This paper undertakes both a narrow and wide replication of the estimation of a money demand function conducted by Ireland (American Economic Review, 2009). Using US data from 1980 to 2013, we show that the substantial increase in the money-income ratio during the period of near-zero interest rates is captured well by the log-log specification but not by the semi-log specification, contrary to the result obtained by Ireland (2009). Our estimate of the interest elasticity of money demand over the 1980-2013 period is about one-tenth that of Lucas (2000), who used a log-log specification. Finally, neither specification satisfactorily fits post-2015 US data.
In regression analyses of money demand functions, there is no consensus on whether the nominal interest rate as an independent variable should be used in linear or log form. For example, Meltzer (1963), Hoffman and Rasche (1991), and Lucas (2000) employ a log-log specification (i.e., regressing real money balances (or real money balances relative to nominal GDP) in log on nominal interest rates in log), while Cagan (1956), Lucas (1988), Stock and Watson (1993), and Ball (2001) employ a semi-log specification (i.e., nominal interest rates are not in log).
This paper extends the recent approaches to estimate trend inflation from the survey responses of individual forecasters. It relies on a noisy information model to estimate the trend inflation of individual forecasters. Applying the model to the recent Japanese data, it reveals that the added noise term plays a crucial role and there exists considerable heterogeneity among individual trend inflation forecasts that drives the dynamics of the mean trend inflation forecasts. Divergences in forecasts as well as moves in estimates of trend inflation are largely driven by a identifiable group of forecasters who see less noise in the inflationary process, expect the impact of transitory inflationary shocks to wane more quickly, and are more flexible in adjusting their forecasts of trend inflation in response to new information.
There is no doubt that trend inflation, embedded in actual data of consumer prices and in inflation expectations of various economic players, is one of the most important variables for the conduct of monetary policy. For this reason, huge effort has been made by a number of researchers to extract trend inflation. In this paper, we try to contribute to this literature by extending the existing studies in the following two ways. First, we incorporate a noisy information model more explicitly in an unobserved components model. An unobserved components model such as Beveridge and Nelson (1981) is a useful tool to decompose actual data into its trend and transitory components. Stock and Watson (2007, 2016) apply the procedure to estimate trend inflation by incorporating stochastic volatility in the model. Kozicki and Tinsley (2012) use an unobserved components model to analyze inflation forecasts. Other research papers, many of them more recent, have extracted trend inflation from actual and forecast inflation rates (Chan et al. (2018), Nason and Smith (2021), Patton and Timmermann (2010) and Yoneyama (2021)).
There is a large heterogeneity in health and macroeconomic outcomes across countries during the COVID-19 pandemic. We present a novel framework to understand the source of this heterogeneity, combining an estimated macro-epidemiological model and the idea of revealed preference. Our framework allows us to decompose the difference in health and macroeconomic outcomes across countries into two components: preference and constraint. We find that there is a large heterogeneity in both components across countries and that some countries such as Japan or Australia are willing to accept a large output loss to reduce the number of COVID-19 deaths.
The COVID-19 pandemic has posed the world a question that has not been asked for many decades: How should a society balance infection control and economic activity during a pandemic? Different countries have struggled with this question differently, and we have witnessed a diverse set of health and macroeconomic outcomes across countries during the COVID-19 pandemic. As shown in Figure 1, there are countries that have seen large output loss and many deaths, while there are countries that have seen small output loss and few deaths. There are countries with large output loss and few deaths, yet there are countries with small output loss and many deaths.
Using credit card transaction data, we examine the impacts of two successive events that promoted cashless payments in Japan: the government’s program and the COVID19 pandemic. We find that the number of card users was 9-12 percent higher in restaurants that participated in the program than those that did not. We present a simple framework accounting for the spread of cashless payments. Our model predicts that the impact of the policy intervention diminished as the use of cashless payments increased, which accords well with Japan’s COVID-19 experience. The estimated impact of COVID-19 was around two-thirds of that of the program.
The share of payments using cashless methods is much lower in Japan than many other countries. BIS statistics, for example, show that total payments via cashless means such as credit cards, debit cards, and e-money in Japan amounted to 74 trillion yen or 24 percent of household final consumption expenditure in 2018. This percentage is considerably lower than the 40 percent or more in other developed countries such as the United States, the United Kingdom, and Singapore. The social cost of relying on cash payments is substantial. For instance, using data for several European countries, Schmiedel et al. (2012) show that the unit cost of cash payments is higher than that of debit card payments. In addition, Rogoff (2015) argues that cash makes transactions anonymous, which potentially facilitates underground or illegal activities and leads to law-enforcement costs.