Have the content, sentiment, and timing of the Federal Reserve (Fed) communications changed across communication types during the COVID-19 pandemic? Did similar changes occur during the global financial and dot-com crises? We compile dictionaries specific to COVID-19 and unconventional monetary policy (UMP) and utilize sentiment analysis and topic modeling to study the Fed’s communications and answer the above questions. We show that the Fed’s communications regarding the COVID-19 pandemic concern matters of financial volatility, contextual uncertainty, and financial stability, and that they emphasize health, social welfare, and UMP. We also show that the Fed’s communication policy changes drastically during the COVID-19 pandemic compared to the GFC and dot-com crisis in terms of content, sentiment, and timing. Specifically, we find that during the past two decades, a decrease in the financial stability sentiment conveyed by the Fed’s interest rate announcements and minutes precedes a decrease in the Fed’s interest rate.

This paper employs text mining analysis to measure the comprehensibility and information quality of the interest rate announcements published by the Bank of Israel over the past two decades. We examine these texts for ease of comprehension and the sentiment conveyed to the public and benchmark them against comparable texts published by the Fed and the ECB. The findings reveal that readers require fewer years of education to comprehend the Bank of Israel interest rate announcements than they do to understand the interest rate announcements published by the Fed and the ECB. In addition, we show that the sentiment within these announcements is aligned with economic fluctuations and that there is a direct correlation between the uncertainty the communications reflect and the volatility of the domestic market.

We develop a behavioral New Keynesian model to analyze optimal monetary policy with heterogeneously myopic households and firms. Five key results are derived. First, our model reflects coherent microeconomic and aggregate myopia due to the consistent transition from subjective to objective expectations. Second, the optimal monetary policy entails implementing inflation targeting in a framework where myopia distorts agents' inflation expectations. Third, price level targeting emerges as the optimal policy under output gap, revenue, or interest rate myopia. Given that bygones are not bygones under price level targeting, rational inflation expectations are a minimal condition for optimality under bounded rationality. Fourth, we show that there are no feasible instrument rules for implementing the optimal monetary policy, casting doubt on the ability of simple Taylor rules to assist in the setting of monetary policy when agents are myopic. Finally, bounded rationality is not necessarily welfare decreasing, and is even associated with welfare gains for extreme cognitive discounting.

 

We present a hierarchical architecture based on Recurrent Neural Networks (RNNs) for predicting disaggregated inflation components of the Consumer Price Index (CPI). While the majority of existing research is focused on predicting headline inflation, many economic and financial institutions are interested in its partial disaggregated components. To this end, we developed the novel Hierarchical Recurrent Neural Network (HRNN) model, which utilizes information from higher levels in the CPI hierarchy to improve predictions at the more volatile lower levels. Based on a large dataset from the US CPI-U index, our evaluations indicate that the HRNN model significantly outperforms a vast array of well-known inflation prediction baselines. Our methodology and results provide additional forecasting measures and possibilities to policy and market makers on sectoral and component-specific prices.

Significant shifts in the composition of consumer spending as a result of the COVID-19 crisis can complicate the interpretation of official inflation data, which are calculated by the Central Bureau of Statistics (CBS) based on a fixed basket of goods. We focus on Israel as a country that experienced three lockdowns, additional restrictions that significantly changed consumer behavior, and a successful vaccination campaign that has led to the lifting of most of these restrictions. We use credit card spending data to construct a consumption basket of goods representing the composition of household consumption during the COVID-19 period. We use this synthetic COVID-19 basket to calculate the adjusted inflation rate that should prevail during the pandemic period. We find that the differences between COVID-19-adjusted and CBS (unadjusted) inflation measures are transitory. Only the contribution of certain goods and services, particularly housing and transportation, to inflation changed significantly, especially during the first and second lockdowns. Although lockdowns and restrictions in developed countries created a significant bias in inflation weighting, the inflation bias remained unexpectedly small and transitory during the COVID-19 period in Israel.

Each person’s characteristics may influence that person’s behaviors and outcomes. This study builds and uses a new database to estimate experts’ performance and boldness based on their experience and characteristics. Our study classifies experts providing inflation forecasts based on their education, experience, gender, and environment. We provide alternative interpretations of factors affecting experts’ inflation forecasting performance, boldness, and pessimism by linking behavioral economics, the economics of education, and forecasting literature. The study finds that an expert with previous experience at a central bank appears to have a lower propensity for predicting deflation.

The global financial crisis (GFC) triggered the use of macroprudential policies imposed on the banking sector. Using bank-level panel data for Israel for the period 2004–2019, we find that domestic macroprudential measures changed the composition of bank credit growth but did not affect the total credit growth rate. Specifically, we show that macroprudential measures targeted at the housing sector moderated housing credit growth but tended to increase business credit growth. We also find that accommodative monetary policy surprises tended to increase bank credit growth before the GFC. We show that accommodative monetary policy surprises increased consumer credit when interacting with macroprudential policies targeting the housing market. Accommodative monetary policy interacted with nonhousing macroprudential measures to increase total credit.

Non-Technical Summary
 

We review several existing text analysis methodologies and explain their formal application processes using the open-source software R and relevant packages. Several text mining applications to analyze central bank texts are presented.

Uncertainty about an economy's regime can change drastically around a crisis. An imported crisis such as the global financial crisis in the euro area highlights the effect of foreign shocks. Estimating an open-economy nonlinear dynamic stochastic general equilibrium model for the euro area and the United States including Markov-switching volatility shocks, we show that these shocks were significant during the global financial crisis compared with periods of calm. We describe how US shocks from both the real economy and financial markets affected the euro area economy and how bond reallocation occurred between short- and long-term maturities during the global financial crisis. Importantly, the estimated nonlinearities when domestic and foreign financial markets influence the economy, should not be neglected. The nonlinear behavior of market-related variables highlights the importance of higher-order estimation for providing additional interpretations to policymakers.

Governments, central banks, and private companies make extensive use of expert and market-based forecasts in their decision-making processes. These forecasts can be affected by terrorism, a factor that should be considered by decision-makers. We focus on terrorism as a mostly endogenously driven form of political uncertainty and assess the forecasting performance of market-based and professional inflation and exchange rate forecasts in Israel. We show that expert forecasts are better than market-based forecasts, particularly during periods of terrorism. However, the performance of both market-based and expert forecasts is significantly worse during such periods. Thus, policymakers should be particularly attentive to terrorism when considering inflation and exchange rate forecasts.

This paper presents an analysis of the stimulants and consequences of money demand dynamics. By assuming that household's money holdings and consumption preferences are not separable, we demonstrate that the interest-elasticity of demand for money is a function of the household's preference to hold real balances, the extent to which these preferences are not separable in consumption and real balances, and trend inflation. An empirical study of U.S. data revealed that there was a gradual fall in the interest elasticity of money demand of approximately one-third during the 1970s due to high trend inflation. A further decline in the interest-elasticity of the demand for money was observed in the 1980s due to the changing household preferences that emerged in response to financial innovation. These developments led to a reduction in the welfare cost of inflation that subsequently explains the rise in monetary neutrality observed in the data.

Central banks' monetary policy rules being consistent with policy objectives are a fundamental of applied monetary economics. We seek to determine, first, which of the central bank's rules are most in line with the historical data for the US economy and, second, what policy rule would work best to assist the central bank in reaching its objectives via several loss function measures. We use Bayesian estimations to evaluate twelve monetary policy rules from 1955 to 2017 and over three different sub-periods. We find that when considering the central bank's loss functions, the estimates often indicate the superiority of NGDP level targeting rules, though Taylor-type rules lead to nearly identical implications. However, the results suggest that various central bank empirical rules, be they NGDP or Taylor type, are more appropriate to achieve the central bank's objectives for each type of period (stable, crisis, recovery).

This paper analyzes the role of money and monetary policy as well as the forecasting performance of New Keynesian dynamic stochastic general equilibrium models with and without separability between consumption and money. The study is conducted over three crisis periods in the Eurozone, namely, the ERM crisis, the dot-com crisis, and the global financial crisis (GFC). The results of successive Bayesian estimations demonstrate that during these crises, the nonseparable model generally provides better out-of-sample output forecasts than the baseline model. We also demonstrate that money shocks have some impact on output variations during crises, especially in the case of the GFC. Furthermore, the response of output to a money shock is more persistent during the GFC than during the other crises. The impact of monetary policy also changes during crises. Insofar as the GFC is concerned, this impact increases at the beginning of the crisis, but decreases sharply thereafter.

This study examines how money and monetary policy have influenced output and inflation during the past decade in Israel by comparing two New Keynesian DSGE models. One is a baseline separable model (Gali, 2008) and the other assumes non-separable household preferences between consumption and money (Benchimol & Fourçans, 2012). We test both models by using rolling window Bayesian estimations over the last decade (2001–2013). The results of the presented dynamic analysis show that the sensitivity of output with respect to money shocks increased during the Dot-com, Intifada, and Subprime crises. The role of monetary policy increased during these crises, especially with regard to inflation, even though the effectiveness of conventional monetary policy decreased during the Subprime crisis. In addition, the non-separable model including money provides lower forecast errors than the baseline separable model without money, while the influence of money on output fluctuations can be seen as a good predictive indicator of bank and debt risks. By impacting and monitoring households’ money holdings, policy makers could improve their forecasts and crisis management through models considering monetary aggregates.

This article checks whether money is an omitted variable in the production process by proposing a microfounded New Keynesian Dynamic Stochastic General Equilibrium model. In this framework, real money balances enter the production function, and money demanded by households is differentiated from that demanded by firms. Using a Bayesian analysis, our model weakens the hypothesis that money is a factor of production. However, the demand of money by firms appears to have a significant impact on the economy, even if this demand has a low weight in the production process.

We propose a New Keynesian Dynamic Stochastic General Equilibrium (DSGE) model where a risk aversion shock enters a separable utility function. We analyze five periods from 1971 through 2011, each lasting for 20 years, to follow over time the dynamics of several parameters such as the risk aversion parameter; the Taylor rule coefficients; and the role of the risk aversion shock in output, inflation, interest rate, and real money balances in the Eurozone. Our analysis suggests that risk aversion was a more important component of output and real money balance dynamics between 2006 and 2011 than it was between 1971 and 2006, at least in the short run.

We present and test a model of the Eurozone, with a special emphasis on the role of risk aversion and money. The model follows the New Keynesian DSGE framework, money being introduced in the utility function with a non-separability assumption. Money is also introduced in the Taylor rule. By using Bayesian estimation techniques, we shed light on the determinants of output, inflation, money, interest rate, flexible-price output, and flexible-price real money balance dynamics. The role of money is investigated further. Its impact on output depends on the degree of risk aversion. Money plays a minor role in the estimated model. Yet, a higher level of risk aversion would imply that money had significant quantitative effects on business cycle fluctuations.