Predicting Inflation Component Drivers in Nigeria: A Stacked Ensemble Approach

Our study examined the disaggregation of inflation components in Nigeria using the stacked ensemble approach, a machine learning algorithm capable of compensating the weakness of a base learner with the strength of another. This approach gives flexibility of a synergistic performance of stacking each base learner and produces a formidable model that yields the highest level of accuracy and best predictive ability. We analyzed the test data, out-of-sample, and our results show a strong accuracy in predicting inflation. Our results further show that food CPI is the most important driver for headline, urban, and rural inflation while bread and cereals is the most important driver for food inflation. However, biscuits, agric rice, garri white are among the top main drivers of bread and cereal inflation. We note that some CPI items that mostly drive inflation have lower weights while others have higher weights therefore, focusing entirely on CPI weights as a policy guide will stymied a successful control of inflation in Nigeria. In addition, ignoring CPI items with lower weights in policy intervention will make inflation difficult to control. Above all, adequate trace of the source of inflation to the least sub-component of each component will help address or formulates an appropriate policy to confront inflation problems in Nigeria.JEL: C53, E37

Management of New Risks in Economic and Food Security

The article examines the consequences of the coronavirus in the form of a system of risks. The main goal of the study is assessment the risks of economic food security at post-COVID period. System, institutional and comparative analysis were used as methods of research. There are five types of risks in the research. We assessed monopolization in breeding, seed production and chemicalization, lockdown and value chain disruptions, increasing income inequality, food inflation, and climate changes. The introduction to the theory of nondestructive development and non-aggressive coexistence is described in the article. The authors propose a concept of non-destructive development, in which a special place is given to respect for nature, reducing aggressiveness between countries, reducing the monopolization of breeding and seed production, and reducing income inequality around the world. The monopolization of breeding and seed production is of particular concern, as it can lead to a decrease in food availability and a decrease in food security and independence. The gaps in the value chains due to the lockdown have already led to a decrease in the availability of food in individual countries. A further food crisis must not be allowed as it is fraught with social upheaval. The authors insist on the need to reduce aggression in the world, since a post-like revival of the economy is impossible in an unfavorable political situation in the world.

Analysis of Food Inflation in Java and Sumatra Islands

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.

Measuring food inflation during the COVID-19 pandemic in real time using online data: a case study of Poland

PurposeThe purpose of this study paper is to focus on developing novel ways to monitor an economy in real time during the COVID-19 pandemic. A fully automated framework is proposed for collecting and analyzing online food prices in Poland. This is important, as the COVID-19 outbreak in Europe in 2020 has led many governments to impose lockdowns that have prevented manual price data collection from food outlets. The study primarily addresses whether food price inflation can be accurately measured during the pandemic using only a laptop and Internet connection, without needing to rely on official statistics.Design/methodology/approachThe big data approach was adopted to track food price inflation in Poland. Using the web-scraping technique, daily price information about individual food and non-alcoholic beverage products sold in online stores was gathered.FindingsBased on raw online data, reliable estimates of monthly and annual food inflation were provided about 30 days before final official indexes were published.Originality/valueThis is the first paper to focus on measuring inflation in real time during the COVID-19 pandemic. Monthly and annual food price inflation are estimated in real time and updated daily, thereby improving previous forecasting solutions with weekly or monthly indicators. Using daily frequency price data deepens understanding of price developments and enables more timely detection of inflation trends, both of which are useful for policymakers and market participants. This study also provides a review of crucial issues regarding inflation that emerged during the COVID-19 pandemic.

Global commodity prices and inflation expectations

PurposeIn this paper, we study the effect of global energy and food inflation on household inflation expectations during the period 1988M01–2020M03 for a set of European economies.Design/methodology/approachWe use multifractal de-trended cross-correlation analysis to estimate the non-linear and time-varying cross-correlation. We provide additional robustness tests using the Autoregressive-Distributed Lag method.FindingsWe find that household inflation expectations, global energy inflation and global food inflation are all multifractal. We also find that the household inflation expectations, global energy inflation and global food inflation are positively correlated (i.e., they are persistent). However, household inflation expectations respond more when the volatility of the global energy inflation is lower than when the volatility is higher. The correlation between household inflation expectations and global food inflation does not depend on the level of volatility.Research limitations/implicationsFirst, paying attention to the global commodity inflation might help anchor inflation expectations better. It is so because Central Bank's efficacy in achieving price stability may be weakened if there is a relationship between commodity inflation and inflation expectation. This task would become even more difficult in the average inflation targeting regime than inflation targeting regime if actual inflation is persistently different from the target inflation. Second, our results also emphasize the importance of effective strategy for communicating to households about actual inflation, inflation target and keep them updated about how monetary policy functions.Originality/valueWe contribute to the literature by estimating the cross-correlation between household inflation expectations with the global commodity inflation, conditional to the volatility of the commodity inflation under consideration.