Wholesale Price Index Research Articles

Adding Exogenous Variable in Forming ARIMAX Model to Predict Export Load Goods in Tanjung Priok Port

The main idea of world maritime has been launched by the Indonesia’s Government through the development of inter-island connectivity, namely a logistics distribution line system using cargo ships with scheduled routes. However, in terms of inter-island sea transportation connectivity using sea transportation, the number of ships used for loading and unloading activities at Tanjung Priok in 2020 reached 11,876 units, which number decreased by 12.6% compared to the previous year, this figure was not sufficient for transportation of Indonesian loading and unloading goods (exports). This condition is important to note because the implementation of sea transportation, especially for sea toll transportation, if it cannot reach all regions, will cause freight transportation in some areas to be limited and regional economic growth cannot be distributed evenly. The purpose of this study is to predict the number of goods loaded (exported) at the Port of Tanjung Priok, by establishing an export forecasting model. Exogenous variable in the form of the Indonesian Wholesale Price Index. After analyzing the data, the order of the ARIMA model (5,1,1) was obtained as a parameter to estimate the ARIMAX model. From the ARIMAX model (5,1,1), the model's accuracy rate is 13.25% which is quite feasible to use to predict the total export cargo for the period January 2021-December 2021. Forecasting results show better fluctuations than in 2020.

A Measure of Inflation in Pakistan

Although a number of series on wholesale and retail prices are being published by the Central Statistical Office and other organizations, there is no general price index to measure annual price changes in the country. The price indices (composed of more than one commodity) presently available in Pakistan are cost of living indices of limited coverage. This mono¬graph presents an annual wholesale price index for the purpose of measuring the extent of inflationary pressure in Pakistan. The index is computed for the East and West wings separately and for the whole of Pakistan. It dates from 1951-52 through 1959-60 (July-June), with 1951-52 as the base year. Owing to lack of data, it was not possible to carry the index back to Partition, while the need for accuracy precluded the construction of the index on a quarterly or monthly basis. The choice of 1951-52 as the base year is inevitable if the special conditions of the Korean Boom, its aftermath and the Plan period are to be avoided. Moreover, 1951-52 was a year of fewer controls, and prices in that year did not differ much from the preceding two years.

Macroeconomic Determinants of Housing Finance in Pakistan

The concept of human welfare cannot be imagined without housing as it is one of the basic needs of human life. It is alpha and omega for social inclusion and standard of living. The persons need a house for a variety of reasons including protection, security, space and other services and utilities attached with the houses. The people who do not have adequate housing facilities may encounter many pitfalls. This study explains the macroeconomic determinants of housing finance in Pakistan based on quarterly data ranging from 2005Q1 to 2018Q4. Vital factors that influence the housing finance in Pakistan are interest rate, per capita income, urbanization and wholesale price index. Findings expose that the interest rate and wholesale price index are negatively while per capita income and urbanization are positively related to housing finance of Pakistan.

PENGARUH JUMLAH UANG BEREDAR, NILAI TUKAR, FOREIGN DIRECT INVESTMENT, DAN INDEKS HARGA PERDAGANGAN BESAR TERHADAP EKSPOR INDONESIA TAHUN 2009-2021

This study aims to determine the effect of the money supply, exchange rate, foreign direct investment and wholesale trade price index on exports in Indonesia. This study uses a quantitative approach. The method used is Error Correction Model (ECM). The data obtained are secondary data published by the Central Statistics Agency (BPS) in the form of money supply, the wholesale and export price index, the Ministry of Trade in the form of exchange rates and the Investment Coordinating Board (BKPM) in the form of foreign direct investment. The results of this study indicate that the money supply has a positive and significant effect on Indonesian exports in the short and long term. Meanwhile, the exchange rate and foreign direct investment have a negative and significant impact on Indonesia's exports in the short and long term. Also, the wholesale price index has a positive and significant impact on Indonesian exports in the short term.

Canola and soybean oil price forecasts via neural networks

Forecasts of commodity prices are vital issues to market participants and policy-makers. Those of cooking section oil are of no exception, considering its importance as one of main food resources. In the present study, we assess the forecast problem using weekly wholesale price indices of canola and soybean oil in China during January 1, 2010–January 3, 2020, by employing the non-linear auto-regressive neural network as the forecast tool. We evaluate forecast performance of different model settings over algorithms, delays, hidden neurons, and data splitting ratios in arriving at the final models for the two commodities, which are relatively simple and lead to accurate and stable results. Particularly, the model for the price index of canola oil generates relative root mean square errors of 2.66, 1.46, and 2.17% for training, validation, and testing, respectively, and the model for the price index of soybean oil generates relative root mean square errors of 2.33, 1.96, and 1.98% for training, validation, and testing, respectively. Through the analysis, we show usefulness of the neural network technique for commodity price forecasts. Our results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

The volatility spillover effect of macroeconomic indicators and strategic commodities on inflation: evidence from India

Purpose This study aims to analyze the volatility spillover effects of crude oil, gold price, interest rate (yield) and the exchange rate (USD (United States Dollar)/INR (Indian National Rupee)) on inflation volatility in India.Design/methodology/approach This study uses the multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models (Baba, Engle, Kraft and Kroner [BEKK]-GARCH and dynamic conditional correlation [DCC]-GARCH) to examine the volatility spillover effect of macroeconomic indicators and strategic commodities on inflation in India. The monthly data are collected from January 2000 till December 2020 for the crude oil price, gold price, interest rate (5-year Indian bond yield), exchange rate (USD/INR) and inflation (wholesale price index [WPI] and consumer price index [CPI]).Findings In BEKK-GARCH, the results reveal that crude oil price volatility has a long time spillover effect on inflation (WPI). Furthermore, no significant short-term volatility effect exists from crude oil market to inflation (WPI). However, the short-term volatility effect exists from crude oil to inflation while considering CPI as inflation. Gold price volatility has a bidirectional and negative spillover effect on inflation in the case of WPI. However, there is no price volatility spillover effect from gold to inflation in the case of CPI. The price volatility in the exchange rate also has a negative spillover effect on inflation (but only on CPI). Furthermore, volatility of interest rates has no spillover effect on inflation in WPI or CPI. In DCC-GARCH, a short-term volatility impact from all four macroeconomic indicators to inflation is found. Only crude oil and exchange rate have long-term volatility effect on inflation (CPI).Practical implications In an economy, inflation management is an essential task. The findings of the current study can be beneficial in this endeavor. The knowledge of the volatility spillover effect of all the four markets undertaken in the study can be significantly helpful in inflation management, especially for inflation-targeting policy.Originality/value It is observed that no other study has addressed this issue. We do not find any other research which studies the volatility spillover effect of gold, crude oil, interest rate and exchange rate on the inflation volatility. The current study is novel with a significant contribution to the vast knowledge in this context.

An Econometric Analysis of Food Inflation in India

"In India, Food inflation seems to be persistent in recent years. The objective of this study was to determine the relationship between macroeconomic growth variables and food inflation in India. The period of study was from 1982-83 until 2019-20, the data on food wholesale price index, consumer price index for agricultural labour, interest rate and exchange rate were collected from various secondary sources; dummy variables of trade liberalisation and National Food Security Mission were utilised in this study. The collected data were analysed to check cointegration relationships among the variables by using the Johansen Cointegration Test (JCT) and Vector Error Correction Model (VECM). From 1982-83 to 2019-20, food inflation has been increasing at a rate of 7.47 per cent per annum. Among all the commodities eggs, meat and fish (148.9 %) were found to have a high percentage change in inflation over the last decade (2010 to 2020). The JCT results revealed a long-run cointegration relationship between variables, with three cointegration equations. The error correction model result suggested the existence of a short-run relationship between the variables and the previous year’s error term was corrected at a 12.6 per cent convergence speed within the year. The Chow test was used to estimate the presence of structural breaks, and the findings (Fcal>Ftab) revealed that there was a substantial difference between the coefficients of the three groups. The core idea of the study is that food inflation and other macroeconomic targets must be consistent."

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

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

Systemic spillover dynamics of crude oil with Indian Financial indicators in post WPI revision and COVID era

The study first investigates the sensitivity of major Indian financial indicators, viz. Equity Index (Nifty), Exchange Rate (USDINR), Bond (Govt 10Y Bond), Gold, Agricultural Index (N-Krishi) to ascertain the existence of significant sensitivity to standard shocks Engle and Campos-Martins (2020) either arising due to global political/economic factor or endogenous macroeconomic scenario. After that, the study undertakes the systemic spillover dynamics of Crude by estimating a self-exciting regime-switching Threshold Vector Auto-regression model based on cut-off values of Crude to test shock transmission from Crude amid major global events empirically. The findings indicate the existence of three regimes with threshold cut-offs for Crude return, viz. −2.358% and 2.294% for 2008 and 2020, respectively. After that Spillover Index is estimated across the three regimes. The findings indicate a sporadic increase in spillover during the Covid era, GFC and Oil Crisis. Despite Govt. Bond ranking high insensitivity, the spillover linkage is low. The results also indicate that, on average, systemic volatility shocks from crude oil are highest in Gold. In fact, Gold's sensitivity to crude price fluctuations escalates to new heights during the 2008–09 crisis, thus serving as a source of idiosyncratic shocks. Moreover, in recent duration, a unique see-saw kind of link has emerged between crude oil and Gold, where downward pressure by Crude on USDINR is eased by a fall in gold imports. Moreover, an analysis of the spillover pattern across the Wholesale Price Index (WPI) 2012 shows an intensification of spillover from Crude. The study is beneficial to policymakers to apply a systemic approach while empirically analyzing the spillover from a Global commodity such as Crude.

Factors Affecting Movement of Indian Stock Market: A Study with Special Reference to CNX Nifty

Stock markets are often referred to as barometer of an economy. Stock market indices not only tell us about the condition of stock markets but in a way they also reflect the health of an economy. Stock prices do not move in vacuum rather they are affected by a large number of factors. The objective of the study is to identify the factors affecting the movement of Indian stock market and the impact of these factors on the same. For the purpose of this study, CNX Nifty has been used to represent the movement of Indian stock markets. CNX Nifty is a well-diversified index consisting of 50 large cap companies representing most of the important sectors of Indian economy. The study deals with the time series data and consists of eleven variables i.e. Oil Prices, S&P 500, Nikkei, FTSE, NASDAQ, Wholesale Price Index as a proxy for inflation. Index of Industrial Production, Foreign Exchange Rate, Current Account Deficit, FII Investment and CNX Nifty. The study has been conducted over a period of 32 quarters from financial year 2005-06 to 2012-13. Researchers have used Principal Component Analysis and Regression Analysis for conducting the study and drawing the conclusions. All these variables have been clubbed into two factors namely Macroeconomic Factors and International Factors. The study revealed that both these factors i.e. macroeconomic factors as well as international factors have significant impact on movement of CNX Nifty.

Non-Linear Relationship between Macroeconomic Variables and Stock Prices in India: An Artificial Neural Networks Approach

Neural networks are powerful forecasting tools that draw on the most recent developments in artificial intelligence research. They are non-linear models that can be trained to map past and future values of time series data and thereby extract hidden structures and relationships that direct the data. Many studies have shown that artificial neural networks have the capacity to learn the underlying mechanics of stock markets. In fact, artificial neural networks have been widely used for forecasting financial markets. However, such applications to Indian stock markets are scarce. The paper investigates the application of artificial neural networks to the dynamic interrelations between macroeconomic variables i.e. Foreign Exchange rate, Foreign Exchange reserves and Wholesale price index. Multilayer perceptron network is used to build the monthly prices model for CNX Nifty and the network is trained using Error Back Propagation algorithm. It is found that the predictive power of the network model is influenced by the previous values. The study shows that satisfactory results can be achieved when applying neural networks to predict the Indian Stock prices.

Food Price Inflation and Relative Price Variability in India: Evidence from Decomposition Analysis

The present study examines the decomposition of relative price variability into two components (i.e., inflationary and real factors) in India using monthly data on 105 commodities prices over the period January 2005 to March 2017. The analysis of the study revealed that under primary food articles, 53% of relative price variability is due to real factors, and the remaining 47% due to inflationary factors. Whereas for manufactured food products, 30% of relative price variability is due to real factors, and the rest of the 70% due to inflationary factors. Overall, the important conclusion that emerges from the analysis is that few commodity prices have a larger contribution to relative price variability in the food basket. The majority of commodity prices under primary food articles have a larger contribution to variability in relative price changes, whereas under manufactured food products have the least contribution. Moreover, this study also accounts for wholesale price index and consumer price index non-food commodities to identify to which extent relative price variability is determined by real and inflationary factors. JEL Classifications: E30, E31, Q11, L66

Impact of the global financial crisis on the Indian economy

This paper provides a descriptive review of the impacts of the global financial crisis on the Indian economy. While examining the growth in the gross domestic product (GDP), fiscal deficit, exports, imports, Wholesale Price Index (WPI) inflation, and the foreign exchange rates (FX), we find that the GDP has been severely impacted during the crisis period, especially during 2008-09. While the fiscal deficit continuously increased during 2008-09 and 2009-10, the exports and imports declined considerably. The WPI inflation followed a downward trend, and the rupee depreciated by approximately 20 percent during 2008. During 2008-09, the agricultural and industrial growth significantly declined, but the service sector sustained itself. The findings also reveal that the policy initiatives by the Indian government and those by the Reserve Bank of India safeguarded the Indian economy to a large extent.

The external debt‐inflation nexus in Egypt

Egypt has a long history with external financing dating back to the 19th century. Nevertheless, Egypt's external debt has been mounting remarkably in the past few years. This paper examines the impact of Egypt's external debt on its price level, a topic ignored by researchers. Nonetheless, as inflation is a hydra‐headed problem, this paper develops several models which incorporate fiscal and monetary policies besides other inflation‐inducing internal and external factors using monthly data extending from 2000M1 to 2020 M1. By employing ARDL cointegration analyses on monthly time series variables and using Egypt's wholesale price index to account for inflation, the paper concludes that external debt raises prices both in the short and long runs. Moreover, money supply and interest rate were also found to increase prices in the long run long despite decreasing them in the short run. Finally, upsurges in the international prices of primary products stimulate domestic prices both in the short and long terms, while depreciation in the local currency aggravates inflation in the short and long terms as well. As external debt raises inflation and affects many other inflation‐inducing factors indirectly, the paper concludes that reducing Egypt's external debt may help in curbing Egypt's inflation.

Seçimlerin Ekonomik Etkileri: Türkiye Üzerine Bir Uygulama

This study aims to explore the effects of election periods on the economy from the perspective of the “public choice theory”. We selected our variables from among those focused by the research that examine elections from the standpoint of political business cycles; i.e., exports, Gross Domestic Product (GDP), Wholesale Price Index(TEFE), the number of unemployed, and non-performing loans. Then, we constructed simultaneous models in which these variables were explored as endogenous variables. We used dummy variables for general and local elections in these models and identified 2002:04-2020:04 as our study period. We estimated the coefficients for the econometric models we constructed by using the Engle-Granger and Dynamic Least Squares methods. Our analysis results demonstrate that manipulative decisions and arrangements made before elections do not always create business cycles. Expenditures during election periods impose a permanent burden on the system and the economic arrangements during election periods may not result in political business cycles. Moreover, it is harder for the government in power to manipulate policy objectives than to manipulate policy instruments. Because policy instruments such as public expenditure and money supply are largely controlled by the government in power, while government intervention in indicators such as Gross National Product, exports, inflation, and unemployment is relatively limited. Keywords: Engle-Granger Method, Dynamic Least Squares method, Political Business Cycles. Jel Code: E32, C01, G18

Prediction of national agricultural products wholesale price index in China using deep learning

Prediction of national agricultural products wholesale price index in China using deep learning

Gold Prices, World Oil Prices and Large Trade Price Index on Indonesian Sharia Stock Index with Exchange Value as A Moderating Variable in Indonesia

The aim of this study was to determine how much influence the Gold Price, World Oil Price and Wholesale Price Index have on the Indonesian Sharia Stock Index with the Exchange Rate as a Moderating Variable in Indonesia. This study uses a quantitative approach to secondary data with the MRA analysis method. The results of his research show that the price of gold has a positive and significant influence on the Indonesian Islamic Stock Index; World oil prices have a positive and significant impact on the Indonesian Islamic Stock Index; Wholesale Price Index has a negative and significant influence on the Indonesian Islamic Stock Index; The rupiah exchange rate moderates (strengthens) the relationship between the gold price and the Indonesian Sharia Stock Index; The rupiah exchange rate moderates (strengthens) the relationship between world oil prices and the Indonesian Sharia Stock Index; The rupiah exchange rate moderated (strengthened) the relationship between the Wholesale Price Index and the Indonesian Sharia Stock Index.

The Impact of Macroeconomic Factors on the Indian Stock Market : An Assessment

The present study attempted to analyze the relationship between selected macroeconomic variables: interest rate, inflation rate, foreign institutional investments, foreign exchange reserves, gold prices, money supply, and India’s stock market index, that is, the Bombay Stock Exchange from April 2012 – June 2020. Various studies in the well-documented literature have examined the association between macroeconomic variables and the stock market in diverse ways and forms. This paper encapsulated the long-run and short-run dynamics of the relation between the variables, as mentioned above, with the ARDL model’s help. The empirical analysis displayed a strong liaison between India’s stock market and money supply, foreign institutional investment, foreign exchange reserves, wholesale-price index, and call money rate (interest rate). Further, it also showed a negative and significant long-term adjustment coefficient.

Impact of macroeconomic variables on sustainability indices using ARDL model

ABSTRACT The present study is conducted to check the effect of the collection of macroeconomic variables on Indian sustainability indices. The independent variables adopted are Industrial Index, Wholesale Price Index, Economy Money Supply (M3), Crude Oil Prices, Real Effective Exchange Rate (REER), while dependent variables are S&P BSE GREENEX and S&P BSE CARBONEX. The study uses monthly data (converted into natural log form) from April 2012 to March 2021. Our study shows that GREENEX is linked with the index of Industrial Production, Wholesale Price Index, M3, Crude Oil Prices, and (REER) and CARBONEX is linked with the index of Industrial Production, M3, Crude Oil Prices, and REER while it is not linked with wholesale prices index. There is no association of interest rates with any of the indices taken (GREENEX and CARBONEX).

Examining the Effects of Food and Product Production Values and Production Added Value on Inflation Over the Years: Empirical Evidence for Turkey

In this study, it is aimed to examine the effects of food and product production values on inflation. In the study, the variables of the World Bank Country Reports between 1991-2019 Consumer Price Index, Wholesale Price Index, Food Production Index, Product Production Index and Production Value Added were used. According to the results obtained from the study, there is a statistically significant relationship between TUFE and TOFE and GUE, UUE and UKD variables (p <0.01). According to the results of controlled correlation analysis, the effects of food and product production indexes on consumer and wholesale inflation level are not statistically significant (p> 0.05). The effect of UKD and GUE parameters on inflation is statistically significant (p <0.05). Explanation power of both models is very high. According to the regression coefficients, UKD has a negative effect, and GUE has a positive effect. The results show that production has a positive effect on inflation, while production value added has a decreasing effect on consumer and wholesale prices. These results show that the production in our country is actually high cost and its added value is low.