Time Series Cointegration Analysis Research Articles

How the Income Elasticity of Meat Consumption differs between social groups? A case of the UK and the Czech Republic

The purpose of the article is to show different consumer behaviour between ten different income levels (deciles) and different countries and to examine the elasticity distance between income deciles in the UK (a high-income country) and the Czech Republic (a low-income country) within the context of meat consumption. The official statistic services provided data in the Czech Republic (Czech Statistical Office, 2020) and the UK (Office for National Statistics, 2020). Data on the Czech Republic come from the household budget surveys (HBS). In contrast, corresponding data on UK consumers was drawn from the Living Costs and Food survey, which succeeded the National food survey and household expenditure survey. Both sets of data were set according to households’ structure from the EU-SILC Survey (national module of the European Union – Statistics on Income and Living Conditions). To estimate the income elasticity of meat in the Czech Republic and the United Kingdom in different consumer income groups, a time series cointegration analysis was applied to analyse the annual data for 2000-2017. The Törnquist equation and the difference between income elasticity in monetary and natural expression show saturation and preference of high quality meat in the higher-income consumers in the UK than the same groups in the Czech Republic and overall increasing demand for quality in other income groups. The results support the theory of nutrition transitions. The value of the research is that it would enable the exploration of the potential impact and nature of fiscal interventions for improving diets whilst enabling food producers to forecast meat consumption within the different customer segments.

Finance as a friend, enemy and stranger in the US Economy, 1952–2019

AbstractThis article aimed to illustrate that the role of the finance sector in an economic system can be explained more systemically and systematically in the context of its interaction with macroeconomic governance, based on the case of the USA from 1952Q1 to 2019Q2. The article introduced two modes of economic governance based on negative and positive institutional complementarities, developed its hypotheses built on a structural analysis of the long-run relationship between the finance sector and macroeconomic governance within the frame of these two modes, and quantitatively tested the hypotheses using time-series cointegration analysis. The article concluded that the finance sector enhanced and impaired the US economic performance, respectively, in the periods 1952Q1–1973Q4 and 1980Q1–2007Q4, and that its long-run relationship with the US economic performance disappeared in the period 2008Q1–2019Q2.

Some aspects of the "dutch disease" in the economy of the Republic of Kazakhstan

The fundamental structural transformations that have taken place in the country's economy have led to a deterioration in the quality of the economic system and increased dependence of the national economy on the conjuncture of world markets. Our previous research was concerned with determining what triggered the change in the structure of the economy, how effective the changes were, and what changes in sector proportions they led to. In this article, we tried to analyze whether we are facing the consequences of the "Dutch disease", which led to structural transformations in the economy.The purpose of the study is to confirm our assumption that the economy of the Republic of Kazakhstan is subject to the "Dutch disease".Methodology of the study. The authors relied on the cointegration analysis of time series using the Engle-Granger tests, the analysis of the stationarity of the Dickey-Fuller methods in order to determine cointegration, to assess the degree of interrelation between the values of mining and manufacturing production volumes on GDP indicators, as evidence of the presence of "Dutch disease".Originality / value of the research. The originality and value of the study lies on the fact that the authors tried to analyze the economy for the presence of "Dutch disease" in the economy of Kazakhstan using time series analysis methods.Findings. There is an assumption that the economy of Kazakhstan has certain symptoms and signs of the "Dutch disease", but it is not possible to say unequivocally that it was the main catalyst for structural changes, due to the fact that we could not prove the existence of a negative relationship between the growth rates of the mining industry and the manufacturing industry.

Sectoral Contribution to Economic Development in India: A Time-Series Co-Integration Analysis

This research paper examines the causal relationship between India’s economic growth and sectoral contribution to Gross Domestic Product (GDP) and vice versa, in the short-run and long-run, over a 10 years time period. Johansen’s method of cointegration is used to study the cointegration between the sectoral contributions to Indian GDP vis-a-vis India’s economic growth. Further, the route of interconnection between economic growth and sectoral contribution is tested by using Vector Auto Regression (VAR) model. Special attention was given for investigating impulse responses of economic growth depending on the innovations in sectoral contribution using time-series data from 1960 to 2015. This paper highlighted a dynamic co-relationship among industrial sector contribution and agricultural sector contribution and economic development. In the long run, one percent change in industrial sector contribution causes an increase of 3.42 percent in the economic growth and an increase of 1.12 percent in the primary sector contribution, while in the short run industrial and service sector contributions showed significant impact on economic development and agriculture sector. The changing composition of sector contribution is going to be an important activity for the policymakers to monitor and control where the technology and integration of sectors play a significant role in economic development.

Are the Economies of Canada and the United States Integrated? Evidence from Cointegration Analysis

ABSTRACTIn recognition of his contributions to the development of the method of cointegration analysis for analyzing nonstationary time series, late Sir Clive William John Granger (September 4, 1934–May 27, 2009) was awarded the Nobel Memorial Prize in Economic Sciences in 2003. Since then, this method has become a dominant paradigm in empirical economic research. However, this method is not without critics. This article is one in a series to point out some inconsistent arguments used in the development of the method of cointegration analysis. To illustrate by example, we apply the method of time series cointegration analysis and present statistical evidence that supports the proposition that the economies of Canada and the United States are integrated. We conclude this article by laying out a foundation to formally criticize the method of cointegration analysis in subsequent research.

Telling a Similar Story Twice? NCVS/UCR Convergence in Serious Violent Crime Rates in Rural, Suburban, and Urban Places (1973–2010)

This study examines UCR and NCVS serious violence crime trends in urban, suburban, and rural areas, and assesses the extent to which discrepancies in the two data series are due to victim reporting or police crime-recording practices. Particular attention is paid to the dynamics of the rural data series. NCVS data for 1973–2010 are used to estimate subnational rates of serious violence and comparable rates for crimes that victims said were reported to police, and these estimates are compared to subnational UCR data. Time-series cointegration analysis is used to assess convergence in the NCVS and UCR series along with descriptive comparative analyses. The degree of convergence in UCR and NCVS trends was found to vary across areas; however this was not due to differences in rates of reporting to police. Suburban and urban UCR and NCVS trends converged with and without adjustment for police reporting. Little evidence of NCVS/UCR series convergence was found in rural areas even after victim reporting was taken into account. The recording and production of crime data by the police appears to contribute to subnational differences in the convergence between the UCR and NCVS series. The findings suggest rural crime trend analysis should not be based solely on UCR data. To illustrate the difference between conclusions based on UCR and NCVS rural violence trends, we find that poverty rates have a large, significant association with rural violence as measured in the NCVS, but are unrelated to UCR rates.

The Effects of Korean and Chinese Economic Growth on Particulate Matter in Korea: Time Series Cointegration Analysis

본 연구는 한국과 중국의 경제성장이 한국의 미세먼지농도에 미치는 영향을 분석한 것이다. 한국의 미세먼지의 발생 원인을 한국과 중국의 화석연료소비로 보고 한국의 미세먼지와의 관계를 확인하고자 64개 분기별 실질GDP와 한국의 81개 도시의 미세먼지농도의 분기별 자료를 이용하였다. 세 시계열에 모두 단위근이 존재하고 공적분 관계가 있음을 확인하여, 오차수정모형을 구성하였다. 모형 추정결과, 한국의 실질GDP의 1%증가는 한국의 미세먼지농도를 4.32%증가시키고, 중국의 실질GDP의 1%증가는 한국의 미세먼지농도를 3.32% 증가시키는 것으로 나타났다. 본 연구는 기후 및 기타 사항을 고려하지 않고 모형화한 것에 한계를 가지며 한국과 중국의 경제성장이 한국의 미세먼지에 미치는 영향을 장기균형식을 통해 계량적 수치로서 확인하는데 의의를 가진다.

Are the Economies of Canada and the United States Integrated? Evidence from Cointegration Analysis

This paper is first in a series to point out some inconsistent arguments advanced by Engle and Granger who first proposed cointegration analysis for analyzing non-stationary time series that has become a dominant paradigm in empirical economic research. To illustrate, we apply time series cointegration analysis and present statistical evidence that supports the proposition that the economies of Canada and the United States are cointegrated. We conclude this paper by laying out a foundation to formally criticize the cointegration analysis in subsequent research. We recommend that, except for pedagogical review of policy failure of historical magnitude, the method of cointegration analysis not be used in any public policy analysis.

Research on the Relationship between Foreign Trade and the GDP Growth of East China—Empirical Analysis Based on Causality

In open economy, development of foreign trade greatly impacts GDP growth. Adopt modern testing methods like unit root, timeseries cointegration analysis and error correction model for researching the causalities between foreign trade including total export and import with the collected 28year statistical data of east China from 1981 to 2008, including total export and total import and GDP growth of east China. The result suggests that there exist long term or short termcausality between GDP and total export and import as well as between GDP and export, foreign trade is the long term and short term reason of GDP growth, but no evidence can prove that there exists long term stationary causality between import trade and GDP. This paper finally provides with some instructive recommendations on how to develop the foreign trade of east China under the new global economy environment.

Impacto en Asturias de las urgencias de atención primaria sobre las hospitalarias: un análisis de cointegración de series temporales

Greater accessibility to the primary care continuing care points (CCP's) could reduce the visits to the Hospital Emergency Services (HES's). This study analyses whether Primary Care can replace and Hospital Services in emergencies. All of the emergency visits (n=6.454.034) made to the HES's and Primary Care CCP's in Asturias and of each one of the healthcare districts within the 1994-2001 period were calculated. The time series were constructed with monthly frequencies for Asturias and each one of the districts, a cointegration analysis having been made to assess whether the two series are inter-replaceable. A mean annual increase of the total number of emergencies in Asturias of 6.2% (CCP: 7,8%; HES: 5.1%) was found, with different growth among the healthcare districts. In the time series cointegration analysis, no replaceability was found between the primary care and hospital emergencies for Asturias and for the healthcare districts, except for the healthcare district of Oviedo, where a 10% growth rate in primary would lower hospital emergencies by 2.7%. The greater accessibility to the Primary Care CCP's increases the use thereof without reducing the visits to the HES's. Therefore, the increase in Primary Cart resources does not seem to be an effective alternative for reducing the visits to the HES's.

Empirical Testing of New Keynesian Phillips Curve in Conditions of the Czech Republic in 1994 - 2003

New concepts have been presented in modelling of inflation dynamics recently, among others the new Keynesian Phillips curve (NKPC). There are several traditional ways of NKPC model validity testing, but none of them seems to be practically applicable in conditions of the Czech Republic. We tried to test the validity of NKPC on the basis of time series. For this purpose we applied an interesting non-traditional method proposed by Demery and Duck. This method does not rely on direct estimation of NKPC parameters, but relatively easy tests based on the cointegration analysis of time series are employed. Its application indicates that the NKPC model cannot be considered as effective in conditions of the Czech Republic; this model does not describe the inflation process sufficiently and it is not a suitable model for inflation prediction or for the choice of appropriate monetary (antiinflation) policy.

Modelling inbound international tourism demand to Portugal

AbstractIn this paper the Johansen cointegration analysis of time series is used to model the Portuguese inbound international tourism demand from five countries of origin—France, Germany, The Netherlands, Spain and UK. This approach examines the long‐run relationships between the demand for holiday visits and the variables that affect holiday travel such as income, destination prices and travel costs (airfares and road costs). Demand functions, for each country of origin, are estimated using annual data on tourism flows from 1975 to 1997. Copyright © 2002 John Wiley & Sons, Ltd.

CATS in RATS: cointegration analysis of time series: version 1.01

CATS in RATS: cointegration analysis of time series: version 1.01