Producer Price Index Research Articles

Viscoelastic or Viscoplastic Theory (VGT #84): A Comparison Study of the Influences on the Gross Domestic Product (GDP), Inflation Rate, and Consumer Price Index (CPI) in the USA Resulting from Consumer Psychological Reactions and Spending Behavior Changes over 9-Quarters During the COVID-19 Pandemic Period from Y2020Q1 to Y2022Q1 Based on GH-Method: MathPhysical Medicine, Especially the VGT Energy Tool (No. 674)

The author is a mathematician and engineer who has conducted medical research work over the past 13 years. Thus far, he has written 670 medical research papers. Beginning with paper No. 578 dated 1/8/2022, he wrote a total of 80 medical research articles using the viscoelasticity and viscoplasticity theories (VGT) from physics and engineering disciplines on 80 different medical problems with their associated data. These papers aim to explore some hidden biophysical behaviors and provide a quantitative understanding of the inter-relationships of a selected medical output (symptom) versus either singular input or multiple inputs (root causes, risk factors, or influential inputs). The hidden biophysical behaviors and possible inter-relationships exist among lifestyle details, medical conditions, chronic diseases, and certain severe medical complications, such as heart attacks, stroke, cancers, dementia, and even longevity concerns. The chosen medical subjects with their asso- ciated data, multiple symptoms, and influential factors are “time-dependent” which means that all biomedical variables change from time to time because body living cells are dynamically changing. This is what Profes- sor Norman Jones, the author’s adviser at MIT, suggested to him in December 2021 and why he utilizes the VGT tools from physics and engineering to conduct his medical research work since then. From 1980 to 1981, the author attended a college in California for his MBA degree, emphasizing finance and marketing. In addition, he spent many years managing a successful high-tech semiconductor business in Silicon Valley, where it involved many key factors of economics and finance, such as gross domestic product (GDP), inflation rate, consumer price index (CPI), NASDAQ stock performance, price/earnings ratio (P/E ratio), various investment decisions, return on investments (ROI), etc. As a result, he has a strong background in money topics associated with finance and economics.

The impact of technological innovation on renewable energy production: accounting for the roles of economic and environmental factors using a method of moments quantile regression

Advancement in renewables is one of the most effective techniques for sustained long-term development, and nations across the globe are making efforts to change their economic and industrial structures in a bid to boost green growth. With the advent of the Fourth Industrial Revolution (4IR), the availability, access, and use of green technologies including renewable energy have significantly improved. Researches on the factors that influence renewable energy production are available. However, we are unaware of any previous research that examines the role of renewable energy innovation in the promotion of renewable energy production. As a result, this study evaluates the impact of technical innovation on green growth from 1993 to 2018, while accounting for real GDP, producer price index, and CO2 emissions. Due to their pivotal status among the developing countries, our study has focused on the BRICS countries. By using a new panel quantile regression augmented with the method of moments, the empirical findings suggest that the influence of renewable energy innovation on renewable energy production is significantly positive across all quantiles. Moreover, the coefficients are generally bigger at the small quantiles, which suggests that countries with smaller renewable energy production per capita (India and South Africa) have a higher probability to experience a greater impact of renewable energy innovation per capita than countries with bigger renewable energy production per capita (Brazil and Russia).

VEC 모형을 활용한 지방 주택중개시장의 영향 요인 및 활성화 연구

The study analyzed the effects of changing economic and demographic variables such as population, interest rate and consumer price index on house brokerage markets by focusing on gradually sluggish housing markets in Jeollabuk-do For methods, a time series multiple regression analysis using Vector Error Correction Model (VECM) was employed in that the number of house brokerage and input variables have characteristics observed in a time series way. This study suggested the improvement schemes of housing brokage market as follows; First, it is necessary to take into account regional differences in terms of population movement, outflow, inflow and aging. Second, house brokerage markets in local cities are affected by gross regional domestic product and consumer price index so that it is important to take a look at changing commodity price as well as varying economic situation. As the patterns of economic development vary according to regions, it is necessary to consider this point from which house brokerage markets could be immensely affected. Third, the need to forecast the potential effects of urban regeneration new deal projects underway on house brokerage markets should be considered given regional differences. Lastly, the recession of house brokerage markets can be seen as natural as its value as investment goods is underrated in that there is a right balance between demand and supply of houses in economically advanced South Korea. Against this backdrop, developing both house brokerage markets and real estate management markets will be an important breakthrough like advanced countries.

Strengthening relations between the countries of the Eurasian Economic Union at the stage of transformation of the geo-economic system

In the context of modern conditions of foreign economic relations between the countries of the Eurasian Economic Community, they are being transformed under the influence of external challenges and threats. The pandemic temporarily paralyzed intercountry relations in the integration group. Despite this, the official institutions of the EAEU are developing new programs and strategic directions for the further development of this association. The purpose of the study is to assess the economic positions of the EAEU countries in international markets and to study individual strategic directions for the development of Eurasian integration until 2025. As a theoretical and methodological basis, the article uses the statistical, historical and logical method, dialectical principles and contradictions, the method of scientific abstraction. On this basis, the current state of the countries of the Eurasian Economic Union is analyzed, the indicators of international trade for 2017-2020 are assessed, the export and import range, proportion of imports covered by exports and etc. Particular attention is paid to the dynamics of industrial producer price indices for 2016-2020 and on this basis, a forecast for this indicator is given. It is noted that the share of innovative products of Russia in the total volume of shipped products of its own production is decreasing every year, which significantly affects the country’s position in international markets. It was noted that in order to strengthen the relationship of the EAEU countries at the stage of transformation of the geo-economic system, it is necessary to comprehensively and stage-by-stage implement the Strategic Directions for the Development of Eurasian Economic Integration.

Relationship between Foreign Portfolio Investment and Exchange Rate: The Case of India Using VECM Testing Approach

India attracts a large sum of foreign investments every year. These foreign investments have a remarkable impact on Indian economy. Real economy is assumed to be affected by foreign investment through its constituents like exchange rate, foreign exchange reserves, economic growth, etc. Exchange rate movements are also believed to affect the foreign portfolio investment, especially foreign institutional investment coming to the country. The unit root test is applied to determine stationary of the time series data. Vector error correction model is applied to determine the dynamics of the relationship between foreign investment and exchange rate. The result shows that foreign portfolio investment, foreign direct investment, index of industrial production, interest rate and wholesale price index have a positive impact on the exchange rate (REER), that is, Indian rupee appreciates whereas import has negative impact on exchange rate in India that is, Indian rupee depreciates. In short, foreign portfolio investment in India may lead to rupee appreciation with several other currencies and their selling and disinvestment may lead to its depreciation.

Causality in Relation to Futures and Cash Prices in the Wheat Market

Mutual interactions between the agricultural commodities futures market and the spot market are some of the most important relationships that can be observed between the financial market and the real economy. The process of the flow of price impulses between these markets has long been of interest to researchers. It is important both for stock market investors and agricultural producers, as well as for decision-makers responsible for food policy. The studies conducted so far do not provide unambiguous answers about the strength and direction of flows of price impulses. This is one example of the need for continuous process monitoring. Therefore, the aim of the study was to identify the process of the flow of price impulses between the futures market and the wheat spot market. The study was carried out based on the US example. The futures prices from the CBOT exchange and the wheat producer price index were analyzed. The data covers the period between 01.2010 and 01.2022. The Granger causality test and the VAR model were used for the study. The results are in the minority, as a causal relationship was obtained from the spot market to the futures market, which is a less common result.

Analysis of the Economic Impact of International Crude Oil Price Fluctuations on China

This paper analyzes and examines the impact of international crude oil price fluctuations on China's economy. By constructing an SVAR model between crude oil prices and China's economy, prices and monetary policy, we study the impact of crude oil price fluctuations on some important economic indicators in China and explore their intrinsic relationships. The empirical results show that the increase in crude oil prices has a significant positive impact on the consumer price index and producer price index; it has a negative impact on industrial production in the short term, but fails to change its overall development trend. Overall, the economic system based on crude oil prices is stable, despite the fact that international crude oil prices are influenced by various complex and variable international factors.

An EEMD-CNN-BiLSTM-attention neural network for mixed frequency stock return forecasting

The regularly issued low frequency data, such as the change of fund position (weekly), and Producer Price Index (monthly), can affect the subsequent trend of stock returns. However, the forecasting effect of low frequency data on high frequency has not been discussed amply. This paper proposes a new mixed frequency neural network that helps to fill this research gap. The original time series is decomposed into several components through ensemble empirical mode decomposition, then the frequency alignment method is applied to integrate the high frequency component with low frequency variable as inputs, and the CNN-BiLSTM-Attention network completes the remaining forecasting work. The empirical results show that compared with other benchmark models, the proposed procedures perform better when predicting the high frequency components and obtain a smaller statistical error in the final ensemble results. The proposed model has great potential for the forecasting of reverse mixed time series.

Propelling steady growth and high‐quality development through deeper reform and more comprehensive opening up: Outlook, policy simulations, and reform implementation—A summary of the Annual SUFE Macroeconomic Report (2021–2022)

AbstractChina's economy underwent a steady recovery in 2021. Investment grew steadily with structural improvement. Exports and imports surged while trade surplus expanded. On the other hand, although labor market conditions improved, income distribution worsened, contributing to sluggish growth in consumption, whereas the gap between consumer price index and producer price index widened, and the profits of enterprises of different sizes diverged, which may go beyond how they are correlated with the locations of the enterprises in the chain of production and trade. While proper liquidity was maintained with prudent monetary policy, risk spillover rose in the financial system, particularly for small and medium‐sized banks. Household and local government debts remained at relatively high levels, further dragging down growth in consumption and infrastructure investment. The “dual carbon” goals exerted downward pressure on near‐term growth in trading off their long‐term benefits. The economy also faced challenges in its external environment in the midst of the prolonged COVID‐19 pandemic aboard, trade protectionism, and the readjustment of the global value chain. Moreover, excessive supervision and inadequate implementation disturbed China's economy, resulting in declined market vitality and confidence of market participants. Based on the Institute for Advanced Research‐China Macroeconomic Model, the baseline real gross domestic product growth rate is projected to be 5.5% in 2022. Alternative scenario analyses and policy simulations are conducted, in addition to the benchmark forecast, to reflect the influences of various risks and possible favorable situations. The findings suggest that China should deepen reform and open up more comprehensively and initiatively, while special effort should be placed on providing accommodative policy and friendly public opinion environment, to facilitate steady growth and propel high‐quality development. A comprehensive macroeconomic governance framework with Chinese characteristics must be developed from systems thinking, to resolve the various issues, internal and external, cyclical and secular, structural and institutional, in an all‐inclusive and coherent manner.

Examining comovement and causality between producer price index for P&C insurance premium and uncertainty indices: Wavelet and non-parametric quantile causality approach

This study examines the impact of the news-based climate policy uncertainty index (CPU) on PPI-P&C. To understand the impact of different types of uncertainty on P&C insurance premiums, the study also examines the relation of economic policy uncertainty (EPU), and geopolitical risk uncertainty (GPU) with PPI-P&C index. The time-frequency relation between the indices is examined using wavelet coherence analysis (WCA), whereas the casual dependency is examined using the non-parametric causality in quantiles (CIQ) approach and linear and non-linear Granger causality tests. WCA shows significant co-movement phases between CPU and PPI-P&C across time-frequency domain with CPU leading the PPI-P&C over a specific time interval. Results from CIQ give evidence of uncertainty indices having an asymmetric significant dependency relation with the PPI-P&C index. The results have implications for examining the impact of rising uncertainties on rising insurance costs for P&C insurance providers.

Faktor-faktor Penentu Harga Tembaga di Pasar Internasional Terhadap PT. Freeport Indonesia

This study aims to determine the factors that influence copper prices in the international market. The data used is monthly starting from January 2005 to December 2019. The variables are Copper price, Industrial Producer Index (IPI), Producer Price Index (PPI), Fed Rate, Consumer Price Index (CPI), New York Stock Exchange (NYSE), Oil Price, and Gold Price sourced from the internet. The research data was processed using the VECM method with Eviews 10 software. From data processing, the results were obtained: there is a two-way causality relationship between CPI and copper prices, gold prices and IPI have a one-way causality relationship to copper prices, and copper prices have a one-way causal relationship to PPI. The shock that occurred in the IPI variable gave the largest impulse response in the short and long term to the copper price in addition to the shock that occurred by the copper price itself. The shock was given by the fed rate, gold price, and CPI only gives an impulse response effect in the long term. In the short term, the largest decomposition variance was contributed by IPI and the price of copper itself, while in the long term the largest decomposition variance was contributed by IPI, fed rate, gold price, CPI, and also the price of copper itself. From the results of the study, it was concluded that IPI, Fed Rate, gold price, and CPI variables were the most influential factors in changes in copper prices. Keywords: causality, copper price, impulse response, variance Decomposition,  VECM

The energy, environment and economy impact of coal resource tax, renewable investment, and total factor productivity growth

China is the biggest emitter in the world, and coal is dominant in China's energy structure. At present, China has used many strategies to reduce coal use, but their cost-effectiveness is controversial. This paper applies the China-Energy-Environment-Economy Analysis (CEEEA) model, a dynamic recursive computable general equilibrium model with multi-sectors and multi-residents, to simulate progressive policy mix to reduce coal consumption and CO2 emission during 2020–2030, and presents the impact on energy, environment, and economy. The results show that the coal resource tax and the renewable investment can significantly reduce coal share and CO2 emissions to different degrees. The producer price index increases a lot in coal-related industries. However, the consumer price index only increases by no more than 1%. After all, energy products account for a small proportion of the supply chain of bulk commodities. Another, this paper tests the boundary of Porter Hypothesis of such policy mix: it only requires an additional 0.09% increase of total factor productivity every year to cover the negative economic impact of the taxes.

PPI and CPI seasonal adjustment during the COVID-19 pandemic

The U.S. Bureau of Labor Statistics publishes seasonally adjusted Consumer Price Index (CPI) and Producer Price Index (PPI) data monthly. Seasonal adjustment removes within-year seasonal patterns from data. To seasonally adjust data and estimate seasonal patterns of time series, the CPI and PPI use a filter-based approach that employs moving averages of historical data. In 2020, many PPIs and CPIs experienced extreme movements because of the coronavirus disease 2019 (COVID-19) pandemic. For example, the PPI and CPI for gasoline decreased 53.0 percent and 16.5 percent in April 2020, respectively. Because the CPI and PPI use historical data to estimate seasonal patterns, the extreme price movements in 2020 could have adversely affected the capability of the two price programs to accurately estimate seasonally adjusted data. This article explains how the CPI and PPI mitigated the effects of the COVID-19 pandemic on their seasonally adjusted price indexes. Mitigation steps included identifying price indexes whose movements were affected by the pandemic, estimating time series models to quantify these effects, and removing pandemic-related price movements from the data before estimating seasonal patterns.

Forecasting Pakistan stock market volatility: Evidence from economic variables and the uncertainty index

This study examines the impact of the economic policy uncertainty index (EPU) and macroeconomic variables on the volatility of the Pakistan stock market using the GARCH-MIDAS (mixed data sampling) model. The model allows us to observe whether those variables contain valuable information to forecast stock market volatility. Our empirical findings show several outcomes. First, our out-of-sample results show economic policy uncertainty index has predictive power to forecast Pakistan stock market volatility. Second, among all variables, oil prices are the most powerful predictor of volatility with a higher out of sample R square value. Third, all macroeconomic variables including exchange rate, short-term interest rate, money supply M2, foreign direct investment, gold prices, inward remittances, industrial production, and consumer price index (proxy for inflation) contain useful information for stock market volatility forecasting. However, the long-run interest rate is an ineffective indicator of volatility during the sample period study. Finally, we find that the combination forecast information is also useful for volatility forecasting.

How macro-variables drive crude oil volatility? Perspective from the STL-based iterated combination method

Based on the GARCH-MIDAS framework, this article mainly explores whether the Seasonal and Trend decomposition using Loess (STL decomposition) and the iterated combination approach can improve the prediction accuracy of crude oil price volatility by using macro variables. We introduce five macro variables, that is, consumer price index, interest rate, producer price index, industrial production index, and unemployment rate, into the long-term component in the GARCH-MIDAS model. Though the model including raw macro variables performs better than the model containing each STL-based single subsequence, we find that the iterated combination forecasts using the forecasts obtained by each subsequence are superior to the corresponding standard ones. Besides, when mean-variance investors perform asset allocation, they can obtain more certainty-equivalent-returns by applying our new iterated combination approach. Finally, various robustness checks can verify the above-mentioned findings. In short, this article may provide a new perspective for economic empirical applications and theoretical research.

Biaya Produksi, Biaya Penjualan, Daya Beli dan Laba Bersih pada Perusahaan Manufaktur Periode 2020

This research aims to determine the effect of production costs and sales costs on purchasing power and net income in manufacturing companies listed on the Indonesia Stock Exchange for the period 2020. The research method used is quantitative methods with descriptive and associative approaches, where the type of data used is secondary data from financial statements of manufacturing companies listed on the Indonesia Stock Exchange for the 2020 period and the value of the Consumer Price Index (CPI) and Producer Price Index (PPI) as indicators of purchasing power. The total population in this study amounted to 195 manufacturing companies. The sampling technique used is purposive sampling technique, from these results obtained a sample of 36 manufacturing companies with a period of 4 quarters during 2020 so that the number of samples used is 144 samples. The data analysis method used in this study is multiple regression analysis method with path analysis model using the help of the IBM SPSS 26 application. Based on the t test, it is known that in equation one partially production costs have a positive effect on purchasing power and sales costs have a negative effect to purchasing power. While in equation two, production costs have a negative effect on net income, while sales costs and purchasing power have no effect on net income. The results of the path analysis show that purchasing power is not an intervening variable or there is no mediating effect between the relationship between production costs and selling costs on net income.

The Geometric Young Formula for Elementary Aggregate Producer Price Indexes

Abstract We re-estimate historical U.S. Producer Price Indexes (PPI) using the geometric Young formula at the elementary level. The geometric Young has better axiomatic properties than the modified Laspeyres, and may better approximate a feasible economic target. We find in most cases, indexes that use the geometric Young escalate between 0.1 and 0.3 percentage points less each year than those that use the modified Laspeyres. However, for wholesale and retail trade, as well as some other services, the differences are much larger. As a result, using the geometric Young at the elementary level lowers the U.S. PPI for Final Demand by 0.55 percentage points per year during the study period, a magnitude larger than what has been previously found for the U.S. Consumer Price Index.

Impact of China’s National Sword Policy on the U.S. Landfill and Plastics Recycling Industry

This paper analyzes the impacts of China’s Green Fence and National Sword Programs, under which strict contamination limits were imposed on recyclable materials, besides prohibiting imports of low quality recyclables. Specifically, this study investigates the impacts of this policy on landfills, and the risks to the U.S. plastics secondary materials market and material recovery facilities (MRFs). A hierarchical regression analysis reveals the significant impacts of China’s Green Fence and National Sword polices on the amount landfilled plastic. Controlling for oil prices, producer price index (PPI), and amount of plastic scrap exported, our findings show that the Green Fence had no statistically significant impact on the amount of plastic landfilled in the U.S. However, the quantity of plastic landfilled in the U.S. increased by 23.2% following the implementation of National Sword. Furthermore, analysis of the annual reports submitted by registered MRFs in New York (NY) state reveals that the total amount of plastic recovered by them has decreased. We suggest that demand creation and investments that improve the quality of bales are needed to help solve this economic dilemma.

Asset allocation efficiency from dynamic and static strategies in underfunded pension funds

This study attempts to conduct a comparative analysis between dynamic and static asset allocation to achieve the long-term target return on asset liability management (ALM). This study conducts asset allocation using the ex ante expected rate of return through the outlook of future economic indicators because past economic indicators or realized rate of returns which are used as input data for expected rate of returns in the “building block” method, most adopted by domestic pension funds, does not fully reflect the future economic situation. Vector autoregression is used to estimate and forecast long-term interest rates. Furthermore, it is applied to gross domestic product and consumer price index estimation because it is widely used in financial time series data. Based on asset allocation simulations, this study derived the following insights: first, economic indicator filtering and upper-lower bound computation is needed to reduce the expected return volatility. Second, to reach the ALM goal, more stocks should be allocated than low-yielding assets. Finally, dynamic asset allocation which has been mirroring economic changes actively has a higher annual yield and risk-adjusted return than static asset allocation.

Analysis of Construction Cost and Investment Planning Using Time Series Data

Construction costs and investment planning are the decisions made by construction managers and financial managers. Investment in construction materials, labor, and other miscellaneous should consider their huge costs. For these reasons, this research focused on analyzing construction costs from the point of adopting multivariate cost prediction models in predicting construction cost index (CCI) and other independent variables from September 2021 to December 2022. The United States was selected as the focal country for the study because of its size and influence. Specifically, we used the Statistical Package for Social Sciences (SPSS) software and R-programming applications to forecast the elected variables based on the literature review. These forecasted values were compared to the CCI using Pearson correlations to assess influencing factors. The results indicated that the ARIMA model is the best forecasting model since it has the highest model-fit correlation. Additionally, the number of building permits issued, the consumer price index, the amount of money supply in the country, the producer price index, and the import price index are the influencing factors of investments decisions in short to medium ranges. This result provides insights to managers and cost planners in determining the best model to adopt. The improved accuracies of the influencing factors will help to enhance the control, competitiveness, and capability of futuristic decision-making of the cost of materials and labor in the construction industry.