Forecasting and Technical Comparison of Inflation in Turkey With Box-Jenkins (ARIMA) Models and the Artificial Neural Network

2020 ◽  
Vol 9 (4) ◽  
pp. 84-103 ◽  
Author(s):  
Erkan Işığıçok ◽  
Ramazan Öz ◽  
Savaş Tarkun
Keyword(s):  
Price Index ◽  
Consumer Price Index ◽  
Predictive Performance ◽  
Decision Makers ◽  
Inflation Expectations ◽  
Arima Models ◽  
Goods And Services ◽  
The Future ◽  
Alternative Techniques ◽  
Artificial Neural

Inflation refers to an ongoing and overall comprehensive increase in the overall level of goods and services price in the economy. Today, inflation, which is attempted to be kept under control by central banks or, in the same way, whose price stability is attempted, consists of continuous price changes that occur in all the goods and services used by the consumers. Undoubtedly, in terms of economy, in addition to the realized inflation, inflation expectations are also gaining importance. This situation requires forecasting the future rates of inflation. Therefore, reliable forecasting of the future rates of inflation in a country will determine the policies to be applied by the decision-makers in the economy. The aim of this study is to predict inflation in the next period based on the consumer price index (CPI) data with two alternative techniques and to examine the predictive performance of these two techniques comparatively. Thus, the first of the two main objectives of the study are to forecast the future rates of inflation with two alternative techniques, while the second is to compare the two techniques with respect to statistical and econometric criteria and determine which technique performs better in comparison. In this context, the 9-month inflation in April-December 2019 was forecast by Box-Jenkins (ARIMA) models and Artificial Neural Networks (ANN), using the CPI data which consist of 207 data from January 2002 to March 2019 and the predictive performance of both techniques was examined comparatively. It was observed that the results obtained from both techniques were close to each other.

Forecasting and Technical Comparison of Inflation in Turkey With Box-Jenkins (ARIMA) Models and the Artificial Neural Network

2022 ◽  
pp. 1194-1216
Author(s):  
Erkan Işığıçok ◽  
Ramazan Öz ◽  
Savaş Tarkun
Keyword(s):  
Consumer Price Index ◽  
Predictive Performance ◽  
Central Banks ◽  
Decision Makers ◽  
Inflation Expectations ◽  
Arima Models ◽  
Goods And Services ◽  
The Future ◽  
Alternative Techniques ◽  
Artificial Neural

Inflation refers to an ongoing and overall comprehensive increase in the overall level of goods and services price in the economy. Today, inflation, which is attempted to be kept under control by central banks or, in the same way, whose price stability is attempted, consists of continuous price changes that occur in all the goods and services used by the consumers. Undoubtedly, in terms of economy, in addition to the realized inflation, inflation expectations are also gaining importance. This situation requires forecasting the future rates of inflation. Therefore, reliable forecasting of the future rates of inflation in a country will determine the policies to be applied by the decision-makers in the economy. The aim of this study is to predict inflation in the next period based on the consumer price index (CPI) data with two alternative techniques and to examine the predictive performance of these two techniques comparatively. Thus, the first of the two main objectives of the study are to forecast the future rates of inflation with two alternative techniques, while the second is to compare the two techniques with respect to statistical and econometric criteria and determine which technique performs better in comparison. In this context, the 9-month inflation in April-December 2019 was forecast by Box-Jenkins (ARIMA) models and Artificial Neural Networks (ANN), using the CPI data which consist of 207 data from January 2002 to March 2019 and the predictive performance of both techniques was examined comparatively. It was observed that the results obtained from both techniques were close to each other.

scholarly journals THE LONG-RUN RELATIONSHIP BETWEEN HOUSE PRICES AND INFLATION IN SOUTH AFRICA: AN ARDL APPROACH

2013 ◽  
Vol 17 (2) ◽  
pp. 188-198 ◽  
Author(s):  
Roula Inglesi-Lotz ◽  
Rangan Gupta
Keyword(s):  
South Africa ◽  
Price Index ◽  
House Prices ◽  
Housing Prices ◽  
Consumer Price Index ◽  
Data Series ◽  
Middle Segment ◽  
Goods And Services ◽  
Long Run ◽  
Housing Costs

This paper investigates whether house prices provide a suitable hedge against inflation in South Africa by analysing the long-run relationship between house prices and the prices of non-housing goods and services. Quarterly data series are collected for the luxury, large middle-segment, medium middle-segment, small middle-segment and the entire middle segment of house prices, as well as, the consumer price index excluding housing costs for the period 1970:Q1–2011:Q1. Based on autoregressive distributed lag (ARDL) models, the empirical results indicate long-run cointegration between the house prices of all the segments and the consumer price index excluding housing costs. Moreover, the long-run elasticity of house prices with respect to prices of non-housing goods and services, i.e., the Fisher coefficient is greater than one for the luxury segment, virtually equal to one for the small middle-segment, and less than one for the large and medium middle-segments, as well as the affordable segments. More importantly though, the estimated Fisher coefficients are not statistically different from unity – a result consistent with the proposed theoretical framework relating housing prices and consumer prices excluding housing expenditure. In general, we infer that house prices in South Africa provide a stable inflation hedge in the long-run.

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 Applying the ARIMA Model to the Process of Forecasting GDP and CPI in the Jordanian Economy

2021 ◽  
Vol 12 (3) ◽  
pp. 70
Author(s):  
Abdullah Ghazo
Keyword(s):  
Time Series ◽  
Economic Policy ◽  
Gross Domestic Product ◽  
Economic Activity ◽  
Price Index ◽  
Arima Model ◽  
Consumer Price Index ◽  
Decision Makers ◽  
Domestic Product

Gross Domestic Product (GDP) and consumer price index (CPI) are significant indicators to describe and evaluate economic activity and levels of development. They are also often used by decision makers so as to plan economic policy. This paper aims at modeling and predicting GDP and CPI in Jordan. In order to achieve this goal, the study applied the Box- Jenkins (JB) methodology for the period 1976-2019. Based on the results, ARIMA (3,1,1) found to be the best model for the GDP. In addition, ARIMA (1,1,0) was the best model for forecasting the CPI. The results were supported with the findings of the stationarity and identification rules test of time series under using AIC and SIC criterion. The forecasted values of the GDP and the CPI for the next three years (2020-2022) were (29342.12, 32095.10, 35106.36 million JD) and (128.31, 133.28, 139.28) respectively. Compared with 2019, the GDP is forecasted to decrease in 2020, while the CPI is forecasted to increase in 2020. This implies that the Jordanian economy is tending toward stagflation. After 2020, both GDP and CPI increased, which indicates that Jordanian economy is tending toward cost-push inflation.

Forecasting inflation in G-7 countries: an application of artificial neural network

foresight ◽  
2015 ◽  
Vol 17 (1) ◽  
pp. 63-73 ◽  
Author(s):  
Sanjeev Gupta ◽  
Sachin Kashyap
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Price Index ◽  
Consumer Price Index ◽  
Suitable Model ◽  
Neural Network Models ◽  
Policy Makers ◽  
Content Type ◽  
Artificial Neural ◽  
Forecasting Inflation

Purpose – The paper aims to evaluate different artificial neural network models and to suggest a suitable model for forecasting inflation in G-7 countries. Design/methodology/approach – The study applies different combinations of neural networks with hyperbolic tangent function using backpropagation learning with the steepest gradient descent technique to monthly data on Consumer Price Index (a measure of inflation) of the USA, the UK, France, Germany, Italy, Japan and Canada. Findings – Predictions of inflation based on the Consumer Price Index for all the seven countries divulged that it is expected that the rate of inflation will decline marginally in the near future. Practical implications – The results proposed in this study will be a benchmark for policy-makers, economists and practitioners to forecast inflation and design policies accordingly. Originality/value – The paper’s findings provide strong evidence for policy-makers that while constructing models for forecasting inflation, the suggested models can be used to track the future rates of inflation and, further, they can apply that model in framing policies.

scholarly journals Are Some Prices in the CPI More Forward Looking Than Others? We Think So

2010 ◽  
pp. 1-6 ◽  
Author(s):  
Michael F. Bryan ◽  
Brent H. Meyer
Keyword(s):  
Price Index ◽  
Consumer Price Index ◽  
Sticky Prices ◽  
Economic Conditions ◽  
Inflation Expectations ◽  
Index Change ◽  
Forward Looking

Some of the items that make up the Consumer Price Index change prices frequently, while others are slow to change. We explore whether these two sets of prices--sticky and flexible--provide insight on different aspects of the inflation process. We find that sticky prices appear to incorporate expectations about future inflation to a greater degree than prices that change on a frequent basis, while flexible prices respond more powerfully to economic conditions--economic slack. Importantly, our sticky-price measure seems to contain a component of inflation expectations, and that component may be useful when trying to gauge where inflation is heading.

scholarly journals A Weighted Markov Chain Application to Predict Consumer Price Index When Facing Pandemic Covid-19

2021 ◽  
Vol 1 (1) ◽  
pp. 41-46
Author(s):  
Indah Purnama Sari Siregar ◽  
Rina Widyasari ◽  
Nurul Huda Prasetya
Keyword(s):  
Infectious Disease ◽  
Economic Growth ◽  
Economic Development ◽  
Markov Chain ◽  
Severe Acute Respiratory Syndrome ◽  
Price Index ◽  
Consumer Price Index ◽  
Purchasing Power ◽  
Markov Chain Method ◽  
The Future

Covid-19 is an infectious disease caused by acute respiratory syndrome coronavirus 2 (severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2). It makes the decrease of people's purchasing power. Meanwhile, the economic growth indicates the success of a country's economic development. Therefore, the Consumer Price Index occurs Inflation and Deflation, which is commonly referred to in the economy as the Consumer Price Index. This study aimed to apply a weighted Markov chain method to predict the consumer price index in the future. The satisfactory results obtained by researchers in predicting the consumer price index are in, the chance is 84.34% and the 12th month has a 78.54% chance.

scholarly journals U.S. consumer price index in the second half of XX century: calculation strategy and ways of its changes

2019 ◽  
Vol 16 (6) ◽  
pp. 67-76
Author(s):  
M. A. Kozlova
Keyword(s):  
Price Index ◽  
Consumer Price Index ◽  
Index Number ◽  
Goods And Services ◽  
Level Of Aggregation ◽  
High Level ◽  
Bureau Of Labor Statistics ◽  
Alternative Solutions ◽  
Data Generalization ◽  
Index Calculation

The purpose of this research is a detection of U.S. consumer price index development and change ways emerged in the second half of XX century. Consumer price index is considered as a practically evaluable index number.Materials and methods. This research is based on the methodology documents of U.S. Bureau of Labor Statistics and its theoretical and practical papers published in Monthly Labor Review. The basic method is historical and descriptive techniques.Results. Data generalization for U.S. consumer price index across five revisions is realized in structure of the calculation method, adapted by ROSSTAT for the national consumer price index. Firstly the dynamic of number of cities, included in consumer price survey and changes of its sample is analyzed. Secondly the principles of point of purchase sampling is in focus. Thirdly the set of goods and services and dynamics of its structure are considered. Fourthly there is a generalization of pricing procedure principles that is frequency according to the type of cities and feature of goods and services. Fifthly the source and limits of data collecting for weights which needed for consumer price index calculation on the high level of aggregation. And sixthly there is description of mean price and price index calculation.Conclusion. The main ways of development and transformation in U.S. consumer price index are defined. It may be considered as alternative solutions in consumer price index of other countries. The main ways are the increase of city and goods sampling, extension of probability use, formation of good classification, equal temporal interval of weight renovation and creation of price index system.

scholarly journals Analisis Indeks Harga Konsumen (IHK) Menurut Kelompok Pengeluaran Nasional Tahun 2018

2019 ◽  
Vol 3 (2) ◽  
pp. 110
Author(s):  
Halida Sofiah Noor ◽  
Cucu Komala
Keyword(s):  
Interest Rates ◽  
Price Index ◽  
Consumer Price Index ◽  
Economic Indicator ◽  
Index Number ◽  
Important Indicator ◽  
Goods And Services ◽  
Time Period ◽  
Rate Of Increase ◽  
Rate Of Decline

The Consumer Price Index is an important indicator of the financial market. The Consumer Price Index (CPI) is an index number that describes changes in prices of goods and services consumed by the public in general for a certain period with a predetermined time period. National expenditure according to CPI is divided into four sub-groups, namely the first general sub-group, the second sub-group of foodstuffs the third sub-group of processed foods, beverages, cigarettes and tobacco and fourth sub-group housing, water, electricity, gas and fuel development CPI 2018, every month from January to December tends to increase. Changes in CPI can describe the rate of increase (inflation) or the rate of decline (deflation) of goods or services. CPI can be regarded as a very important economic indicator and is used to represent changes in the average retail price level at the consumer level for a number of certain types of goods and services. The rise in the CPI can lead to an increase in interest rates, increase in money supply growth, increase the attractiveness of currencies, and increase inflation.

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