Core Inflation Research Articles

Determinants of Household and Professional Forecasters’ Inflation Expectations in India

ABSTRACTThis study analyses and compares the determinants of inflation expectations among households and professional forecasters in India. To this end, we utilize quarterly survey data from the Reserve Bank of India (RBI), specifically the Inflation Expectations Survey of Households (IESH) and the Professional Forecasters’ Survey (SPF). The analysis employs a non‐recursive structural vector autoregressive (SVAR) model to explore the relationships between variables. The impulse response function and variance decomposition analysis shed light on the response of inflation expectations to macroeconomic shocks. Our findings indicate that apart from conventional macroeconomic factors like realized inflation and monetary policy, exogenous factors such as oil prices significantly shape inflation expectations. Comparing IESH and SPF, we found out that household expectations are more sensitive to shocks related to food prices, reflecting the influence of food expenses on household decision‐making. Conversely, professional forecasters’ expectations are more influenced by core inflation. Finally, we observed that inflation expectations adjust more quickly than realized inflation in response to macroeconomic shocks, suggesting a leading role. This study is novel with the contribution to the existing literature as it provides a comparative analysis of determinants of inflation expectations between households and professional forecasters in India, while also considering inflation targeting as a significant factor.

Electricity Price and Inflation: Macroeconomic Implications of Real-Time Pricing

Electricity prices can be an important driver of inflation. However, the transmission from wholesale electricity price to inflation depends on the type of pricing mechanism in the retail market. Using the introduction of Real-Time Pricing (RTP) in the Spanish retail electricity market, we analyze the transmission of wholesale electricity price shocks to inflation dynamics and how this transmission is affected by the introduction of RTP. The main findings are that—after RTP was rolled out—electricity price shocks display a swifter transmission to inflation while the maximum impact is around three times higher than in the previous period. This higher transmission is not only generated by a more pronounced reaction of the retail electricity price but is also caused by a greater transmission of these shocks to core inflation. These results imply that introducing RTP impacts inflation dynamics and, therefore, affects how central banks should react to this type of shock. JEL Classification: E31, Q43, Q48

Do petrol prices affect inflation and inflation expectations? Evidence from New Zealand

Due to their high visibility, petrol prices shape households' inflation expectations. This paper investigates the dynamic effects of petrol price shocks on one- and five-year inflation expectations in New Zealand; effects on headline and core inflation are also analyzed. Estimating partially identified structural vector autoregression models using Bayesian techniques, we show that petrol price shocks had a slightly delayed but persistent effect on one-year inflation expectations, whereas five-year inflation expectations were largely insensitive to these shocks. We also show that headline inflation increased immediately in response to petrol price shocks, while core inflation remained unaffected. Counterfactual analysis reveals that inflation and inflation expectations would have been higher in the absence of petrol price shocks during the second half of 2020. The results underscore the importance of petrol prices in influencing households' short-term inflation expectations, offering valuable insights for policymakers focused on managing the cost of living and inflation expectations in New Zealand.

Singular spectrum analysis to estimate core inflation in Brazil

This paper presents a set of new core inflation measures for Brazil based on the Singular Spectrum Analysis (SSA) method. The measures are based on the official target used in the Brazilian inflation targeting regime. The period of analysis ranges from the beginning of the inflation targeting regime in 1999 and 2021. The SSA measures were compared to the inflation core ones used by the Central Bank of Brazil, through the evaluation of unbiasedness, short-term adjustment dynamics and predictive ability. The measures estimated by SSA meet the desired properties and have a greater predictive capacity than the other inflation cores.

RETRACTED: Corrigendum to “Geopolitical oil price uncertainty transmission into core inflation: Evidence from two of the biggest global players” [Energy Econ. 126 (2023), 106983]

RETRACTED: Corrigendum to “Geopolitical oil price uncertainty transmission into core inflation: Evidence from two of the biggest global players” [Energy Econ. 126 (2023), 106983]

Greenflation, a myth or fact? Empirical evidence from 26 OECD countries

Among the various effects of the green transition on the macroeconomy, we examined the unintended effects of the green transition on inflation, the so-called “greenflation”. We analyzed panel data from 26 OECD countries covering the period from 2005 to 2019 using four models based on the New Keynesian Phillips curve model. To date, only a few studies have examined greenflation, particularly in terms of empirical investigation. The main contribution of this study to the literature is to provide empirical evidence of greenflation. We also provide empirical evidence on the issues in the NKPC literature that the output gap better explains inflation dynamics than unit labor costs and confirm the demise of the Phillips curve. Estimates from the Common Correlated Effect Mean Group (CCEMG), Augmented Mean Group (AMG), and Panel Corrected Standard Error (PCSE) models indicate that the green transition has an upward inflationary impact on headline inflation, but no significant impact on core inflation. In addition, we have examined four circumstances under which the green transition may affect inflation that have not been sufficiently discussed in the literature: increased green transition spending, carbon pricing, increased demand for critical minerals, and the low energy density of renewable energy. Each of these circumstances has the potential to exert both upward and downward pressures simultaneously. Our suggestion to policymakers is that they do not yet need to change their current view of monetary policy with respect to greenflation, but they should carefully consider policy designs that take into account the unintended effects of the energy transition.

Oil jump tail risk as a driver of inflation dynamics

In this paper, we look at the role of various oil jump tail risk measures as drivers of both U.S. headline and core inflation. Those measures are first computed from high-frequency oil future prices and are then introduced into standard regression models in order to (i) assess in-sample determinants of inflation, (ii) assess overtime the evolution of inflation drivers, (iii) estimate impulse response functions and (iv) forecast inflation out-of-sample for various horizons. Empirical results suggest that oil jump tail risk measures contain useful information to describe inflation dynamics, generally leading to upward inflationary pressures. Even after controlling from standard variables involved in a Phillips curve, goodness-of-fit measures show evidence of a gain, in particular for headline inflation. Overall, we observe that oil jump tail risk measures are contributing more to inflation dynamics since the Covid-19 crisis.

Understanding the international rise and fall of inflation since 2020

This paper analyzes inflation dynamics in 21 advanced and emerging market economies since 2020. We decompose inflation into core inflation as measured by the weighted median inflation rate, and headline shocks—deviations of headline inflation from core. Headline shocks occurred largely on account of energy price changes, although food price changes and indicators of supply chain problems also played a role. We explain the evolution of core inflation with two factors: the strength of macroeconomic conditions—measured by the unemployment gap, the output gap, and the ratio of job vacancies to unemployment—and the pass-through into core inflation from past headline shocks. We conclude that the international rise and fall of inflation since 2020 largely reflected the direct and pass-through effects of headline shocks. Macroeconomic conditions generally played a secondary role. In the United States, estimated price pressures from strong macroeconomic conditions had been greater than in other economies but have eased.

Motor fuel and core inflation

We ask whether there exists a fixed exclusion method that is simpler to compute than the CPI excluding food and energy, yet also closely tracks the behavior of outlier exclusion measures such as the trimmed mean. We document that the CPI excluding motor fuel achieves both goals.

The asymmetric effect of monetary policy on core inflation in Jordan: NARDL model

This study aimed to estimate the asymmetric impact of monetary policy on core inflation rates in Jordan during the period 2000-2020 using the Nonlinear Autoregressive Distributed Lag Model (NARDL) Based on quarterly data. The unit root tests showed the different degrees of integration by applying the Augmented Dickey-Fuller Test (ADF) and Philip Perron (P.P). and has a cointegration relationship using Johansson's test, and the Bound test. Also, there is a nonlinear relationship in the time series using waled test. The study used Core Inflation (CORINF) as a dependent variable (calculated based on excluding energy and food prices method) and the overnight deposit rate (CBJR), the loan and advances interest rate (LR), the growth in money supply (GM2), and the growth in real GDP (GRGDP) used as independent variables. The results indicated that there are asymmetric effects of both CBJR and GM2 on core inflation, as a 1% increase in GM2 leads to an increase in the CORINF by 1.3%, while its decrease by 1% leads to a decrease in the CORINF by 1.2%, in contrast, an increase in the LR by 1% leads to a decrease in the CORINF by 0.15% and a decrease by 1% leads to a rise in the CORINF by 0.44%, and the study also found that a rise in LR by 1% lead to a decrease in the CORINF by 0.43%, while its decrease by 1% leads to a rise in the CORINF by 0.44%.

From bottlenecks to inflation: Impact of global supply-chain disruptions on inflation in select Asian economies

Supply-chain bottlenecks emerged alongside unprecedented inflationary surges amid the resulting mismatches in supply and demand for goods and services throughout the COVID-19 pandemic. This study employs a local projection approach to investigate the inflationary impact of global supply-chain pressures in the Philippines and select East Asian neighbors. We find that supply-chain disruptions are followed by sizable and statistically significant increases in headline inflation in the Philippines, Rep. of Korea, Thailand, and Singapore. Supply-chain shocks also have a considerable impact on the tradable segment of core inflation. Meanwhile, non-tradable inflation has a generally smaller, short-lived, and insignificant response to global supply-chain pressures reflecting the limited presence of second-round, indirect effects.

Geopolitical risk, supply chains, and global inflation

AbstractThis paper investigates the effects of global geopolitical risks and global supply chain pressures on global inflation for the monthly period of 1999M1–2022M12. The investigation is based on a structural vector autoregression model, where the effects of global oil prices and global monetary policy are controlled for. Four alternative measures of inflation are used, including headline, core, food, and energy inflation. The empirical results show that disruptions in global supply chains are the main drivers of global inflation in the long run as the corresponding shocks explain the lion's share of volatilities in headline inflation (by 32%), core inflation (by 30%), and food inflation (by 22%), followed by oil price shocks and policy rate shocks. In comparison, energy inflation is explained the most by oil price shocks (by 55%) followed by supply chain shocks and policy rate shocks. Positive supply chain pressure and oil price shocks have positive and statistically significant effects on headline inflation even after five years, whereas positive policy rate shocks have negative and statistically significant effects on headline inflation in the long run. In contrast, positive shocks to geopolitical risk result in higher headline inflation only up to one year, with insignificant effects in the long run. Several policy implications follow.

Consumption categories, household attention, and inflation expectations: Implications for optimal monetary policy

What inflation measure should central banks target? This paper highlights a mechanism where monetary policy optimally targets headline inflation if households pay limited attention to different consumption categories when forming inflation expectations. This result stands in contrast to standard rational expectations models, where optimal policy targets core inflation. The core inflation rate excludes volatile energy and food prices (non-core) from headline inflation. Using novel survey data on inflation expectations for disaggregated consumption categories, I find household expectations are disproportionately driven by beliefs about future non-core prices. A model of bounded rationality accounts for the empirical evidence. While forming inflation expectations, households pay more attention to the volatile non-core components. Embedding this framework into a multi-sector New Keynesian model, I show that targeting headline rather than core inflation provides welfare gains.

AN EMPIRICAL TAKE ON THE “PLUCKING” MODEL

Traditional estimates of potential output based on the symmetric business cycle (boom–bust) view are prone to large retrospective revisions. The plucking view, which is supported by empirics, offers possible respite. Serious attempts to estimate the output ceiling in the plucking view, analogous to potential output in the traditional view, are scarce. This paper has three parts. The first estimates the output ceiling in a computationally simple approach. The second analyzes the relationship of the resultant output gap with inflation and with the unemployment rate. A comparison is made against output gap estimates based on prevailing methods. The third uses a vector autoregression (VAR) to analyze responses to supply and demand shocks. The paper finds that the plucking model offers three points of improvement over prevailing methods based on the boom–bust view. First, boom–bust estimates are prone to large retrospective revisions when two-sided filters are used, while the plucking estimates are not. Second, output gap estimates from both views do not correlate well with core inflation. This absence of evidence for a Phillips curve is problematic for the boom–bust view. The plucking estimates correlated well with the unemployment rate but the boom–bust estimates did not. Third, the output ceiling from the plucking model responded only to supply (oil price) shocks and not demand (interest rate) shocks, hence fits the description of a supply measure. This paper provides a template to estimate the output ceiling of the plucking model in other countries using publicly available macroeconomic data.

Exploring an LSTM-SARIMA routine for core inflation forecasting

The object of the research is the Core Inflation Forecasting. The paper investigates the performance of the novel model routine in the exercise of the Core Inflation Forecasting. It aggregates 300+ components into 6 by the similarity of their dynamics using an updated DTW algorithm fine-tuned for monthly time series and the K-Means algorithm for grouping. Then the SARIMA model extracts linear and seasonal components, which is followed by an LSTM model that captures non-linearities and interdependencies. It solves the problem of high-quality inflation forecasting using a disaggregated dataset. While standard and traditional econometric techniques are focused on the limited sets of data that consists just a couple of variables, proposed methodology is able to capture richer part of the volatility comprising more information. The model is compared with a huge pool of other models, simple ones like Random Walk and SARIMA, to ML models like XGBoost, Random Forest and simple LSTM. While all Data Science model shows decent performance, the DTW+K-Means+SARIMA+LSTM routine gives the best RMSE over 1-month ahead and 2-month ahead forecasts, which proves the high quality of the proposed forecasting model and solves the key problem of the paper. It is explained by the model's capability to capture both linear/seasonal patterns from the data using SARIMA part as long as it non-linear and interdependent using LSTM approach. Models are fitted for the case of Ukraine as long as they’ve been estimated on the corresponding data and may be actively used for further inflation forecasting.

SOLVING THE PUZZLE OF CHINA’S LOW INFLATION: A NEW PERSPECTIVE FROM SECTORAL CORE INFLATION FLUCTUATIONS

China’s constantly rapid economic growth accompanying by a low overall inflation has long been mysterious in macroeconomics. The core purpose of this paper is to solve this puzzle. Therefore, we integrate overdetermined set of equations into a MUCSVO model to explore the volatility mechanism of the overall inflation from a sectoral perspective. Our key findings include: 1) the hedging effect of sectoral inflation fluctuations principally accounts for China’s long-run stable overall inflation; 2) the main contradiction of China’s inflation has been shifting from high price levels in the traditional food and residence categories to rising prices in the health care category; 3) as the proportions of inflation in the food and residence categories fall steadily, sectoral inflation weights become more evenly distributed. In conclusion, China’s overall inflation and deflation will be much less likely to occur, while inflation is still of sectoral imbalance. Unusual price fluctuations in the food and health care categories, which are highly relevant to basic living standards of the low-income group, deserve close attention in particular. Overall, besides solving the puzzle of China’s low inflation, our model is applicable to economies that do not publish inflation weights, which is a useful extension of core inflation measurement.

Weighted Median Inflation Around the World: A Measure of Core Inflation

The standard measure of core or underlying inflation is the inflation rate excluding food and energy prices. This paper constructs an alternative measure, the weighted median inflation rate, for 38 advanced and emerging economies using subclass level disaggregation of the CPI over 1990–2021, and compares the properties of this measure to those of standard core. For quarterly data, we find that the weighted median is less volatile than standard core, more closely related to economic slack, and more closely related to headline inflation over the next year. The weighted median also has a drawback: in most countries, it has a lower average level than headline inflation. We therefore also consider a measure of core inflation that eliminates this bias, which is based on the percentile of sectoral inflation rates that matches the sample average of headline CPI inflation.

Significant Policies Have the Federal Reserve Enacted to Control Inflation from the Pandemic

Since April 2021, headline and core inflation figures in the United States have been above target. The factors behind the price spikes are difficult to clarify and have been the subject of debate among policymakers and academics. Lowering interest rates makes borrowing cheaper, which potentially stimulates demand and causes inflation. Conversely, raising interest rates can decrease demand and mitigate inflation. Tight monetary policy helps curb inflation. The Federal Reserve has increased interest rates 11 times since March 2022, bringing the federal funds rate to its highest level since 2001. As interest rates rise, so do borrowing costs. Higher interest rates not only increase borrowing costs but also make saving more attractive, as individuals can earn higher returns on their savings. This change in behavior can result in less investment and consumer expenditure, further dampening economic growth. Therefore, Federal reserve need to make corresponding and correct responses to better deal with inflation. This paper will mainly discuss what economic policies the Federal Reserve has adopted in response to the inflation caused by the epidemic and will actually look at whether or not these monetary policies have actually curbed inflation.

The Symmetric and Asymmetric Nexus Between Monetary Policy Interest Rate and Core Inflation in Jordan

Objectives: This study investigates monetary policy's symmetric and asymmetric impacts on core inflation in Jordan. It employs linear and nonlinear autoregressive distributed lag (NARDL) models. The research also seeks to assess the relationship between the overnight deposit rate, money supply growth, and real GDP growth on core inflation and to explore potential linear or nonlinear effects. Methods: The study applies ARDL and NARDL models to estimate the relationships above. It utilizes data from the first quarter of 1998 to the first quarter of 2023. Tests for Granger causality are conducted, and the Brock-Dechert-Scheinkman (BDS) test is employed to ascertain the presence of nonlinear paths within the study variable. Robustness checks are conducted to ensure parameter stability. Results: The analysis provides compelling empirical evidence supporting a long-term relationship between the overnight deposit rate and core inflation. The Brock-Dechert-Scheinkman (BDS) test confirms the existence of nonlinear dynamics. The ARDL model reveals a linear relationship: a 1% interest rate increase leads to a 0.26% reduction in core inflation in the long run. In contrast, the Nonlinear Autoregressive Distributed Lag (NARDL) analysis unveils an asymmetric effect: a 1% increase in the overnight deposit rate decreases core inflation by 0.43%, while a 1% reduction increases it by 0.37%. Conclusions: These findings have critical implications for monetary policymakers. They underscore the importance of judiciously considering interest rate adjustments to manage inflation dynamics, acknowledging the nonlinear and asymmetric effects inherent in the relationship between the overnight deposit rate and core inflation. When crafting monetary policy strategies, policymakers must consider these nuanced dynamics to address inflation concerns effectively.

Relative Price Shocks and Core inflation in Nigeria: Implication of Second-Round Effects for Monetary Policy

Relative Price Shocks and Core inflation in Nigeria: Implication of Second-Round Effects for Monetary Policy