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The purpose of this paper is to examine the nominal exchange rate pass-through to domestic prices in Sudan from 1978–2017. An autoregressive distributed lag (ARDL) approach to cointegration is employed. The analysis is based on impulse response functions (IRFs) and forecast error variance decompositions (FEVDs). The dynamics of the cointegrated system can be investigated via the variance decompositions and IRFs. The findings confirm that the degree of exchange rate pass-through in Sudan is incomplete, and the empirical results also show that the domestic price index is predominantly caused by foreign price in both the short and long runs, in addition to the import price index and the nominal exchange rate; the exchange rate shock has a negative effect on the domestic price. Furthermore, FEVDs analysis illustrates that the variation in domestic price is primarily determined by the import prices, while changes in the exchange rate are primarily determined by the exchange rate itself.

The paper analyses the development of international liquefied natural gas markets in Asia Pacific for the period up to 2040 in four main aspects. Firstly, it examines the outlook of LNG demand growth in Asia Pacific and shows that although Asia Pacific will remain the main driver of global LNG consumption its growth rate is highly uncertain. Secondly, it estimates the long-term excess of LNG supply and shows that the aggregate capacity of liquefication terminals around the world currently under construction would be enough to match even the optimistic LNG demand growth forecasts up to 2040. Hence the competition among LNG exporters will inevitably intensify. Thirdly, it is argued that the global LNG market developments during the COVID-19 pandemic in 2020–21 are likely to prevent JKM price index from advancing as a common price reference in long-term LNG supply contracts in Asia Pacific. That could also stimulate the potential replacement of oil indexation in those contracts with indexation to European spot natural gas price TTF. Finally, the paper concludes that both of those potential developments suit the interests of Russian LNG exporters.

This study is about the analysis of the exchange rate of fishery product processors (NTPI) in Bandung Regency. This study aims to analyze the NTPI in Bandung Regency and analyze the price index of processed fishery products in Bandung Regency to find out the level of welfare. The method used in this study is a survey method with interview techniques using questionnaire, the types of data collected include primary data and secondary data. The research data were analyzed using quantitative descriptive analysis methods. Based on the results of the research that has been carried out, it can be concluded that the NTPI in Bandung Regency as a whole, both processing bloater, shredded fish, and fish balls has an NTPI > 100 which indicates that fishery processors in Bandung Regency are at a prosperous level and prices increase in the base year and 2021 only occurred in processed fish floss and fish balls although they were not large, while in the processed bloater there was no price increase at all from the base year until 2021.

The Islamic capital market plays an important role in the growth of the economy in Indonesia. During its development, the performance of the stock index in a country is often influenced by other stock indices in other countries. This study tries to analyze dependance of macroeconomic variable towards JII and IHSG price, using Autoregression Distributed Lag (ARDL). The results show that the Consumer Price Index (CPI) and exchange rate (EXC) significantly affect the movement of the JII and IHSG price index. This indicates that the movement of the JII and IHSG index in the short term is influenced by domestic production and exchange rate. Furthermore, the results of the research show a little uniqueness in the crude oil price (COP) variable.Keywords: ARDL, Macroeconomic, Oil price, Islamic stockJEL Classifications: E44, F41, Q43DOI: https://doi.org/10.32479/ijeep.10911

The purpose of this study is to analyze the factors of food inflation in Java and Sumatra. This study uses Panel Data regression analysis to identify inflation and uses a panel data model from 2013 - 2019 with a sample of 16 provinces in Sumatra and Java, to determine the effect of each variable on the Consumer Price Index. The results of this study indicate that from the panel data regression analysis that the independent variables of the World Food Price Index, the Average Price of Onions, Rice, Chili and Chicken in the Provincial Capital have an effect on the Consumer Price Index, while Per Capita PDRB does not have a significant effect on the Consumer Price Index in the Province. Java and Sumatra.

We focus on forecasting the probability that euro-area inflation will fall into one of three intervals by employing an ordered multinomial model augmented with macroeconomic variables. We directly forecast the probability that the expected euro area HICP price index inflation rate (12-month percent changes) over the next 12 and 24 months will be less than 1.5 percent, exceed 2 percent, or be between these two values. The model includes many predictors, and deal with dimensionality issues by an approach which mixes factor models with Bayesian shrinkage. Our results show that macroeconomic variables’ inclusion improves the model’s forecast quality, especially at the longer horizon considered. The Deflationary Pressure Index coincides with the probability that inflation is below 1.5 percent on average in the next 24 months, and it is useful as a policy monitoring tool.

With the average contribution of imports to Gross Regional Domestic Product (GRDP) in the last five years reaching 19.1%, the dynamics of global commodity prices also influence the economic performance of East Java, including the movement of inflation. A composite indicator of global commodity prices is needed to find out the impact of changes in various global commodity prices on inflation in East Java. By adopting the Bank Indonesia methodology in forming a composite global price known as the Imported Inflation Price Index (IHIM) which has considered the method of forming a global composite price created by the IMF (IMF Commodity Price Index), the compilation of East Java global price composites also examines the accuracy of commodity selection and aspects of data availability. The selected global price composite for East Java is a composite of seven global commodities which include food (wheat, soybeans, corn and CPO) and non-food (iron, gold and oil). These are two aspects determining the relative weight, namely (I) the import portion of the total input based on the Input-Output table and (ii) the commodity weight of derivatives in the East Java Consumer Price Index (IHK) basket. Furthermore, with OLS regression, the composite of East Java global commodity prices affects the core-traded inflation movement in East Java. Thus, the composite of global commodity prices in East Java can be used as an indicator of East Java inflation projections, especially core-traded inflation. This study, in general, will also examine the effect of the exchange rate impact on the movement of core inflation, especially traded groups in East Java. Based on the regression results it is known that the impact of the exchange rate movement on core traded inflation in East Java is more significant than the effect of world commodity price movements.

The reduction in oil prices might make crude oil a cheaper alternative to renewable energy (RE). Given this, the present paper examines the effect of fluctuation of oil prices on the use of RE in the United States (US) during the period 1970 to 2018. We constructed two nonlinear autoregressive distributed lag (NARDL) models to examine the effect of the positive and negative oil price shocks on the use of RE in the US. The RE consumption is taken as the dependent variable and the gross domestic product (GDP), Brent crude prices, population density, trade openness, and price index as independent variables. The result revealed that the rise in crude oil price, GDP, and population density will increase RE use in the short run and in the long run as well. Moreover, the study finds that any decrease in oil prices will decrease RE use in the short run and its effect will eventually diminish in the long run. On the policy front, it is suggested that US should raise its energy security by reducing its dependency on imported crude oil and increase the role of RE through the imposition of taxes on oil and increase the base of production and consumption through a series of measures.

Machine Learning is a subset and technology developed in the field of Artificial Intelligence (AI). One of the most widely used machine learning algorithms is the K-Nearest Neighbors (KNN) approach because it is a supervised learning algorithm. This paper applied the K-Nearest Neighbors (KNN) algorithm to predict the construction price index based on Vietnam's socio-economic variables. The data to build the prediction model was from the period 2016 to 2019 based on seven socio-economic variables that impact the construction price index (i.e., industrial production, construction investment capital, Vietnam’s stock price index, consumer price index, foreign exchange rate, total exports, and imports). The research results showed that the construction price index prediction model based on the K-Nearest Neighbors (KNN) regression method has fewer errors than the traditional method.

We examine the notion of a price index as the solution to the problem of minimizing the distance between the index values and the vector of price ratios. To do so, the choice of a suitable distance function is of crucial importance. We use a generalized least-squares criterion for this purpose and show that the generalized quasilinear functions are the only solutions to the problem of minimizing the distance thus defined. There are numerous special cases that are obtained for specific choices of the requisite functions and weights. In particular, we show that, in addition to the well-established indexes of Laspeyres, Paasche, Marshall-Edgeworth, Walsh, and Törnqvist, the arithmetic-current-period index, the arithmetic-hybrid index, the harmonic-base-period index, and the harmonic-hybrid index can be obtained with suitably chosen distance functions. Furthermore, the logarithmic least-squares criterion is employed to obtain indexes that are based on geometric means.

We present a mixed frequency repeat sales model for commercial real estate, taking into account changes in net operating income between the date of buying and selling the property. Moreover, we relate monthly private market index asset returns to lags, up to one year, of daily REIT returns. The underlying REIT model enables us to interpolate the monthly private market index returns on a daily basis, and to predict the private market index asset returns going forward. The mixed frequency repeat sales model facilitates daily appraisal of commercial real estate portfolios. We apply the model on sale prices (all property types, and apartments only) in the period January 2006 up to July 2020, provided to us by Real Capital Analytics. We find that the mixed frequency repeat sales model reduces one-month-ahead forecasts errors and index revisions, compared to a benchmark model without daily REIT returns.

Along with the 2008 global economic crisis, the impact of housing prices on the economy drew attention. For this reason, many studies have been conducted examining the importance of housing prices from different perspectives. In this study, the effect of housing prices on private final consumption expenditures is analyzed using panel data analysis method utilizing data on private final consumptionexpenditure, nominal housing price index and GDP. The data of thirty three OECD countries were analyzed on a quarterly basis, starting from the first quarter of 2008 to the second quarter of 2020. The long-term relationship between variables was examined using the panel cointegration test. The DOLSMG estimator was used to determine the long-term elasticity coefficients. The main purpose of the study is to determine which macroeconomic indicators affect the relationship between housing prices and consumption expenditures more. For this reason, 33 OECD countries are classified according to various macroeconomic criteria and divided into two. As a result of the tests carried out, if income inequality is high, net savings rates are low and urban population rates bolumlerare at the extreme, the level of impact of housing prices on private consumption increases. In countries with these characteristics, it would be appropriate to make efforts to prevent sharp fluctuations in house prices with various financial and economic measures. Otherwise, in countries with these characteristics, sudden loss of value in housing prices may cause deeper economic contractions by rapidly decreasing private consumption.

The aim of the study is to determine the dates of food price bubbles in Fragile Eight countries during the 2008 global crisis and the Covid 19 pandemic. For this purpose, dates of food price bubbles are estimated using the FAO (Food and Agriculture Organization) Food Price Index for the period 2007:01-2020:09 through recursive right-tailed unit root tests (Sup-Augmented Dickey-Fuller;SADF and Generalized Sup-Augmented Dickey-Fuller;GSADF). As a result of the study, short-term food price bubbles were detected during the global crisis in other Fragile Eight countries, with the exception of Argentina, Brazil and Chile. When the findings of the Covid 19 pandemic period are examined, it is seen that there are food price bubbles that started in the second quarter of 2020 and are still continuing in other Fragile Eight countries except Indonesia and Argentina. Considering the impact of the drought experienced throughout the world during the pandemic period, it is an expected finding that the food price bubbles will be longer. The common point of the bubbles, which took place during the covid 19 pandemic period, is that the bubbles peaked in the fourth month of 2020. In addition, the findings show that the largest food price bubble occurred in Turkey during the Covid 19 pandemic period.

The paper aims to summarize and analyze the statistical data on the generation structure and price behavior in the Russian electric power industry and, based on the analysis, make a statistical contribution to the discussion on the outcomes of the electricity reform in Russia. A brief review of this topic is provided. The study states that the electric power industry should be regarded as a combination of four sub-industries when regulation, market design, and industrial organization problems are discussed. These four sub-industries are generation, transmission, distribution, and sales because of different regulations applied to these businesses. The main trends in the retail price index and some other indexes in the four sub-industries are observed from 2009 to 2018. The trends in electricity consumption and generation structure are discussed as essential components of economic processes in the industry. The findings suggest that the four sub-industries make different contributions to the overall growth of the electricity supply costs for end consumers. Most growth was determined by regulated government-granted monopolies in network businesses (transmission and distribution companies). The sales business is represented in the research by suppliers of last resort.

Suppliers of tourist services continuously generate big data on ask prices. We suggest using this information, in the form of a price index, to forecast the occupation rates for virtually any time-space frame, provided that there are a sufficient number of decision makers “sharing” their pricing strategies on the web. Our approach guarantees great transparency and replicability, as big data from OTAs do not depend on search interfaces and can facilitate intelligent interactions between the territory and its inhabitants, thus providing a starting point for a smart decision-making process. We show that it is possible to obtain a noticeable increase in the forecasting performance by including the proposed leading indicator (price index) into the set of explanatory variables, even with very simple model specifications. Our findings offer a new research direction in the field of tourism demand forecasting leveraging on big data from the supply side.

Purpose – The aim of this study is to reveal the relationship between the amounts of domestic credits, macroeconomic factors and financial development levels by providing worldwide evidence. Design/methodology/approach – This study uses the data obtained from the financial development database provided by the World Bank and covering all countries of the world and the years 2001-2019. The dependent variable is the ratio of domestic loans distributed to the private sector to gross domestic product, in the fixed effects panel regressions, while macroeconomic factors such as money supply, exchange rate, interest rate, price index are included as independent variables. Two indices are used to represent the financial institutions’ development levels. One of them shows the extent to which the borrowers and lenders are guaranteed by the laws in financial markets, the other index shows the efficiency of the debtor information provided by the credit registry institutions. Findings – This paper unveils strong findings for the positive relationship between domestic credit and expansionary monetary policy. On the other hand, it is found that the consumer price index and the domestic credits are negatively associated. The paper provides no evidence for the effect of financial inst itutions’ development levels on domestic credits. Discussion – While the discussions continue on the positive and negative consequences of the credit expansions in the economy, the importance of the papers those analyze the reasons of credit expansions are also increasing. This study contributes to the existing literature, which mostly emphasizes the importance of macroeconomic factors, considering the differences in financial development levels between countries. The econometric analysis applied in the study reveals the negative relationship between the consumer price index and domestic credits, and the positive relationship between money supply and domestic credit. Taken together, these adverse effects reveal that policy makers should be careful in their monetary policies designs.

Countries use either the Dutot, Carli or Jevons indices for the calculation of their Consumer Price Index (CPI) at the lowest level of aggregation. In this paper, we compare expected values and var...

Introduction. Despite the start of the outbreak of the virus (COVID-19) was in December 2019, stock markets did not respond immediately as there was little information about the expected duration of the crisis and whether China would be able to contain it within a short period of time, and the risks entailing to the global economy due to the virus spread becoming pandemic that endanger the global health situation. As a result of the great uncertainty that prevailed among investors in the third week of February, stock markets around the world incurred trillions of US dollars in losses in a single week (ending February) seen as the worst week for financial markets since the 2008 global financial crisis. The initial purpose of this paper is to assess the reaction of major Asian stock markets to the early outbreak of COVID-19 pandemic and its spillover effects among these markets. Material and methods. To capture switching behavior of major Asian stock markets due to the early outbreak of COVID-19, the paper uses daily price indexes of Shanghai composite, Hong Kong, Nikkei 225, and Korea stock market, during the period from December 2, 2019 to March, 13,2020. Markov switching dynamic regression (MSDR) employed to assess the behavior of each market to the response of the other markets’ behavior. Results. Our finding indicate evidence of two states that distinguish the behavior of the stock markets during the early outbreak of the pandemic. In state 1, when the significance of the pandemic was not fully realized there was a strong link and influence between these markets, but in state 2, when the scale and size of the pandemic realized these markets displayed decoupling behavior. Results also indicate, Hong Kong and Nikkie stock markets were the epicenter in both states. The impact of the pandemic news on the behavior of these markets as indicated by the transition probabilities of state 2, varied from 3 days duration effect (Hong Kong) to 3month duration effect (Nikkei 225). Discussion and conclusions. The interactive association between these stock markets is important for investors as well as for policy-makers. Increasing departure of stock prices from their fundamental driver, that is the common economic bonds linking these markets, implies increasing risk for investors in these stock markets. The duration of the shock as indicated by the transition probabilities show that Hong Kong stock exchange was the most resilient in the group, while Nikkei was the most reactive to the pandemic shock.

This study attempts to examine the relationship between monetary policy and housing prices in India. We use monthly data from January 2009 to December 2018 of four variables- Housing Price Index (HPI), Real Effective Exchange Rate (REER), Gross Domestic Price (GDP), and interest rate for our estimations using the Autoregressive Distributive Lag (ARDL) Model. The results from the study show that the impact of monetary policy on housing prices is significant only on lag three; however, the coefficient is very small. The results from the ARDL model are also supported by the variance decomposition of housing price. The variance decomposition of housing prices highlights that monetary policy explains around 13 percent of the variation in housing prices over a period of ten months. Further, the accumulated impulse response function reveals that with one-unit shock to interest rate results in a -0.000875 unit change in housing price. The study stipulates that, since conventional monetary policy has a modest impact on housing prices, therefore, it is insignificant for addressing the problems of real estate in India.

BACKGROUND The Chinese government removed the price ceiling of most drugs in June 2015 to establish a market-driven pricing system. The purpose of this study is to examine the impact of the elimination of drug ceiling price (EDCP) policy on drug prices. METHODS Using a national macro-level dataset, we employed an interrupted time series method to study the abrupt level and gradual trend changes of Chinese and Western medicine consumer price index (CPI) between June 2014 and June 2017. RESULTS The policy exerted level change on Chinese medicine CPI, increasing 0.201% (95% CI 0.026% to 0.376%, p = 0.026). And the trend in Chinese medicine CPI was still decreased nonsignificantly after the EDCP policy. However, there was no significant price change in Western medicine. CONCLUSION The EDCP policy has different effects on the prices of Chinese and Western medicines. Therefore, we need to pay more attention to the drug pricing mechanism in the future.

We develop a theoretical model of the beer industry consisting of two segments: monopolistically competitive heterogeneous craft breweries that compete based on variety and quality and oligopolistically competitive macrobreweries that compete based on economies of scale. We also present empirical evidence that, in contrast to the practice in most manufacturing industries, smaller craft breweries pay more for higher quality inputs and charge higher prices. The model is simulated to analyze three counterfactual scenarios: the taste renaissance by expanding consumers' (i) taste for variety and (ii) preference for unique quality beer and (iii) the 2017 tax reform. The taste renaissance scenarios have similar directional effects on the monopolistic segment, but the impacts of an increase in the love‐of‐variety are much stronger than those of an increase in quality. In all three scenarios, the craft segment expands and the macrosegment contracts, but industry‐level sales rise and the aggregate price index declines, benefiting consumers.

Farmer Exchange Rate (NTP) is a proxy indicator or an indicator of the approach to the welfare level of breeders. The welfare of breeders can illustrate the purchasing power of farmers. The purpose of this study 1) To determine the level of welfare of cattle breeders and 2) To determine the effect of price index factors paid by farmers on the price index received by cattle breeders.This study uses NTP time series data with the base year 2012 = 100 as the basis for calculating the years 2018 - 2019. The location studied was determined by purposive sampling method in 5 districts in Sidoarjo Regency which are centers of beef cattle and dairy cows. The sample was selected by purposive random sampling as many as 75 cattle breeders. The NTP analysis method was carried out descriptively and the analysis of the factors that influenced the NTP was carried out using multiple linear regression. The results showed that the exchange rate of cattle farmers in Sidoarjo regency in 2019 increased by 1.81 percent from 109.41 in 2018 to 111.21 in 2019. This shows that cattle breeders in Sidoarjo district are experiencing a surplus or prosperity. The production input price index which includes the price of seeds, feed, and labor wages that must be paid by cattle breeders is a factor that has a significant effect on the cattle price index received by breeders.

This study investigates the effects of increasing real energy prices on technical environmental efficiency in Iran using a directional distance function. To achieve this purpose, six variables are used, namely labor, capital stock, energy consumption, real energy price, industrial value added, and environmental cost index, for the period 2009–2015. Moreover, two indexes for industrial environmental cost and real energy price are introduced. The environmental cost index is derived from the pollution cost of six types of emissions (NOx, SO2, CO2, CO, CH4, SPM) produced by 23 industries. The results show that the real energy price and distance function have an inverse relationship. Specifically, higher real energy prices cause industries to move toward using more energy-efficient technologies and more technical environmental efficiency. This study is one of the few studies that have evaluated the environmental effects of targeted subsidy law or energy price liberalization in Iran and demonstrates that an increase in real energy prices would lead to improved environmental technical efficiency.

Purpose The repeat sales house price index (HPI) has been widely used to measure house price movements on the assumption that the quality of properties does not change over time. This study aims to develop a novel improvement-value adjusted repeat sales (IVARS) HPI to remedy the bias owing to the constant-quality assumption. Design/methodology/approach This study compares the performance of the IVARS model with the traditional hedonic price model and the repeat sales model by using half a million repeated sales pairs of housing transactions in the Auckland Region of New Zealand, and by a simulation approach. Findings The results demonstrate that using the information on improvement values from mass appraisal can significantly mitigate the time-varying attribute bias. Simulation analysis further reveals that if the improvement work done is not considered, the repeat sales HPI may be overestimated by 2.7% per annum. The more quality enhancement a property has, the more likely it is that the property will be resold. Practical implications This novel index may have the potential to enable the inclusion of home condition reporting in property value assessments prior to listing open market sales. Originality/value The novel IVARS index can help gauge house price movements with housing quality changes.

This manuscript presents a framework to develop vector error correction (VEC) models applicable to forecasting the short- and long-run movements of the average hourly earnings of construction labor, which is an essential predictor of the construction labor costs. These models characterize the relationship between average hourly earnings and a set of explanatory variables. The framework is applied to develop VEC forecasting models for the average hourly earnings of construction labor in the USA based on the identified variables that govern its movements, such as Global Energy Price Index, Gross Domestic Product, and Personal Consumption Expenditures. More than 150 candidate VEC models were created, of which 25 passed the diagnostics. The most appropriate model was then identified by comparing the prediction performance of these models when applied to the forecasting average hourly earnings over 36-months. The proposed framework and the ensuing models address the need for appropriate models that can forecast the short- and long-run movements of the labor costs. Practitioners can use the proposed framework to develop much-needed forecast models and estimate construction labor costs of the various projects. The insights derived from the development and applications of these models can enhance the chances of project success.

We examine the nonlinear dependence dynamics and downside and upside risk spillovers between oil prices and world food prices captured by a world food price index and its subcategories of dairy, cereals, vegetable oil, and sugar. We draw our empirical results using static and dynamic bivariate copulas, Value-at-Risk (VaR) and conditional VaR (CoVaR) methods. Our empirical findings reveal that oil prices and aggregate food prices, as measured by the world food price index, independently move during market upturns and downturns. However, lower and upper tail dependence is observed between oil prices and cereals, vegetable oil, and sugar prices. We also identify upside and downside asymmetric risk spillovers from individual food commodities to oil and from oil to food commodities. Oil prices most strongly affect sugar and vegetable oil prices (downside and upside) whereas oil prices are most strongly impacted in the downside and upside by vegetable oil and sugar prices, respectively. The implications of the results are discussed.

Current food crisis predictions are developed by the Famine Early Warning System Network, but they fail to classify the majority of food crisis outbreaks with model metrics of recall (0.23), precision (0.42), and f1 (0.30). In this work, using a World Bank dataset, classical and neural network (NN) machine learning algorithms were developed to predict food crises in 21 countries. The best classical logistic regression algorithm achieved a high level of significance (p &lt, 0.001) and precision (0.75) but was deficient in recall (0.20) and f1 (0.32). Of particular interest, the classical algorithm indicated that the vegetation index and the food price index were both positively correlated with food crises. A novel method for performing an iterative multidimensional hyperparameter search is presented, which resulted in significantly improved performance when applied to this dataset. Four iterations were conducted, which resulted in excellent 0.96 for metrics of precision, recall, and f1. Due to this strong performance, the food crisis year was removed from the dataset to prevent immediate extrapolation when used on future data, and the modeling process was repeated. The best “no year” model metrics remained strong, achieving ≥0.92 for recall, precision, and f1 while meeting a 10% f1 overfitting threshold on the test (0.84) and holdout (0.83) datasets. The year-agnostic neural network model represents a novel approach to classify food crises and outperforms current food crisis prediction efforts.