Dissecting the purchasing managers’ index: Are all relevant components included? Are all included components relevant?

Siliverstovs, Boriss

doi:10.2298/pan150504014s

Cited by 3 publications

(6 citation statements)

References 14 publications

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“…However, the 2008-2009 crisis proved that models built based on PMI can fail (Lombardi and Maier 2011). Therefore, there were allegations that the algorithm for calculating PMI is already outdated and not adapted to the operating conditions in the 21st century (Siliverstovs, 2018). Some authors argue that the methodology for calculating PMI should vary depending on individual countries (Pelaez, 2003;Cho and Ogwang, 2006).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Some authors argue that the methodology for calculating PMI should vary depending on individual countries (Pelaez, 2003;Cho and Ogwang, 2006). Siliverstovs (2018) proposes that PMI should be built using additional subindices, which would allow for more reliable results. At the same time, empirical research indicates that measurable effects that increase the accuracy of economic forecasts follow creation of models based on several sentiment measures (PMI, SPF) (D'Agostino and Schnatz, 2012) or creating relatively advanced econometric models…”

Section: Literature Reviewmentioning

confidence: 99%

“…Researchers use various transformation methods, among which more complex ones can be distinguished, such as mixed-data sampling regression (MIDAS), which allows to study the relationship between incompatible data. MIDAS was relatively often used by the authors when analyzing the relationship of PMI with GDP (Siliverstovs, 2018;D'Agostino and Schnatz, 2012;Chudik, Grossman, and Pesaran, 2014), however, at work it was decided on a different, relatively easy, and effective way to match the data by reducing the readings from monthly to quarterly based on the calculated average of three months per quarter. Based on the possibility of access to historical data of individual indicators, the study used quarterly data, starting from the third quarter of 1998 (1998Q3) to the second quarter of 2019, i.e., 84 observations.…”

Section: The Scope Of Datamentioning

confidence: 99%

“…A clear delay in the publication of economic data over time has forced the search for alternative (to GDP dynamics) measures allowing for publication with a higher frequency and the least delay possible in time (Siliverstovs, 2018). Research conducted in this direction has proved that the expected criteria can be met by indicators based on the sentiments of specific social groups.…”

Section: Introductionmentioning

confidence: 99%

“…With this opinion agreed, among others. Siliverstovs (2018), paying special attention to the need to make changes in economic modelling. Analyzing the subject and direction of scientific research, a certain trend is observed, which is manifested in the fact that on the one hand the role of sentiment indicators in economic forecasting is strengthened, and on the other hand, new possibilities to improve those indicators (Siliverstovs, 2018), that seem to be currently the most developmental are constantly being sought tools created for forecasting in economics.…”

Section: Introductionmentioning

confidence: 99%

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The Relationship between the Purchasing Managers’ Index (PMI) and Economic Growth: The Case for Poland

Sobko¹,

Klonowska-Matynia²

2021

ERSJ

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Purpose: The article aims to analyze and evaluate the relationship between the Purchasing Managers' Index PMI (economic sentiment indicator) and GDP dynamics in the Polish economy. The subject of detailed research was the possibility of forecasting Poland's economic situation using a model built based on the PMI sentiment indicator. Approach/Methodology/Design: The study used data on GDP dynamics, EUR/PLN and USD/PLN exchange rates, as well as two indicators of economic sentiment prepared by independent institutions for Poland: the PMI and ESI indicator. The analysis was based on quarterly data for the period from the third quarter of 1998 to the second quarter of 2019 (84 observations). PMI data came from the bankier.pl website, ESI data from the European Commission database, GDP dynamics data from the World Bank database, and exchange rate information was taken from the stooq.pl website. The analysis contained in the article was performed using the ARDL and ECM models. Findings: The analysis showed that the model based on PMI indicator and the model based on ESI indicator is too inaccurate to be considered a tool for forecasting the economic situation in Poland. It also turned out that extension of the model with other explanatory variables increased its accuracy of fitting to real data. Practical Implications: Even though the estimated models were significantly unreliable, it turned out that in periods of greater economic instability, the PMI model showed better forecasting properties. This indicates the possibility of using the PMI model, e.g., in times of recession or economic crisis. Originality/Value: The article broadened the research perspective for forecasting the Polish economy. The results set the directions for further development of research in this aspect. It turned out that probably the optimal solution would be to create different models for different phases of the business cycle, or a different rate of economic growth.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: The Scope Of Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Relationship between the Purchasing Managers’ Index (PMI) and Economic Growth: The Case for Poland

Sobko¹,

Klonowska-Matynia²

2021

ERSJ

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On the relation between purchasing manager’s index and trade policy uncertainty: evidence from China, Japan and the USA

Shaikh

2021

JCEFTS

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Purpose Trade uncertainty does influence the firm’s new investment, profitability and supply chain finance. Consequently, it results in decreased consumption and low consumer confidence and eventually disrupts global economic activity. This paper aims to propose a model to uncover the effects of trade policy uncertainty (TPU) on the real economic activity and economy’s health measured in terms of the purchasing manager’s index (PMI). Design/methodology/approach This study uses the PMI, trade policy uncertainty index, economic policy uncertainty index and short-term interest rate. The relation between economic activity and uncertainty was studied using nested regression and vector autoregressive model. Findings The empirical results show that PMI of China and Japan were more responsive to the TPU of the USA and remained more fluctuating during the year 2018–2019. Importantly, this paper notices that the US’s PMI reached a low historically subject to its own trade policy and tension with China. Overall, TPU has shown more pronounced effects on PMI across China, Japan and the USA, followed by important economic and political events and major trade tariff uncertainty deals. Practical implications The empirical outcome holds some practical implications trade uncertainty affects not only the economic health of the economy but also market participants, global investors and international political environment, recent trade barriers, tariff wars and ambiguity raise question about free and fair global trade and competitiveness of the member country of the world trade organization. Originality/value The work is a novel that attempts to explain economic activity and supply chain through PMI. Unlike conventional economic indicators, e.g. gross domestic product, producer price index, consumer price index, employment, etc. PMI measures manufacturing industries’ overall status concerning the number of orders, inventory levels, productions, supplier deliveries and employment.

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The relationship between the purchasing managers' index (PMI) with economic and financial indicators in Turkey: A Var analysis

DAYAN,

ERDOGAN

2023

Trakya Üniversitesi İktisadi Ve İdari Bilimler Fakültesi E-Dergi

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In this study, the relationship between Turkey’s Purchasing Managers' Index (PMI) and various economic and financial indicators was investigated. The data for the period 2018.4 - 2021.2 were used in the analysis, and the Vector Autoregression (VAR) method was preferred. According to the results, although there is a significant relationship between various indicators, it has been determined that the most effective index on PMI is the Export Climate Index. A positive relationship was found between PMI and the Export Climate Index in the short run. There is also a unidirectional causality relationship from the Export Climate Index to PMI. This situation has revealed that exports are influential on the investment decisions of purchasing managers, and therefore market-oriented strategies have an important place in business policies. It is thought that this study will make important contributions to policymakers, researchers and literature.

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Dissecting the purchasing managers’ index: Are all relevant components included? Are all included components relevant?

Cited by 3 publications

References 14 publications

The Relationship between the Purchasing Managers’ Index (PMI) and Economic Growth: The Case for Poland

The Relationship between the Purchasing Managers’ Index (PMI) and Economic Growth: The Case for Poland

On the relation between purchasing manager’s index and trade policy uncertainty: evidence from China, Japan and the USA

The relationship between the purchasing managers' index (PMI) with economic and financial indicators in Turkey: A Var analysis

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