The Stabilizing Effects of Publishing Strategic Central Bank Projections

Expectations are among the main driving forces for economic dynamics. Therefore, managing expectations has become a primary objective for monetary policy seeking to stabilize the business cycle. In this paper, we study whether central banks can manage private-sector expectations by means of publishing one-period ahead inflation projections in a New Keynesian learning-to-forecast experiment. Subjects in the experiment observe these projections along with the historic development of the economy and subsequently submit their own one-period ahead inflation forecasts. In this context, we find that the central bank can significantly manage private-sector expectations and that this management strongly supports monetary policy in stabilizing the economy. Moreover, published central bank inflation projections drastically reduce the probability of a deflationary spiral after strong negative shocks to the economy.

Budget 2022

This Article outlines and assesses changes to the tax and welfare system announced as part of Budget 2022. It first looks at the main taxation measures announced before turning to employment, education and social welfare supports. It then considers the effect of the package of measures as a whole on the incomes of households using representative survey data from the Survey of Incomes and Living Conditions run on SWITCH – the ESRI’s tax and benefit microsimulation model – and ITSim – an indirect tax microsimulation model developed jointly by the ESRI and the Department of Finance. The Article concludes with some brief reflections on inflation forecasts and the policy-making process.

Central Bank Communications and Professional Forecasts: Evidence From India

We analyze the influence of qualitative and quantitative communications of the Reserve Bank of India (RBI) on inflation expectations of professional forecasters and draw out implications for policy. Estimating Carroll-type epidemiological models of expectation formation under information rigidities, we get a large speed of adjustment of professional forecasters’ expectations. Analysis of the determinants of inflation forecasts, inflation surprises, and forecaster disagreement reveals significant influence of quantitative RBI communications in the form of inflation projections. This effect is prominent for shorter-horizon forecasts and after adoption of flexible inflation targeting. Macroeconomic fundamentals like lagged inflation and repo rate also significantly influence inflation forecasts. Choice of words in the RBI monetary policy statements has more impact after October 2016, when the monetary policy committee became the decision-making body. JEL Classification: E31, E52, E58

Market Participants Neither Commit Predictable Errors nor Conform to REH: Evidence from Survey Data of Inflation Forecasts

We develop a novel characterization of participants’ forecasts with a mixture of normal variables arising from a Markov component. Using this characterization, we formulate five behavioral specifications, including four implied by the diagnostic expectations approach, as well as three implied by REH, and derive several new predictions for Coibion and Gorodnichenko.s regression of forecast errors on forecast revisions. Predictions of all eight specifications are inconsistent with the observed instability of individual CG regressions’ coefficients, based on inflation forecasts from 24 professionals. Our findings suggest how to build on key insights of the REH and behavioral approaches in specifying individuals’ forecasts.

German forecasters’ narratives: How informative are German business cycle forecast reports?

Based on German business cycle forecast reports covering 10 German institutions for the period 1993–2017, the paper analyses the information content of German forecasters’ narratives for German business cycle forecasts. The paper applies textual analysis to convert qualitative text data into quantitative sentiment indices. First, a sentiment analysis utilizes dictionary methods and text regression methods, using recursive estimation. Next, the paper analyses the different characteristics of sentiments. In a third step, sentiment indices are used to test the efficiency of numerical forecasts. Using 12-month-ahead fixed horizon forecasts, fixed-effects panel regression results suggest some informational content of sentiment indices for growth and inflation forecasts. Finally, a forecasting exercise analyses the predictive power of sentiment indices for GDP growth and inflation. The results suggest weak evidence, at best, for in-sample and out-of-sample predictive power of the sentiment indices.

Evaluation of the Inflation Forecasting Process of the Reserve Bank of India: A Text Analysis Approach

This study attempts to evaluate the monetary policy of the Reserve Bank of India (RBI) based on an investigation of the policy statements. The analysis based on text mining of the central bank’s monetary policy statements seeks to unravel the information considered by the central bank and the processes followed in making its inflation forecasts. The findings indicate that although the RBI examined high-frequency economic indicators, its inflation forecasts have generally been off the mark. Specifically, the monetary policy committee failed to foresee the sharp disinflation that followed the demonetization announced on November 8, 2016. This failure resulted in a high real interest rate regime that dealt a blow to the economy staggering under the effects of demonetization. Our research findings show that the monetary policy governance practices need to be refined and better aligned to economic realities, particularly under the RBI’s new monetary policy framework.

Forecasting Inflation in Latin American Countries Using a SARIMA-LSTM Combination

Inflation forecasting has been and continues to be an important issue for the world's economies. Governments, through their central banks, watch closely inflation indicators to make national decisions and policies. Controlling growth and contraction requires governments to keep a close eye on the rate of inflation. When planning strategic national investments, governments attempt to forecast inflation over longer periods of time. Getting the inflation forecast wrong, can result in significant economic hardships. However, even given its significance, there is limited new research that applies updated methodologies to forecast it, and even fewer studies in emerging economies where inflation may be drastically higher. This study proposes to forecast the inflation rate in emerging economies based on the commonly used Seasonal Autoregressive Integrated Moving Average (SARIMA) approach combined with Long Short Term Memory (LSTM). The results indicate that the proposed model based on the combination of SARIMA and LSTM, have a higher accuracy in inflation forecasts as measured by the Mean Square Error (MSE) of the proposed models over the SARIMA model and LSTM alone. The loss function used is Mean Squared Error (MSE), and the Model Confidence Set (MCS) is used to test the superiority of the models in the economies of Mexico, Colombia and Peru.

Communicating Monetary Policy Rules

Despite the ubiquity of inflation targeting, central banks communicate their frameworks in a variety of ways. No central bank explicitly expresses their conduct via a policy rule, which contrasts with models of policy. Central banks often connect theory with their practice by publishing inflation forecasts that can, in principle, implicitly convey their reaction function. We return to this central idea to show how a central bank can achieve the gains of a rule-based policy without publicly stating a specific rule. The approach requires central banks to specify an inflation target, inflation tolerance bands, and provide economic projections. When inflation moves outside the band, inflation forecasts provide a time frame over which inflation will return to within the band. We show how this communication replicates and provides the same information as a rule-based policy. In addition, the communication strategy produces a natural benchmark for assessing central bank performance.

Transmission mechanism of monetary policy and response to outside shocks in 2020 by National Bank of the Republic of Kazakhstan

The article examines the features and current state of monetary policy and transmission mechanism of the National Bank of Kazakhstan. The transmission mechanism is not a tool to achieve the goals set for the development of the modern economy. This mechanism is a set of interactions in economic processes through which the results of decisions or transactions in the framework of monetary policy affect the economy. In this regard, the article describes the level of inflation and its impact on the level of prices for goods and services. The situation with the use of the consumer price index to calculate inflation, calculated on the basis of the analysis of prices in the portfolio of consumer goods and services of the Ministry of National Economy of the Republic of Kazakhstan, is analyzed in detail. Macroeconomic and microeconomic factors, reflecting the viable direction of the monetary policy of the Republic of Kazakhstan, affect the level of the consumer price index. To make decisions, the NBRK conducts research to determine inflation forecasts. Inflationary expectations make it possible to assess the possible impact on the economy. In this context, the article states that the NBRK's inflation target will be achieved through the following channels (base rate, exchange rate and money supply). The conclusion is based on the fact that, given the global pandemic around the world, one can see the NBRK's expansionary monetary policy and decisions to lower the base rate, increase the money supply and stabilize the currency.