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.
We examine the impact of domestic macroprudential (MaP) policy measures targeted at the banking sector, alongside the impact of domestic monetary policy on housing, consumer, and business bank credit dynamics, using individual bank panel data for the period 2004–19. We find that domestic MaP measures targeting housing sector credit reduced the growth rate of housing credit and contributed to business credit growth. Other general MaP measures reduced growth of credit to the business sector. Monetary policy was generally found to be effective, with a significant negative impact on bank credit before the Global Financial Crisis (GFC). The interaction between monetary policy and MaP highlights the role of monetary policy after 2008, and the effect of accommodative monetary policy on consumer and business credit fostered by housing MaP measures. We found that the impact of foreign monetary policy on credit growth is negative, as is the impact of domestic monetary policy, suggesting its capacity to function as a leading indicator for domestic monetary policy.
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 build a behavioral New Keynesian model that emphasizes different forms of myopia for households and firms. By examining the optimal monetary policy within this model, we find four main results. First, in a framework where myopia distorts agents' inflation expectations, the optimal monetary policy entails implementing inflation targeting. Second, 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 in a behavioral world. Third, 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. Fourth, bounded rationality may be associated with welfare gains.
We review several existing methodologies in text analysis and explain formal processes of text analysis using the open-source software R and relevant packages. We present some technical applications of text mining methodologies comprehensively to economists.
Each person's characteristics may influence that person's behaviors and their outcomes. We build and use a new database to estimate experts' performance and boldness based on their experience and characteristics. We classify 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. An expert with previous experience at a central bank appears to have a lower propensity for predicting deflation.
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.