) partially and simultaneously in the period of 2006–2014. The research population is inflation rate, SBI rate, and exchange rate of IDR/USD. Data analysis in this research is multiple regression by using time series monthly data of 2006–2014. Research results show that partially inflation rate gives positive significant impact on IHSG, SBI rate has negative significant impact on IHSG, and exchange rate of IDR/USD has positive significant impact on IHSG.  Simultaneously it shows that inflation, SBI rate, and exchange rate of IDR/USD have an impact on IHSG at BEI to the period of year 2006 – 2014.  Those variables affect IHSG by 58,74%, while other variables affect IHSG by 41,26%.  That information can be used by investors to make decision on their investment.Keywords: inflation, SBI, exchange rate, IHSG, BEI.

scholarly journals The relationship between M3 and consumer price index in the Czech Republic

2018 ◽  
Vol 4 (10) ◽  
pp. 226-236
Author(s):  
Libena Cernohorska
Keyword(s):  
Time Series ◽  
Czech Republic ◽  
Price Index ◽  
Statistical Data ◽  
Consumer Price Index ◽  
Monetary Aggregate ◽  
The Czech Republic ◽  
Significance Level ◽  
The Relationship ◽  
Cointegration Relationship

This paper aimed at analysing the influence of monetary aggregate M3 on consumer price index (CPI) in the Czech Republic. Cointegrating this selected indicator M3 is demonstrated in relation to the development of CPI using the Engle – Granger cointegration test. These tests are applied to selected statistical data from the years 2003 to 2016. After using the Akaike criteria for all-time series, we analysed a unit root using the Dickey–Fuller test. If the time series are non-stacionary, testing is then continued with the Engle–Granger test to detect cointegration relations. Based on these tests, it is found that at a significance level of 0.05, a cointegration relationship between M3 and CPI in the Czech Republic does not exist. Conclusions resulting from the verification of the hypotheses are supported with graphical visualisation of data from which it is apparent that these hypotheses can be rejected. Keywords: M3; Czech Republic ; CPI ; Akaike criteria

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