Understanding the Relationship Between Shrinking Cities and Land Prices:Spatial Pattern,Effectiveness,and Policy Implications

Urban shrinkage has emerged as a widespread phenomenon globally and has a significant impact on land,particularly in terms of land use and price.This study focuses on 2851 county-level cities in China in 2005–2018(excluding Hong Kong,Macao,Taiwan,and‘no data’areas in Qinhai-Tibet Plateau)as the fundamental units of analysis.By employing nighttime light(NTL)data to identify shrinking cities,the propensity score matching(PSM)model was used to quantitatively examine the impact of shrinking cities on land prices,and evaluate the magnitude of this influence.The findings demonstrate the following:1)there were 613 shrinking cities in China,with moderate shrinkage being the most prevalent and severe shrinkage being the least.2)Regional disparities are evident in the spatial distribution of shrinking cities,especially in areas with diverse terrain.3)The spatial pattern of land price exhibits a significant correlated to the economic and administrative levels.4)Shrinking cities significantly negatively impact on the overall land price(ATT=–0.1241,P<0.05).However,the extent of the effect varies significantly among different spatial regions.This study contributes novel insights into the investigation of land prices and shrinking cities,ultimately serving as a foundation for government efforts to promote the sustainable development of urban areas.

From Finnish Assortment Pricing to Market Economy Using Prices for Sawn Wood and Chips in Reference Bucking

Dominant Finnish assortment pricing gives prices for sawlog and pulp wood volumes. Buyers buck stems to sawlogs using secret price matrices. Agreed dimensions allow wide range of sawlog volumes. Forest owners cannot objectively compare biddings: timber trade is a lottery game. Bucking is analyzed in terms of sawlog, pulp wood, log cylinder, sawn wood, value-weighted sawn wood, and chips. Sawn wood and its value are computed from top diameter of the sawlog. Profit maximization requires buyers to buck logs producing smaller than maximal value, causing dead weight loss. Nominal assortment prices have unpredictable relation to effective stumpage price. Assortment pricing does not meet requirements of market economy. If sawmills linked to pulp mills buck smaller sawlog percentages than independent sawmills, as generally believed, they use higher price for chips in their own harvests than they pay for independent sawmills, indicating imperfect competition for chips. Sawn wood potential pricing is suggested which gives prices for sawn wood and chips coming both from sawlogs and pulp wood in reference bucking which maximizes sawn wood for given minimum and maximum log length and minimum top diameter. Simple algorithm generates feasible bucking schedules from which optimum can be selected using any objective. Pricing produces unit price for all commercial wood utilizing ratio of theoretical sawn wood and commercial volume in stand. Unit price can be compared to stem pricing and could be compared to assortment pricing if assortment pricing would produce predictable sawlog percentages. Sawn wood potential pricing is concrete, transparent, easy to compute, considers stem size and tapering, reduces trading cost and is less risky to buyers than stem pricing. It meets requirements of market economy. Readers can repeat computations using open-source software Jlp22.

The Effects of Infrastructure Projects on House Prices and Rents:Evidence from the HS2 Extension Cancellation in the UK

This paper uses the HS2 extension cancellation in November 2021 as a quasi-experiment to study its impact on house prices and rents in Leeds.Using a DiD approach on repeat sales and monthly rents,I compare property values near the HS2 station and proposed construction site before and after the announcement.Results show a 3.6%decrease in house prices and a 3.9%decline in rents near the station,while properties near the construction site experienced a 2.4%increase in prices and a 2.1%rise in rents.This is the first paper to analyse the HS2 cancellation effect using panel data methods.

Hybridized Intelligent Neural Network Optimization Model for Forecasting Prices of Rubber in Malaysia

Rubber producers,consumers,traders,and those who are involved in the rubber industry face major risks of rubber price fluctuations.As a result,decision-makers are required to make an accurate estimation of the price of rubber.This paper aims to propose hybrid intelligent models,which can be utilized to forecast the price of rubber in Malaysia by employing monthly Malaysia’s rubber pricing data,spanning from January 2016 to March 2021.The projected hybrid model consists of different algorithms with the symbolic Radial Basis Functions Neural Network k-Satisfiability Logic Mining(RBFNN-kSAT).These algorithms,including Grey Wolf Optimization Algorithm,Artificial Bee Colony Algorithm,and Particle Swarm Optimization Algorithm were utilized in the forecasting data analysis.Several factors,which affect the monthly price of rubber,such as rubber production,total exports of rubber,total imports of rubber,stocks of rubber,currency exchange rate,and crude oil prices were also considered in the analysis.To evaluate the results of the introduced model,a comparison has been conducted for each model to identify the most optimum model for forecasting the price of rubber.The findings showed that GWO with RBFNN-kSAT represents the most accurate and efficient model compared with ABC with RBFNNkSAT and PSO with RBFNN-kSAT in forecasting the price of rubber.The GWO with RBFNN-kSAT obtained the greatest average accuracy(92%),with a better correlation coefficient R=0.983871 than ABC with RBFNN-kSAT and PSO with RBFNN-kSAT.Furthermore,the empirical results of this study provided several directions for policymakers to make the right decision in terms of devising proper measures in the industry to address frequent price changes so that the Malaysian rubber industry maintains dominance in the international markets.

Identifying Spatial Heterogeneity in the Effects of High-Tech Firm Density on Housing Prices:Evidence from Guangdong-Hong Kong-Macao Greater Bay Area,China

Innovation capitalization is a new concept in innovation geography research.Extant research on a city scale has proven that innovation is an important factor affecting housing prices and verified that innovation has a capitalization effect.However,few studies investigate the spatial heterogeneity of innovation capitalization.Thus,case verification at the urban agglomeration scale is needed.Therefore,this study proposes a theoretical framework for the spatial heterogeneity of innovation capitalization at the urban agglomeration scale.Examining the Guangdong-Hong Kong-Macao Greater Bay Area(GHMGBA),China as a case study,the study investigated the spatial heterogeneity of the influence of high-tech firms,representing innovation,on housing prices.This work verified the spatial heterogeneity of innovation capitalization.The study constructed a data set influencing housing prices,comprising 11 factors in 5 categories(high-tech firms,convenience of living facilities,built environment,the natural environment,and the fundamentals of the districts)for 419 subdistricts in the GHMGBA.On the global scale,the study finds that high-tech firms have a significant and positive influence on housing prices,with the housing price increasing by 0.0156%when high-tech firm density increases by 1%.Furthermore,a semi-geographically weighted regression(SGWR)analysis shows that the influence of high-tech firms on housing prices has spatial heterogeneity.The areas where high-tech firms have a significant and positive influence on housing prices are mainly in the GuangzhouFoshan metropolitan area,western Shenzhen-Dongguan,north-central Zhongshan-Nansha district,and Guangzhou—all areas with densely distributed high-tech firms.These results confirm the spatial heterogeneity of innovation capitalization and the need for further discussion of its scale and spatial limitations.The study offers implications for relevant GHMGBA administrative authorities for spatially differentiated development strategies and housing policies that consider the role of innovation in successful urban development.

Is pass‑through of the exchange rate to restaurant and hotel prices asymmetric in the US?Role of monetary policy uncertainty

This study examines the exchange rate pass-through to the United States(US)restaurant and hotel prices by incorporating the effect of monetary policy uncertainty over the period 2001:M12 to 2019:M01.Using the nonlinear autoregressive distributed lag(NARDL)model,empirical evidence indicates asymmetric pass-through of exchange rate and monetary policy uncertainty.Moreover,a stronger pass-through effect is observed during depreciation and a negative shock in monetary policy uncertainty,corroborating asymmetric pass-through predictions.Our results further show that a positive shock in energy prices leads to an increase in restaurant and hotel prices.Furthermore,asymmetric causality indicates that a positive shock in the exchange rate causes a positive shock to restaurant and hotel prices.We found feedback causal effects between positive and negative shocks in monetary policy uncertainty and positive and negative shocks in the exchange rate.Additionally,we detected a one-way asymmetric causality,flowing from a positive(negative)shock to a positive(negative)shock in energy prices.Therefore,these findings provide insights for policymakers to achieve low and stable prices in the US restaurant and hotel industry through sound monetary policy formulations.Highlights.The drivers of restaurant and hotel business in tourism destinations are examined.There is asymmetric pass-through of exchange rate and monetary policy uncertainty.A stronger pass-through is observed during appreciation and a negative shock to monetary policy uncertainty.There is asymmetric causality from positive shock in exchange rate to postive shock in restaurant and hotel prices.

Asymmetric relationship between global and national factors and domestic food prices:evidence from Turkey with novel nonlinear approaches

This study investigates the asymmetric relationship between global and national fac-tors and domestic food prices in Turkey,considering the recent rapid and continuous increase in domestic food prices.In this context,six global and three national explana-tory variables were included,and monthly data for the period from January 2004 to June 2021 were used.In addition,novel nonlinear time-series econometric approaches,such as wavelet coherence,Granger causality in quantiles,and quantile-on-quantile regression,were applied for examination at different times,frequencies,and quan-tiles.Moreover,the Toda-Yamamoto(TY)causality test and quantile regression(QR)approach were used for robustness checks.The empirical results revealed that(i)there is a significant relationship between domestic food prices and explanatory variables at different times and frequencies;(ii)a causal relationship exists in most quantiles,excluding the lowest quantile,some middle quantiles,and the highest quantile for some variables;(iii)the power of the effect of the explanatory variables on domestic food prices varies according to the quantiles;and(iv)the results were validated by the TY causality test and QR,which show that the results were robust.Overall,the empiri-cal results reveal that global and national factors have an asymmetric relationship with domestic food prices,highlighting the effects of fluctuations in global and national variables on domestic food prices.Thus,the results imply that Turkish policymakers should consider the asymmetric effects of global and national factors on domestic food prices at different times,frequencies,and quantiles.

The predictive power of Bitcoin prices for the realized volatility of US stock sector returns

This paper is motivated by Bitcoin’s rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets.It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns,particularly at the sectoral level of data.We specifically assess Bitcoin prices’ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons,based on daily data from November 22,2017,to December,30,2021.The findings show that Bitcoin prices have significant predictive power for US stock volatility,with an inverse relationship between Bitcoin prices and stock sector volatility.Regardless of the stock sectors or number of forecast horizons,the model that includes Bitcoin prices consistently outperforms the benchmark historical average model.These findings are independent of the volatility measure used.Using Bitcoin prices as a predictor yields higher economic gains.These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors,which is important for practitioners and policymakers.

Cryptocurrency technology revolution:are Bitcoin prices and terrorist attacks related?

As a financial innovation of the information age,cryptocurrency is a complex concept with clear advantages and disadvantages and is worthy of discussion.Exploring from a terrorism perspective,this study uses the time-varying parameter/stochastic volatil-ity vector autoregression model to explore the risk hedging and terrorist financing capabilities of Bitcoin.Empirical results show that both terrorist incidents and brutality may explain Bitcoin price,but their effects are slightly different.Compared to terrorist brutality,terrorist incidents have a weaker impact on Bitcoin price,showing that Bitcoin investors are more concerned about the number of deaths than the frequency of ter-rorist attacks.In turn,the impact of Bitcoin price on terrorist attacks is negligible.Bitcoin is a potential means of financing terrorism,but it does not currently play an important role.Our research findings can help investors analyze and predict Bitcoin prices and help improve the theoretical system of anti-terrorist financing,helping to maintain world peace and security.

Impact of Sharp Fluctuations of Live Pig Prices on Financial Capacity of Pig Breeding Enterprises : An Analysis Based on the Data of Listed Enterprises from 2018 to 2021

Based on the general equilibrium theory of microeconomics,this study first analyzed the causes of sharp fluctuations in live pig prices,and then explored the financial capabilities of enterprises during the sharp fluctuations of live pig prices by using the financial data of 4 typical top listed enterprises from 2018 to 2021.By comparing the changes in the capabilities of enterprises,the impact of price on the financial capability of enterprises and differences were identified.The research results showed that the price of live pigs played a decisive role in enterprise profits,and there were huge differences in the fluctuation period.In the sharp increase period of price,price temptation is easy to cause enterprises to over-invest,resulting in excessive growth of enterprise assets,and increasing the business risk of enterprises.Based on the above conclusions,some policy suggestions were put forward to promote the stable development of industry from the three levels of enterprises,industries and government departments.

Predicting Future Cryptocurrency Prices Using Machine Learning Algorithms

Cryptocurrency price prediction has garnered significant attention due to the growing importance of digital assets in the financial landscape. This paper presents a comprehensive study on predicting future cryptocurrency prices using machine learning algorithms. Open-source historical data from various cryptocurrency exchanges is utilized. Interpolation techniques are employed to handle missing data, ensuring the completeness and reliability of the dataset. Four technical indicators are selected as features for prediction. The study explores the application of five machine learning algorithms to capture the complex patterns in the highly volatile cryptocurrency market. The findings demonstrate the strengths and limitations of the different approaches, highlighting the significance of feature engineering and algorithm selection in achieving accurate cryptocurrency price predictions. The research contributes valuable insights into the dynamic and rapidly evolving field of cryptocurrency price prediction, assisting investors and traders in making informed decisions amidst the challenges posed by the cryptocurrency market.

The Causes of Hiking Ethiopian Consumer Prices

A growing consumer price is creating instability in the macroeconomic environment and hinders the consumption level of especially the poor society.This paper then explored the major causes of such increasing consumer prices using Ethiopian cases.Using data from the National Bank of Ethiopia from 1982/1983 to 2019/2020,it condensed the information of monetary sector,external sector and fiscal sector variables to a small set to estimate the causes of Ethiopian consumer price hiking using the ARDL model.The factors determining consumer price differ from food to non-food.The most important factors determining food price are price expectation and fiscal factors.On the other hand,the main determinant of non-food consumer prices is the fiscal factor.The author also found evidence of fiscal factors and price expectation effects on general consumer prices.Therefore,to contain the rise in consumer prices,it needs to exercise conservative fiscal stances,which require minimizing deficit financing,reducing the import tax rate and reducing domestic indirect tax rates such as excise tax and value added tax on basic consumer goods and services.Moreover,sound government policies are essential to address inflation anticipations(providing information for society about the future of inflation)to change public opinion.

Effect of the Policies to Prevent Drug Shortage and Stabilize Drug Prices in Medical Institutions

Objective To evaluate the effect of some policies to prevent drug shortage and stabilize drug prices,and to provide reference for improving relevant policies.Methods With a combination of random stratified sampling and quota sampling,532 medical institutions in 20 provinces were selected to carry out questionnaire surveys.Then,a comparative analysis was made to study the changes of drugs on the shortage list and drugs on non-shortage list before and after the release of the policy of ensuring supply and stabilizing prices.Results and Conclusion The policy played an important role in curbing the growth of drug shortage in the medical institutions,but it did not curb the growth of drugs on non-shortage list.Besides,the drugs on non-shortage list showed an overall fluctuation and upward trend.Meanwhile,from the perspective of drug prices,the price stability problem of drugs on the shortage list and on the non-shortage list became more serious,and the average price increase was 256% and 239%,respectively.The implementation of policies related to the supply and price stability of drugs prevents the growth trend of drug shortages in the list of medical institutions,which has been recognized by most medical institutions.However,there is an increasing trend in the number of drugs on non-shortage list.In addition,the price increase of drugs on both the shortage list and non-shortage list is severe.Some medical institutions report that they have difficulties in using the information reporting system of drug shortage and the classification,grading and the alternative use of drug shortages.It is recommended to strengthen the management of price stabilization of drugs on the shortage list.Further attention should be paid to the supply and price stabilization of drugs on non-shortage list.At the same time,trainings in the classification and substitution of drug shortage and information reporting system should be actively organized,thus comprehensively improving the capabilities of medical institutions at all levels to deal with the problem of drug shortage.

Impact of Environmental,Social,and Governance(ESG)Factors on Stock Prices and Investment Performance

This study examines the relationship between Environmental,Social,and Governance(ESG)factors and stock prices as well as investment performance.ESG factors have become increasingly relevant in investment decisions as investors prioritize companies with sustainable practices.Using a sample of publicly-traded companies,this research analyzes the impact of ESG factors on stock prices and investment returns.The findings suggest that companies with strong ESG performance tend to have higher stock prices and better investment performance than those with weak ESG performance.The study also highlights the significance of the individual components of ESG,such as environmental policies and corporate governance practices,on stock prices and investment returns.Overall,this research provides valuable insights for investors seeking to incorporate ESG factors into their investment decision-making processes.

The relationship between international crude oil prices and China＇s refined oil prices based on a structural VAR model

With the frequent fluctuations of international crude oil prices and China＇s increasing dependence on foreign oil in recent years, the volatility of international oil prices has significantly influenced China domestic refined oil price. This paper aims to investigate the transmission and feedback mechanism between international crude oil prices and China＇s refined oil prices for the time span from January 2011 to November 2015 by using the Granger causality test, vector autoregression model, impulse response function and variance decomposition methods. It is demonstrated that variation of international crude oil prices can cause China domestic refined oil price to change with a weak feedback effect. Moreover, international crude oil prices and China domestic refined oil prices are affected by their lag terms in positive and negative directions in different degrees. Besides, an international crude oil price shock has a signif- icant positive impact on domestic refined oil prices while the impulse response of the international crude oil price variable to the domestic refined oil price shock is negatively insignificant. Furthermore, international crude oil prices and domestic refined oil prices have strong historical inheri- tance. According to the variance decomposition analysis, the international crude oil price is significantly affected by its own disturbance influence, and a domestic refined oil price shock has a slight impact on international crude oil price changes. The domestic refined oil price variance is mainly caused by international crude oil price disturbance, while the domestic refined oil price is slightly affected by its own disturbance. Generally, domestic refined oil prices do not immediately respond to an international crude oil price change, that is, there is a time lag.

Do the RMB exchange rate and global commodity prices have asymmetric or symmetric effects on China’s stock prices?

With the rapid expansion of the RMB exchange rate’s floating range,the effects of the RMB exchange rate and global commodity price changes on China’s stock prices are likely to increase.This study uses both auto regressive distributed lag(ARDL)and nonlinear ARDL(NARDL)approaches to explore the symmetric and asymmetric effects of the RMB exchange rate and global commodity prices on China’s stock prices.Our findings show that without considering the critical variable of global commodity prices,there is no cointegration relationship between the RMB exchange rate and China’s stock prices,and the coefficient of the RMB exchange rate is not statistically significant.However,when we introduce global commodity prices into the NARDL model,the result shows that the RMB exchange rate has a negative effect on China’s stock prices,that there indeed exists a long-run cointegration relationship among the RMB exchange rate,global commodity prices,and stock prices in the NARDL model,and that global commodity price changes have an asymmetric effect on China’s stock prices in the long run.Specifically,China’s stock prices are more sensitive to increases than decreases in global commodity prices.Thus,increases in global commodity prices cause China’s stock prices to decline sharply.In contrast,the same magnitude of decline in global commodity prices induces a smaller increase in China’s stock prices.

The Changes in World Oil Prices, Monetary Factors, and Foreign Index Toward Composite Index Movement： Indonesian Case for the Period of 2005-2011

Capital market is one of the drivers of the economy through the formation of capital investor excess as well as an indicator of a country＇s economy. Movement of stock price index is often influenced by many factors, derived from the company＇s performance, monetary factor, and changes in world oil prices. This study highlights the problem in world oil prices due to political turmoil in the Middle East. The samples are taken from the Jakarta Composite Stock Price Index （JCI）, oil prices, Indonesian inflation rate, Certificate of Bank Indonesia＇s （CBI） rate, and the reserve assets, during the period from January 2005 to December 2011 （84 months）. Using the data published by the Bank of Indonesia, reports of the Central Bureau of Statistics （Biro Pusat Statistik, BPS）, and other relevant sources, the data analyzed through the Eviews 7.1. The main objective of this study is to examine the effect of oil prices, foreign stock price index, and monetary variables （inflation rate, CBI rate, country＇s foreign reserves, and others） toward the JCI analyzed through the error correction model （ECM）. Hypothesis testing with the F-test for the 95% confidence level indicates that the oil price, exchange rate （Indonesian Rupiah （IDR）/United States Dollar （USD））, CBI rate, foreign exchange reserves, the Dow Jones Index, and the Taiwan stock index, both simultaneously as well as partially have a significant influence on the JCI.

Decomposition laws of tungsten prices fluctuation since 1900 and its applications

Tungsten current price was transformed yearly to its constant price since 1900, which is roughly decomposed into four components as trend, cycle, impact and random. The core prices, consisting of the trend and the cycle, present regularities that a long-run cycle is embedded within two major cycles, and major cycle is composed of low-price period and high-price period, along with the rapid rise into a tower, and along with deep down into next trough; three sharply upward shocks occur by the events in a tower. Fluctuations in prices trend to slow cycles and expand the bands. It can be expected that tungsten price will highly stand over 17 a, and is is a advice that reducing production and restricting export maybe maintain a high price level.

Dynamic interacting relationships among international oil prices, macroeconomic variables and precious metal prices

From the perspective of long-term and short-term, the methods of TY causality test, generalized impulse response function, variance decomposition were used to investigate the impacts of international oil prices and macroeconomic variables on Chinese gold, silver and platinum prices, but also the feedback effects of Chinese precious metal prices under this impact. The results show that international oil prices play an important role in precious metal price variation both in long-term and short-term, and exchange rate only has an effect in short-term, while interest rate is ineffective in predicting precious metal prices. In addition, precious metal prices have some feedback effects on international oil prices and interest rate in short-term.

African swine fever and meat prices fluctuation:An empirical study in China based on TVP-VAR model

African swine fever(ASF),a fatal disease outbroken in China in August 2018,has widely attracted social concern especially in the information era.The occurrence of ASF led to an imbalance between supply and demand in pork and other meat markets.As a result,meat prices fluctuated greatly during the past year in 2019.To measure ASF quantitatively,the internet public concern index about ASF was created using web crawler methods.The relationships between ASF and meat prices were analyzed based on time-varying parameter vector auto-regressive(TVP-VAR)model.The results showed that there were some differences in the impact size,direction and duration of ASF on the prices of pork,chicken,beef and mutton,and the characteristics of time variability and heterogeneity were obvious.At the same time,the impact of ASF on meat prices is not consistent with the trend and degree of ASF.The impulse intensity is strongly correlated with the strength and duration of ASF,and it is generally weak in the early stage and much stronger in the middle and late periods.The results indicate that macro regulations,monitoring and early-warning system,standardizing production and circulation,and the public opinion monitoring and guidance about ASF should be given more attention in future to stabilize the market expectations and to promote a smooth functioning of the livestock markets.