Macroeconomic Policy Formulation Research Articles

China’s GDP-at-Risk: Real-Time Monitoring, Risk Tracing, and Macroeconomic Policy Effects

Timely monitoring GDP-at-risk and tracing economic downside risk sources can help establish effective risk warning and prevention systems. This study constructs a probability distribution for China’s economic growth with skewness determined by a multidimensional predictor information set of macro fundamentals. Such a treatment allows us to identify changing drivers of economic downside risks during monitoring GDP-at-risk’s dynamic evolutionary path. We also employ a time-varying parameter vector autoregression model with random volatility to explore the heterogeneous impacts of different macroeconomic policy instruments on economic slowdowns. Our results provide empirical support for macroeconomic management and policy formulation in emerging markets. We reach three conclusions. First, the dynamics of GDP-at-risk exhibit significant event-driven characteristics, and economic downside risk increases significantly under the influence of extreme events. Moreover, the probability distribution of economic growth is asymmetric--as the downside risk of the economy increases, its upside potential increases disproportionately. Second, the time-varying risk trace of GDP-at-risk shows that the contribution of financial conditions and local government debt to economic downside risk declines. The importance of the risk-driving role of housing price growth gradually increases, suggesting that China’s property prices can provide more valuable early warning information about future growth risk, allowing time for precise preventive measures. Nevertheless, interest rates and inflation as risk divers have consistently minimal impacts. Third, the heterogeneity impulse response function of GDP-at-risk suggests that quantity-based monetary policy and fiscal policy can manage economic downside risks in the short run. In contrast, price-based monetary policy can curb economic overheating and reduce downside risks in the medium to long term. Therefore, the effect of price-based monetary policy is more sustainable in China.

Advancing macroeconomic models through artificial intelligence integration

The integration of Artificial Intelligence (AI) into macroeconomic models marks a significant evolution in the field of economic forecasting and analysis. Traditional macroeconomic models, often constrained by linear assumptions and a limited set of variables, struggle to accurately capture the complexities of the global economy. This paper explores how AI, particularly machine learning algorithms like Random Forests and Neural Networks, enhances these models by processing and interpreting vast and diverse datasets, including unstructured data such as social media sentiment and news analysis. AI's capability to adapt and learn from new data enables dynamic models that remain relevant and accurate amidst changing economic conditions. By addressing non-linearities and enhancing model robustness, AI provides a more nuanced understanding of economic dynamics, uncovering intricate patterns missed by traditional analyses. The implications of AI-enhanced macroeconomic models are profound, offering more reliable foundations for economic research and policy-making. This paper argues that the integration of AI into economic modeling not only improves the precision of economic forecasts but also enriches the field of economic research and the formulation of more effective policy interventions. Through examples such as inflation rate forecasting and the identification of complex, non-linear economic relationships, this study highlights the transformative potential of AI in macroeconomic analysis and policy formulation.

Analysis of the Current Situation of Female Employment from a Macroeconomic Perspective: A Case Study of Female Employment During COVID-19

The underlying causes of the relative economic downturn in all parts of the world since the outbreak of COVID-19 (Coronavirus Disease 2019) deserve deeper scrutiny. This paper focuses on the employment situation of women during the above-mentioned period, including a comparison between the extent to which non-cyclical economic downturns have affected employment for both sexes and the impact of COVID-19 on macroeconomic policies by using qualitative research. This paper first judges COVID-19 to be fundamentally different from the downward economic phase of the traditional business cycle, and then it considers the opportunities and pressures on women's employment from a variety of factors, including supply and demand. The paper also discusses in detail the ways in which women were able to find employment during the epidemic, so as to provide an effective and reliable basis and reference for the formulation of national macroeconomic policies and the recovery of national production. It can be concluded that female employment is more significantly affected by the macro environment, and without formulating correct macro policies, it is impossible to bring about the recovery of female employment.

Housing prices in South African economy: Investigating macroeconomic drivers using time-varying approach

Despite being one of the least liquid assets, housing is unarguably the most significant asset class and the biggest contributor to household wealth. The goal of the current empirical research is to examine the macroeconomic drivers of housing prices. Thus, the study examines the impact of macroeconomic variables and oil prices on housing prices using monthly time series data from 2003(M1) to 2023(M3). Generalized Autoregressive Score Model (GAS) is employed to appraise the time-varying relationships between housing prices and macroeconomic variables, as well as housing prices and oil prices. The findings resulting from the empirical analysis indicate that the housing market in South Africa is substantially influenced by macroeconomic factors and oil prices. Therefore, the South African government and policy makers must maintain strong macroeconomic policy formulation and environment as they have an impact on housing prices, which, in turn, have an impact on the welfare and quality of life of the populace.

ASSESSMENT OF THE CONNECTION BETWEEN THE BANK'S CAPITALIZATION LEVEL AND THE COUNTRY'S MACROECONOMIC STABILITY

A well-capitalized banking system is crucial for maintaining macroeconomic stability, preventing financial crises, and bolstering the economy's resilience to shocks. Governments often strive to ensure adequate bank capitalization to foster stable economic growth. This article aims to assess the relationship between bank capitalization and macroeconomic stability in 34 European countries from 2010 to 2021, based on World Bank statistics.The study utilizes the principal components method to identify relevant indicators of bank capitalization and macroeconomic stability, canonical analysis and regression analyses to detail the interconnections between these blocks. The canonical analysis confirms a link between bank capitalization and macroeconomic stability indicators with a coefficient of determination of 0.617 signifying that 61.8% of the variance in macroeconomic stability is explained by fluctuations in bank capitalization.The article presents one fixed-effect and two random-effect regression models detailing the directions and strength of influence of independent variables (NPL, ROA, ROE - indicators of the bank capitalization level) on dependent variables (INFLATION, UNEMPL, GINI - indicators of macroeconomic stability). The Wald criteria and a p-value less than 0.05 indicated that the models with random effects (UNEMPL, GINI) were statistically significant.The results reveal that a 1% increase in non-performing loans correlates with a 0.25% rise in the unemployment rate, and a 1% increase in return on assets leads to a 0.08% increase in the unemployment rate. Additionally, a 1% increase in non-performing loans raises the Gini index by 0.05%, while a 1% increase in return on equity decreases the Gini index by 0.03%. Notably, the impact of return on assets on the unemployment rate and the Gini coefficient is not statistically significant (p-value greater than 0.05).These results can inform the forecasting of national indicators, the development of tools to ensure sufficient bank capitalization, and the formulation of effective macroeconomic policies, taking into account fluctuations in banks' capitalization levels as key financial intermediaries.

Exploring and predicting China’s consumer price index with its influence factors via big data analysis

The consumer price index (CPI) is an important indicator to measure inflation or deflation, which is closely related to residents’ lives and affects the direction of national macroeconomic policy formulation. It is a common method to discuss CPI from the perspective of economic analysis, but the statistical principles and influencing factors related to CPI are often ignored. Thus, the impact of different types of CPI on China’s overall CPI was discussed from three aspects: statistical simulation, machine learning prediction and correlation analysis of various types of influencing factors and CPI in this study. Realistic data from the National Bureau of Statistics from 2010 to 2022 were selected as the analysis object. The Statistical analysis showed that in 2015 and 2020, CPI had a fluctuating trend due to the impact of education and transportation. Four types of statistical models including Gauss, Lorentz, Extreme and Pearson were compared. It was determined that the R2 fitted by Extreme model was higher (R2 = 0.81), and the optimal year of simulation was around 2019, which was close to reality. To accurately predict the CPI, the results of Support Vector Machine, Regression decision tree and Gaussian regression (GPR) were compared, and the GPR was determined to be the optimal model (R2 = 0.99). In addition, Spearman matrix analyzed the correlation between CPI and various influencing factors. Herein, this study provided a new method to determine and predict the changing trend of CPI by using big data analysis.

Determinants of Short- and Long-Term Commercial Lending Rates in Peru, 2010–2022

Short- and long-term interest rates are crucial for investment and, thus, business dynamism. The injection of money depends on the ability to promote cash flows through credit which boosts productive sectors. In the last decade, among Latin American countries, Peru has stood out for its key financial system and macroeconomic stability characteristics. The Central Reserve Bank of Peru (BCRP) plays a key role in setting the reference rate given its autonomy and strong institutional trust. However, the mixed pension system injects liquidity into the financial system through its private contributions. For the purposes of this study, an autoregressive econometric model of distributed lags (ARDL) was employed. The findings reveal that the reference rate set by the BCRP establishes a long-term relationship for both short and long-term rates, empirically demonstrating the effectiveness of monetary policy. Additionally, the liquidity provided by the pension system triggers an immediate shock response in determining the short-term rate. It is also observed that the issuance of sovereign bonds is a robust tool in determining the long-term rate due to its facilitation of credit access. This research is relevant for identifying macroeconomic and financial variables and guiding the formulation of macroeconomic policies.

The Impact of the Age Structure of the Population and the Price of Housing on the Savings of the Population: An Empirical Study from China

Purpose: The purpose of this paper is to explore the relationship between housing prices, population age structure and savings rate by analyzing the variables of housing prices, population age structure and savings rate, which is of great theoretical and practical significance for the formulation of policies to alleviate the aging population and childlessness in China. Theoretical framework: Most of the studies on the relationship among the consumer price of commercial housing, the old-age dependency ratio, the child dependency ratio, and the savings rate of the residents in China are based on the GMM model, while this study constructs a quadratic VAR model to empirically analyze and test the four variables of the consumer price of commercial housing, the old-age dependency ratio, the child dependency ratio, and the savings rate of the residents, and to study whether there is a long-term stable relationship among these four variables and other general patterns that exist among these four variables. other general laws that exist among these four variables. Design/Methodology/Approach: This study decides to start from the perspective of residents' saving rates. It conducts descriptive statistical analysis and empirical analysis by using 33 macroeconomic data (samples) from 1990-2022, official data of the National Bureau of Statistics (NBS), and housing price data from Jurassic Data Network (JDN). The hypotheses are initially set up by descriptive analysis, and then the VAR function model is constructed to analyze and test the hypotheses empirically. Findings: It is found that the sales price of commercial housing is positively correlated with the savings rate of residents; the child dependency ratio is positively correlated with the savings rate of residents; the old-age dependency ratio is insignificantly correlated with the savings rate of residents; and there is a long-term and stable correlation among the four variables of the sales price of commercial housing, the child dependency ratio, the old age dependency ratio and the savings rate of residents. Research, Practical & Social implications: This study improves the relevant theoretical research of economics, demography, sociology and other interdisciplinary disciplines, complements the existing research paths and methods of empirical analysis, and obtains relevant empirical research conclusions. The significance of the study is to provide assistance to the formulation of national macroeconomic and monetary policies, and to contribute to the adjustment of population policies, the reform of the pension security system, and the revision of the medical security system. It also contributes to the adjustment of the population policy, the reform of the pension security system, and the revision of the medical security system. Originality/Value: The study is able to enrich the research practices on the sales price of commercial properties, the dependency ratio of young children, the dependency ratio of the elderly and the savings rate of the population, and make a new contribution to the improvement of people's livelihood, the development of the real estate industry's transformation and upgrading, and the formulation of rational population and economic policies.

The prediction of Chongqing's GDP based on the LASSO method and chaotic whale group algorithm–back propagation neural network–ARIMA model

Accurate GDP forecasts are vital for strategic decision-making and effective macroeconomic policies. In this study, we propose an innovative approach for Chongqing's GDP prediction, combining the LASSO method with the CWOA—BP–ARIMA model. Through meticulous feature selection based on Pearson correlation and Lasso regression, we identify key economic indicators linked to Chongqing's GDP. These indicators serve as inputs for the optimized CWOA–BP–ARIMA model, demonstrating its superiority over Random Forest, MLP, GA–BP, and CWOA–BP models. The CWOA–BP–ARIMA model achieves a remarkable 95% reduction in MAE and a significant 94.2% reduction in RMSE compared to Random Forest. Furthermore, it shows substantial reductions of 80.6% in MAE and 77.8% in RMSE compared to MLP, along with considerable reductions of 77.3% in MAE and 75% in RMSE compared to GA–BP. Moreover, compared to its own CWOA–BP counterpart, the model attains an impressive 30.7% reduction in MAE and a 20.46% reduction in RMSE. These results underscore the model's predictive accuracy and robustness, establishing it as a reliable tool for economic planning and decision-making. Additionally, our study calculates GDP prediction intervals at different confidence levels, further enhancing forecasting accuracy. The research uncovers a close relationship between GDP and key indicators, providing valuable insights for policy formulation. Based on the predictions, Chongqing's GDP is projected to experience positive growth, reaching 298,880 thousand yuan in 2022, 322,990 thousand yuan in 2023, and 342,730 thousand yuan in 2024. These projections equip decision-makers with essential information to formulate effective policies aligned with economic trends. Overall, our study provides valuable knowledge and tools for strategic decision-making and macroeconomic policy formulation, showcasing the exceptional performance of the CWOA–BP–ARIMA model in GDP prediction.

Advancing the comprehension of consumer price index and influencing factors: insight into the mechanism based on prediction machine learning models

The consumer price index (CPI) is an important indicator to measuring inflation or deflation. Its changes are closely related to residents' lives, and also affect the direction of national macroeconomic policy formulation. In recent years, the combination of economics with mathematical models such as machine learning and macro-statistics has gradually become a hot topic. In this paper, the impact of different types of CPI on China's overall CPI was discussed. Machine learning prediction and correlation analysis of various types of influencing factors and CPI. The machine learning model of the regression decision tree process predicted CPI more accurately. Spearman correlation analysis showed that CPI was mainly positively related to goods and services, living, medical care, food, tobacco and alcohol, clothing and so on, while CPI was mainly negatively related to the transport and communication of residents.

INSTANTANEOUS PRODUCTION GROWTH IN A DYNAMIC ECONOMY

The purpose of this study is to measure the average and instantaneous rates of production growth and to provide new formulas for measuring the contribution to and share of resources in that growth. This is the key idea in a new method of attack as suggested by Cobb and Douglas—as of yet not solved by economic growth theory—to demonstrate that distribution processes are modeled on those of the production of value. We intend to validate the strength of the proposed model by systematizing and analyzing production, investment and employment data from a dynamic economy, i.e., the United States of America, provided by the Bureau of Economic Analysis (BEA) and the Federal Reserve (Fed) for the years 2012 to 2022, the results of which can be found in the Appendix. We conclude that greater convergence with equity between labor and capital contribution and share in production growth results when the elasticity of its composition is between ½ and 1. Outside of this range, we see divergence with inequity adverse to labor. Our model could improve the formulation and execution of macroeconomic policies (fiscal, monetary and trade) and promote greater convergence/equity in the contribution to and share of resources in production growth.

Unemployment and Economic Growth: Testing the Validity of Okun’s Law in Nigeria

This study explores the dynamic relationship between unemployment and Gross Domestic Product (GDP) growth in the context of the Nigerian economy from 1991 to 2021. Guided by Okun’s Law, which posits a negative correlation between these two variables, rigorous econometric analysis was employed to investigate their empirical validity in the Nigerian context. The results of the analysis demonstrate a statistically significant and negative relationship between the unemployment rate and GDP growth. Specifically, a one-percentage-point increase in the unemployment rate is associated with a substantial decrease in GDP growth of approximately 2.5 percentage points. These findings affirm the relevance of Okun’s Law in Nigeria, highlighting the significant effect of unemployment on economic performance. The study also provides valuable insights for policymakers, offering evidence-based recommendations to guide the formulation of effective macroeconomic policies, including targeted measures to reduce unemployment and enhance economic forecasting.

EFFECT OF FOREIGN EXCHANGE RATE ON FINANCIAL PERFORMANCE OF LISTED MANUFACTURING COMPANIES IN KENYA

<p>The government of Kenya’s broad target for enhancing manufacturing is to increase the manufacturing share of gross domestic product from 8.4% to 15% to create more jobs but the target remains a mirage owing to the poor performance of the manufacturing sector over the years where for instance, sector performance declined to 3.5% in 2019 compared to 4.4% in 2018. Studies globally, regionally and locally have been conducted to establish how macroeconomic variables affect the profitability of companies. However, mixed results have been reported pointing to positive, negative, significant and insignificant effects making it unknown how economic growth, inflation and exchange rates influence the performance of manufacturing firms. The purpose of this study was to establish the influence of Interest rates on the financial performance of listed manufacturing companies in Kenya. The study was guided by; the efficient market hypothesis and arbitrage pricing theory. This study adopted a descriptive correlational research design grounded on panel data spanning 6 years from 2015 to 2020 with a target of 8 listed manufacturing firms. The exchange rate showed a negative influence on performance with coefficients 0.358, 2.764 and -1.532 respectively such that a 1% increase in economic growth and inflation increased performance by 0.358% and 2.764% respectively while a 1% increase in exchange rate decreased performance by 1.532%. The study recommends the formulation of prudent macroeconomic policies including bailouts during pandemics geared towards enhancing the performance of manufacturing firms as envisaged under the Big four agenda and Vision 2030 blueprint.</p><p> </p><p><strong>JEL</strong>: L60; O24; F31</p>

Prediction of Economic Operation Index Based on Support Vector Machine

Economic forecasting is not only an important field of economic research but also attracts extensive public attention. The results of the study are directly related to the accurate understanding and view of the economic situation, which in turn affects the rational formulation of macroeconomic policies. However, traditional estimation methods are often limited by expert experience and simple mathematical models, which are difficult to deal with on nonlinear models and do not meet the objective requirements for predicting macroeconomic performance indicators. Support vector machine is a popular new data mining technique. Benefiting from its good theoretical foundation and good generalization performance, SVM has become the starting point of research in recent years. Combining support vector machine research, fuzzy theory, and macroeconomic performance estimation, it attempts to develop a method for predicting macroeconomic function indicators based on support vector machines and expand the theory and projects of support vector machines. In addition, empirical evaluations of early financial forecasts are conducted to integrate theoretical and practical data. The characteristics of the traditional evaluation system are discussed in detail, and the standard model of the prediction system is established, including classical and modern forecasting theories and their forecasting systems. The theory of statistical learning and the theory and basic features of support vector machines are also discussed. For the analysis of the internal relationship between the distribution pattern, SVM, and the forecast of macroeconomic performance indicators, predictable economic activity forecast can be considered as a distribution system. Combining SVM with economic forecasting, SWE intelligent economic operation index forecasting is used for the first time, the automatic selection of symbolic parameters is recognized, and some algorithms and in-depth analysis methods are provided. The experimental results show that the prediction of economic operation indicators based on support vector machine technology is 57% faster than the traditional prediction method and the prediction accuracy rate is as high as 98.56%.

Macroeconomic Policy Formulation: The Driver of Economic Welfare in Ghana

Dialogue on the effects of macroeconomic policies relative to the welfare of citizens has remained virtually unchanged since time immemorial. These economic policy discussions have had different dimensions, from subjective to objective welfare. As a contribution to literature, this paper thoughtfully wades into this discourse with a specific reference to national policies on nominal GDP growth rate, inflation, and unemployment and how they interact to impact the welfare of citizens. It is common knowledge that macroeconomic policy decisions affect the very survival of citizens; however, it is unclear how policymakers communicate the long-term impact of these blueprints on the livelihoods of citizens to the implementing authorities. What is in the public domain are seminal reports on nominal annual rates of these macroeconomic variables, which are thought to either imply an improvement or a deterioration in the well-being of citizens. Indeed, not every gain in nominal rates, particularly GDP, can be construed as an improvement in the economic well-being of citizens. This study questions whether different approaches to designing and implementing macroeconomic policies are the reasons for the mismatch in the living standards of people around the world, or whether the free market economy, which is deficient in developing people's capabilities, is rather dictating the well-being of citizens. The vector error correction model (VECM) results indicate that economic growth (using GDP as a proxy) has a negative effect on welfare in the long run. As such, we believe that national governments should establish and implement comprehensive and long-term macroeconomic policies capable of boosting the welfare of citizens through creating jobs of all varieties, because mere annual gains in macroeconomic indicators do not realistically reflect the economic welfare of the citizenry.

Developmentalism at the periphery: addressing global financial asymmetries

Since the 2000s, new concepts of developmentalism have emerged in Latin America. In such approaches, the state deliberately pushes the development to achieve structural change and income redistribution. We analyse these from the perspective of policy space constraints imposed by international economic asymmetries, which today are predominantly of a financial nature. Based on a broad overview, we identify and compare the most relevant recent approaches. ‘New developmentalism’ has its strength in formulating adequate macroeconomic policies shielding the economy from volatility, although it considers redistribution as rather an outcome of structural change. ‘Social developmentalism’ emphasises the links between redistribution, domestic growth and structural change but lacks a coherent formulation of macroeconomic policies. The same applies to the concept of buen vivir, as the only approach that considers environmental aspects. Thus, all of these concepts have their strength in addressing specific issues, but are incomplete insofar as they do not provide a consistent framework for achieving all goals of growth with structural change, income redistribution and ecological sustainability. We identify the need to debate the interdependencies between sustained economic growth, income distribution and ecological sustainability.

Types of systemic risk and macroeconomic forecast: Evidence from China

<abstract> <p>The macroeconomic forecast is of great significance to the government macroeconomic policy formulation and micro-agent operational decisions. The individual systemic risk measurement has a certain scope of application and application conditions and, therefore, it is difficult for the individual indicator to reflect the systemic risk comprehensively. In this paper, the systemic risk is divided into four types: institution-specific risk, comovement and contagion, financial vulnerability, liquidity and credit. Next, the optimal combination is selected from multiple individual systemic risk indicators through dominance analysis to forecast the macroeconomic performance. The macroeconomic performance selects consumer price index (CPI), producer price index (PPI), industrial growth value (IVA), growth rate of broad money supply (M2) and gross domestic product (GDP) as proxies to compare the forecast effect of systemic risk, with the period considered spans from 2003M4 to 2022M7. The results of immediate forecasts of different macroeconomic performance proxies demonstrate the individual indicator cannot cover all the information of systemic risk, can only reflect the specific aspect of macroeconomic performance, or is only highly relevant in a given period. The contribution of systemic risk to the forecast of different macroeconomic performance proxies in different terms is diverse, and show various types of results. This paper uses the optimal combination of systemic risk to forecast the macroeconomic performance, which provides a valuable reference for improving the macro prudential supervision mechanism.</p> </abstract>

Analysis on Periodic Fluctuation of Economy at County Level

On most occasions, economic development is embodied with periodic characteristics. Comprehensive understanding of the economic cycle is of great significance to guiding the formulation of macroeconomic policies and maintaining stable economic growth. Since the reform and opening up, Tengzhou's economy has maintained a momentum of rapid growth, which, however, is characterized by obvious periodic fluctuations. This paper makes an in-depth analysis of the characteristics and causes of economic periodic fluctuation in Tengzhou, in order to have a comprehensive understanding of the economic cycle, and the economic law is adopted to take corresponding economic growth measures in different periods of economic development, thus achieving high-quality economic development. In this paper, the study of county economic cycle is not subject to economic theory, rather the study is conducted with a large number of historical background and local economic development characteristics. The economy of Tengzhou City, since the reform and opening up, is divided into five cycles. The conclusion is drawn that the county economic cycle is affected by macroeconomic fluctuations, industrial structure adjustment, investment and consumption fluctuations and other factors. We put forward targeted suggestions on using the economic cycle to promote high-quality economic development.

Does ecological compensation have a spillover effect on industrial structure upgrading? Evidence from China based on a multi-stage dynamic DID approach

China's rapid economic development has led to increasingly serious environmental problems, such as the deterioration of its ecology in important river basins. Adjusting the industrial structure through the mechanism of ecological compensation (EC) is a key measure for solving this economic and environmental dilemma. Early research on the impact of ecological compensation mechanisms has mainly focused on evaluating their performance in terms of the ecological environment, with little empirical evidence. Therefore, it is necessary to explore the economic effects of ecological compensation mechanisms in order to achieve sustainable economic and environmental development. Based on panel data from the Xin'an River Basin in China from 2009 to 2018, a multistage dynamic difference-in-differences (DID) model was constructed to systematically study the impact of the ecological compensation mechanism on the upgrading of the industrial structure. The research results show that due to the implementation of the policy, the industrial structure in pilot ecological compensation areas tends to be rational and advanced. The ecological compensation policy has dynamic effects on the upgrading of the industrial structure during the different stages of its implementation. This study is conducive to further enriching the relevant theories and practices underlying the study of ecological compensation mechanisms. At the same time, this paper provides operational suggestions for ecological protection, the adjustment of the industrial structure, and the formulation of relevant macroeconomic policies.

Dynamic causality between PPI and CPI in China: A rolling window bootstrap approach

AbstractThe relationship between PPI and CPI is one of the hotspots in the study of macroeconomics. Taking China as an example, the paper tests dynamic causality between PPI and CPI during the period of 2000:M1‐2019:M12. In view of structural mutations in time series variables, the bootstrap rolling‐window causality test is applied to restudy the dynamic causal relationship. As a result, it exists bidirectional transmission mechanism between CPI and PPI, both positive transmission of PPI to CPI and reverse transmission of CPI to PPI. In addition to putting more emphasis on transmission mechanism and causality between PPI and CPI, formulation of macroeconomic policy needs to focus on the correlation between PPI and CPI, and then the government can implement more targeted measures, such as strengthening the comprehensive application of monetary policy and fiscal policy, further deepening the supply‐side reform, optimizing the industrial structure to strengthen internal relationship between CPI and PPI.