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This study aims to analyze the effect of changes in exchange rates on inflation in Indonesia. It is discussed changes in the exchange rate will affect the use of production factors, especially labor production factors which in turn affect inflation. The research was conducted in Indonesia, with a time period of 2000.1-2020.1. The data used is secondary data published by Bank Indonesia. The data analysis method used is multiple regression analysis with the Error Correction Model (ECM) method. The results showed that Error Correction Term was significant, so it could be concluded that the model specification was correct. In the short term, foreign wages have a positive effect on the inflation , while in the long term, foreign wages have a negative effect on infation in Indonesia. Variable the level of domestic wages in the short term is not significant to inflation, while in the long term this variable has a negative and significant effect on inflation in Indonesia. The foreign price variable shows that the foreign price variable has an effect on n the short and long term. For the exchange rate variable, the results of the study show that the exchange rate has a positive effect in the short and long term on inflation. The long term effect is greater than the short term. This shows that the Exchange Rate Pass Through which works through the use of labor in Indonesia has a greater impact in the long term.

This paper explores the effect of changes in exchange rates on domestic prices, known as Exchange Rate pass Through. The data used in this study are data on the economy in Indonesia in the period 1997.3 to 2017.4. The analytical tool used is multiple regression with the Error Correction model approach. Based on the results of the analysis conducted shows that the effect of the exchange rate on the wholesale price index in the long run is greater than the short run. This shows that the effect of the exchange rate on domestic prices is indirect.The foreign interest rates variabel effect to wholesale price index in the long term, while the domestic interest rates effect in the short and long term. The effect of foreign prices on the wholesale price index is much larger in the short than in the long-term. Meanwhile, the variable of foreign capital in both the short and long-term, it has a positive and significant effect on the wholesale price index.The effect domestic capital variable is different from the ones on foreign capital because in the short term the domestic capital was not significant to the wholesale price index. The effect of domestic capital on the wholesale price index was positive and significant in the long term.The effect of foreign and domestic capitals in the long term on the wholesale price was positive. It suggests that foreign and domestic capital did not substitute each other, but they were complementary.

In this paper, we investigate the short run and long run causal relationship between wholesale price index (WPI) and consumer price index (CPI) in India over the period from 1994 April to 2015 August. Using a linear cointegration and a nonlinear time series model of threshold cointegration with asymmetric error correction model and spectral Granger causality approach, the study attempts to find the linear and nonlinear cointegration relationship between WPI and CPI in India. Empirical results reveal the non-existence of linear cointegration between the WPI and CPI indices in India. Moreover, using consistent threshold autoregressive (C-TAR) and consistent momentum threshold autoregressive (CM-TAR) model, we find the evidence of non-linear cointegration between WPI and CPI in India over the period from 1994 to 2015. The consistent M-TAR model indicates the presence of threshold cointegration between WPI and CPI, which further suggests the consistent inflationary trends after 1995 in India due to rising per capita income and other macroeconomic indicators. Furthermore, results indicate that WPI and CPI are cointegrated with threshold error correction adjustment, and the adjustment towards long-run equilibrium is asymmetric. This further suggests that WPI and CPI respond differently to positive and negative deviations from the long-run equilibrium after the threshold level. Empirical results further indicate that adjustment towards long-run equilibrium tends to persist more for negative deviations and respond more quickly towards positive deviations. The spectral granger causality results do not reveal the causality from WPI to CPI. However, the Granger causality from the asymmetric error correction model reveals the uni-directional causality, which indicates that CPI Granger causes WPI, support the demand-push inflation in India. CPI Granger causes WPI at very low and high frequencies, which takes an average wavelength of more than 3.6 quarters time in a year. Furthermore, empirical results reveal that WPI and CPI reach equilibrium asymmetrically after the threshold level of the two percentages. In sum, results suggest that the Indian policymakers can emphasis on the demand side rather than supply-side inflation to control the level of inflation after the desired threshold level. Moreover, the central bank of India would give importance to control the unwarranted inflationary trend, which has been caused due to the CPI-based demand-side inflation.

For market players and policy officials, commodity price forecasts are crucial problems that are challenging to address due to the complexity of price time series. Given its strategic importance, corn crops are hardly an exception. The current paper evaluates the forecasting issue for China’s weekly wholesale price index for yellow corn from January 1, 2010 to January 10, 2020. We develop a Gaussian process regression model using cross validation and Bayesian optimizations over various kernels and basis functions that could effectively handle this sophisticated commodity price forecast problem. The model provides precise out-of-sample forecasts from January 4, 2019 to January 10, 2020, with a relative root mean square error, root mean square error, and mean absolute error of 1.245%, 1.605, and 0.936, respectively. The models developed here might be used by market players for market evaluations and decision-making as well as by policymakers for policy creation and execution.

Purpose The author aims to examine the long-run dynamic relation between gold price and inflation in the Indian context from 1982 to 2015. The author measures inflation using consumer price index and wholesale price index (WPI). However, this study focuses on the long-run dynamic relation between gold price–WPI inflation. Design/methodology/approach The author uses Johansen’s cointegration technique (Johansen, 1991); single equation error correction model based on Pesaran et al. (2001) and Kanioura and Turner (2005); and the Saikkonen and Lütkepohl (2000) approach. The author also uses a time-varying regression framework in level form based on Kalman filter to examine the dynamic nature of gold–WPI relation. Findings The author finds no evidence of cointegration between gold and WPI. However, The author reports a significant dynamic relation between gold and inflation using a Kalman filter framework, and the comovement between these variables has in fact increased in the past decade. The results further indicate that variation in gold’s sensitivity to inflation can be explained by real effective exchange rate which supports the notion of using gold as an alternative to paper currency. Moreover, the WPI beta of gold is found to be predicted by both short- and long-term interest rate changes highlighting the monetary value of gold as a valuable asset. Practical implications From an emerging economy point of view, the results have implications for policy makers, particularly the central banks. The results of this paper caution the Reserve Bank of India against increasing its gold holdings as a reserve asset presuming that gold would preserve its purchasing power parity, at the same time providing a hedge against inflation. Originality/value To the best of the author’s knowledge, this is the first study to examine the gold price–inflation relation in the Indian market for such a long period of time. More importantly, the study shows that the changes in gold’s long-term sensitivity to WPI can be forecast using fundamental variables like interest rates.

The paper seeks to obtain an optimal asymmetric trimmed mean-based core inflation measure in the class of trimmed mean measures when the distribution of price changes is leptokurtic and skewed to the right for any given period. Several estimators based on asymmetric trimmed mean approach are constructed and estimates generated by use of these estimators are evaluated on the basis of certain established empirical criteria. The paper also provides the method of trimmed mean expression \"in terms of percentile score.\" This study uses 69 monthly price indices which are constituent components of Wholesale Price Index for the period, April 1994 to April 2009, with 1993 - 1994 as the base year. Results of the study indicate that an optimally trimmed estimator is found when we trim 29.5% from the left-hand tail and 20.5% from the right-hand tail of the distribution of price changes.

The Kirchner administration (2002-2015) claimed that under their Leadership Argentina experienced record-breaking GDP growth. However, this article shows that Argentina's GDP growth was underwhelming. Statistical estimates produced by the new Argentine government support the ARKLEMS project's evidence that the Kirchner administration overstated growth. Distortions were large and discretionary and affected all industries, independent of the downward bias of the Consumer Price Index and the Wholesale Price Index. New stylized facts counter the claims of the Kirchner administration. First, real GDP growth in the 2002-2015 period was weaker than in the 1990-1998 period. Second, GDP only grew in the sub-period 2002-2007 because of the commodities boom. Third, GDP growth in Argentina was second lowest among ten Latin American countries in the 1998-2015 period. Fourth, GDP growth during the 2002-2015 period was extensive in nature, based on factor accumulation, not total factor productivity, so was not sustainable.

China’s polyester textile industry is one of the notable contributors to national economy. This paper takes polyester yarn, core raw material in polyester textile industry chain, as research object, and deeply explores its price indicators and risk hedging mechanisms through multiple linear regression models and Holt-Winters approaches. It is worth mentioning that with continuous development of digital technology, digital transformation of production lines and warehouses has become an important development feature in various industries. This study also actively complies with this trend, and innovatively incorporates the upstream and downstream production line start-up rates into price prediction model. Through this initiative, we can more comprehensively consider the impact of supply and demand changes on price of polyester yarn, thus making prediction results more closely reflect the actual market situation. This quantitative analysis method undoubtedly provides new ideas for enterprises to better grasp market dynamics in digital era.

The wholesale price index (WPI) is a crucial economic indicator that provides insights into the pricing dynamics of different goods within a country, especially potato commodities. In this study, we tried to build a hybrid machine learning model technique for predicting the volatile price index of potato. We introduced the Random Forest-Convolutional Neural Network (RF-CNN) model to predict agricultural volatility price index commodities. Traditional statistical time series models (Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH)) were also investigated for comparison with machine learning models (Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN)). Because the data set was volatile, the GARCH model outperformed the ARIMA model which had a lower goodness of fit value. The performance of the SVM model was comparable to that of  the statistical models. However, after utilizing an input lag selection strategy based on autocorrelation function (ACF) and RF, the machine learning models outperformed the statistical models. We used LSTM and CNN models with the appropriate input lag feature assessed by ACF and RF. Our findings indicate that the RF-CNN model beats the other models in terms of error accuracy, with improvements of 67% for root mean square error, 95% for mean absolute percentage error, 63% for mean absolute error and mean absolute squared error on the training set, and more than 90% on the testing set for all goodness of fit. Based on the error accuracy, the RF-CNN model can be utilized to better predict the potato price index in the long term. We hope our study will benefit stakeholders and policymakers by providing a realistic potato price forecast. Furthermore, our study contributes to the growing corpus of research on machine learning models for time series.

Potatoes are one of the widely consumed staple foods all over the world. The prices of potatoes were more unstable than other agricultural commodities because of factors such as perishability, production uncertainties, and seasonal fluctuations. These factors make it difficult for farmers to manage and predict production levels, resulting in supply and price fluctuations. Therefore, it is essential to develop predictive models that can accurately forecast the pricing of agricultural commodities such as potatoes. The study attempted to explore the pattern of potato prices in major markets of northern India using different time series models. The empirical findings indicated positively skewed data distributed with a high instability index. In terms of forecasting accuracy, the EEMD-ANN model exhibited the best performance among the various time series techniques, generating the lowest MAPE values of 9.10%, 12.97%, and 4.27% for the Chandigarh, Delhi, and Shimla markets, respectively. Meanwhile, the EEMD-ARIMA model yielded the most accurate prediction results for the Dehradun market, with an MAPE value of 12.97%. The outcomes of this study offer significant insights to farmers, consumers, and government bodies for making informed decisions regarding the production, consumption, and distribution of potatoes. Moreover, the effectiveness of various time series models in handling complex agricultural price series was also investigated.