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Business confidence is crucial to firm decisions, but it is deeply related to professional forecasters' expectations. Since Brazil is an important inflation targeting country, this paper investigates whether monetary policy credibility and disagreements in inflation and interest rate expectations relate to business confidence in Brazil. The study considers the aggregate business confidence index and the business confidence indexes for 11 industrial sectors in Brazil.

The authors run ordinary least squares and generalized method of moments regressions to assess the direct effects of disagreements in expectation and monetary policy credibility on business confidence. The authors also make use of Wald test of parameter equality to observe whether there are “offsetting effects” of monetary credibility in mitigating the effects of both disagreements in expectations on business confidence. Besides, the authors run quantile regressions to analyze the effect of the main explanatory variables of interest on business confidence in contexts where business confidence is low (pessimistic) or high (optimistic).

Disagreements in inflation expectations reduce business confidence, monetary policy credibility improves business confidence and credibility mitigates the adverse effects of disagreements in expectations on business confidence. The sectors most sensitive to monetary policy credibility are Rubber, Motor Vehicles, Metallurgy, Metal Products and Cellulose. The findings also suggest the effect of disagreement in inflation expectations on business confidence decreases as confidence increases, and the effect of monetary policy credibility on business confidence increases as entrepreneurs are more optimistic.

While there is evidence that monetary policy credibility is beneficial to the economy, there are no studies on the effects of disagreements in inflation and interest rate expectations on business confidence (at the aggregate and sectoral levels). Besides, there are no studies that have investigated whether monetary policy credibility can mitigate the effects of disagreements in inflation and interest rate expectations on business confidence (at the aggregate and sectoral levels). Therefore, there are gaps to be filled in the literature addressing business confidence, monetary policy credibility and disagreements in expectations. These issues are particularly important to inflation targeting developing countries.

The author analyzes households' inflation expectations data for India, collected quarterly by the RBI for more than a decade. The contribution of this paper lies in two folds. First, this study examines the relationship between relatively recent inflation expectations survey of households (IESH) and the actual inflation for India. Secondly, the author employs a structural VAR with the time period 2006 Q2 to 2020 Q2 on inflation expectation survey data of India. A short-term non-recursive restriction is imposed in the model in order to capture the simultaneous co-dependence causal effect of inflation expectation and realized inflation.

This paper studies the dynamic behavior of inflation expectations survey data in two folds. First, the author analyzes the time series property of the survey data. The author begins with testing the stationarity property of the series, followed by the casual relationship between the expected and actual inflation. The author further examines the short-run and long-run behavior of the IESH with actual inflation. Employing autoregressive distributed lag and Johansen co-integration, the author tested if a long-run relationship exists between the variables. In the second approach, the author investigates the determinants of inflation expectations by employing a non-recursive SVAR model.

The preliminary explanatory test reveals that inflation expectation is a policy variable and should be used in monetary policy as an instrument variable. The model identifies the price puzzle for India. The author finds that the response of inflation to a monetary policy shock is neutral. The results also indicate that the expectations of the general public are self-fulfilling.

IESH has only commenced from September 2005, hence is relatively new as compared to other survey in developed countries. Being a new data set so far, the author could not locate any study devoted in analyzing the behavior of the data with other macroeconomic variables.

From the standpoint of a policy maker who has access to a number of expert forecasts, the uncertainty of a combined or ensemble forecast should be interpreted as that of a typical forecaster randomly drawn from the pool. This uncertainty formula should incorporate forecaster discord, as justified by (i) disagreement as a component of combined forecast uncertainty, (ii) the model averaging literature, and (iii) central banks’ communication of uncertainty via fan charts. Using new statistics to test for the homogeneity of idiosyncratic errors under the joint limits with both T and n approaching infinity simultaneously, the authors find that some previously used measures can significantly underestimate the conceptually correct benchmark forecast uncertainty.

The primary aim of revenue management (RM) is to sell the right product to the right customer at the right time for the right price. Ever since the deregulation of US airline industry, and the emergence of the internet as a distribution channel, RM has come of age. The purpose of this paper is to map out ten turning points in the evolution of Revenue Management taking an historical perspective.

The paper is a chronological account based upon published research and literature fundamentally drawn from the Journal of Revenue and Pricing Management.

The significance and success to RM is attributed to the following turning points: Littlewood’s rule, Expected Marginal Seat Revenue, deregulation of the US air industry, single leg to origin and destination RM, the use of family fares, technological advancement, low-cost carriers, dynamic pricing, consumer and price transparency and pricing capabilities in organizations.

The originality of the paper lies in identifying the core trends or turning points that have shaped the development of RM thus assisting futurists or forecasters to shape the future.

The 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.

The 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.

Based on raw online data, reliable estimates of monthly and annual food inflation were provided about 30 days before final official indexes were published.

This 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.