Regime changes and monetary stagflation

This paper examines whether monetary shocks can consistently generate stagflation in a dynamic, stochastic setting. I assume that the monetary authority can induce transitory shocks and longer-lasting monetary regime changes in its operating instrument. Firms cannot distinguish between these shocks and must learn about them using a signal extraction problem. The possibility of changes in the monetary regime greatly improves the ability of money to generate stagflation. This is true whether the regime actually changes or not. If the monetary regime changes on average once every ten years, stagflation occurs in 76% of model simulations. The intuition for this result is simple: increased output volatility due to learning coupled with inflation inertia produce conditions conducive to the emergence of stagflation. The incidence of stagflation can be reduced by a stable, transparent central bank.Inflation (Finance) ; Recessions ; Monetary policy

Convenient prices, currency, and nominal rigidity : theory with evidence from newspaper prices

Newspapers, movie tickets, and concession stand items typically charge prices that facilitate rapid, simple transactions: their prices often coincide with available monetary units, require few pieces of money, or require little change. In this sense, these prices are more convenient than other proximate prices. I model a firm that explicitly incorporates convenience into its pricing decisions, where convenience is quantified by the number of currency units in a transaction. The model illustrates how alternating periods of price rigidity and flexibility can arise in such a setting, along with rapid switching between convenient prices. I compile time series data on newspaper cover prices and use simulations to show that convenience is an essential component of these prices. In the empirical data, firms set prices that were more convenient than adjacent prices 61% of the time. Standard menu costs cannot replicate this behavior. Because convenience appears to affect many of the consumer goods and services with the stickiest prices in the U.S. economy, studies focusing on very sticky prices must be cognizant of convenience’s role in effecting above-average price rigidity.Prices ; Newspapers

How will unemployment fare following the recession?

Since the start of the recession in December 2007, the U.S. unemployment rate has risen more than four percentage points. Similar sharp increases in unemployment have occurred in other severe recessions, such as those in 1973-75 and 1981-82. In the aftermath of those severe recessions, the economy rapidly recovered and unemployment quickly declined. ; Will unemployment behave similarly following this recession? One reason why unemployment may not fall as quickly this time is that the labor market has changed substantively since the early 1980s. In the two recoveries since then, not only did unemployment continue to climb, but it remained persistently high in what have been termed "jobless recoveries." To the extent that labor market changes were responsible for these jobless recoveries, unemployment following the current recession may also be slow to recover. ; A second reason unemployment may not fall quickly this time is that the recession has been coupled with a systemic banking crisis. While the United States has not had many instances of similar crises in the past, evidence from the experiences of other countries may shed light on how future unemployment in the United States is likely to behave. In general, the international data reveal large and persistent increases in unemployment in the aftermath of such events. ; Knotek and Terry examine these factors and quantify their potential implications for the future U.S. unemployment rate. Their analysis suggests that recent trends in labor markets, combined with the presence of a banking crisis in the current recession, raise the likelihood that unemployment will recover much more slowly from this recession than past episodes of severe recession may suggest. Moreover, such a slow recovery has the potential to raise important questions for policymakers, including the level of unemployment consistent with their goals.

How do households respond to uncertainty shocks?

Economic disruptions generally coincide with heightened uncertainty. In the United States, uncertainty increased sharply with the recent housing market crash, financial crisis, deep recession, and uneven recovery. In July 2010 Congressional testimony, Federal Reserve Chairman Bernanke described conditions as "unusually uncertain." The uncertain landscape was also cited as a factor in the slow recovery from the 2001 recession, when the March 2003 Federal Open Market Committee statement highlighted the "unusually large uncertainties" at the time. ; Uncertainty is a standard feature of most macroeconomic models, in which consumers and firms make decisions today based on expectations of an unknown (and hence uncertain) future. But in light of real-world events, economists have begun to think more critically about the role of uncertainty in the economy. Recent research has allowed the degree of uncertainty to vary over time and examined how these fluctuations affect business activity. The results have been mixed thus far, with some authors finding that fluctuations in uncertainty are a key factor in the business cycle, while others have found little such evidence. ; Knotek and Khan take a similar approach in studying levels of uncertainty that can vary over time, but they focus on household responses to changes in uncertainty. Because uncertainty can take many forms, they consider two measures of uncertainty, one based on references to uncertainty in newspaper articles and another derived from the stock market. ; While economic theory predicts sudden, sharp pullbacks of household purchases following increases in uncertainty, the empirical results suggest that household spending reductions are modest and may only appear after a considerable time has passed. In addition, movements in uncertainty account for only a small portion of the total fluctuations in household spending. These results suggest that variations in the amount of uncertainty--at least as they are commonly captured--do not appear to be a key factor driving household spending decisions and, in turn, economic weakness.

Markov-chain approximations of vector autoregressions: application of general multivariate-normal integration techniques

Discrete Markov chains can be useful to approximate vector autoregressive processes for economists doing computational work. One such approximation method first presented by Tauchen (1986) operates under the general theoretical assumption of a transformed VAR with diagonal covariance structure for the process error term. We demonstrate one simple method of more conveniently treating this approximation problem in practice using readily available multivariate-normal integration techniques to allow for arbitrary positive-semidefinite covariance structures. Examples are provided using processes with non-diagonal and singular non-diagonal error covariances.

Alternative methods of solving state-dependent pricing models

We use simulation-based techniques to compare and contrast two methods for solving state-dependent pricing models: discretization, which solves and simulates the model on a grid; and collocation, which relies on Chebyshev polynomials. While both methods produce qualitatively similar results, statistically significant quantitative differences do arise. We present evidence favoring discretization over collocation in this context, given a lack of robustness in the latter.

A tale of two rigidities: sticky prices in a sticky-information environment

Macroeconomic models with microeconomic foundations face a difficult task: they must be consistent with facts both large and small. This paper proposes a model that combines two strands of the literature on stickiness in order to match both sets of facts. (1) Firms acquire information infrequently, as in Mankiw and Reis (2002), resulting in sticky information. (2) Firms face heterogeneous, fixed menu costs which they must pay to change prices, leading to state-dependent sticky prices at the micro level. I estimate key structural parameters and show that a model of sticky prices in a sticky-information environment is consistent with both micro and macro evidencePrices

Convenient prices and price rigidity: cross-sectional evidence

This paper provides cross-sectional evidence of convenient prices--prices that simplify and expedite transactions and thereby reduce the time costs from physically making a transaction. I propose that firms may wish to set convenient prices for items that: (1) are typically purchased with cash; (2) are sold alone or with a few similar items; and (3) are high-traffic transactions, i.e., require queuing or are purchased very frequently. I find broad support for the use of convenient prices in locations where making a rapid transaction is important. Convenience also appears to predominantly affect goods and services with above-average price rigidity.

Asymmetric responses of consumer spending to energy prices : a threshold VAR approach

We document asymmetric responses of consumer spending to energy price shocks: using a threshold vector autoregressive model estimated with Bayesian methods on U.S. data with high and low real energy inflation regimes, positive energy price shocks have a greater negative effect on consumption compared with the increase in consumption that arises from negative energy price shocks. For large shocks, the cumulative consumption responses are three to five times greater for positive than negative shocks. In disaggregated spending data, the asymmetric responses are strongest for durable goods consumption, but asymmetries are also present in the responses of nondurables and services consumption

Real-Time Density Nowcasts of U.S. Inflation : a Model-Combination Approach

We develop a flexible modeling framework to produce density nowcasts for U.S. inflation at a trading-day frequency. Our framework: (1) combines individual density nowcasts from three classes of parsimonious mixed-frequency models; (2) adopts a novel flexible treatment in the use of the aggregation function; and (3) permits dynamic model averaging via the use of weights that are updated based on learning from past performance. Together these features provide density nowcasts that can accommodate non-Gaussian properties. We document the competitive properties of the nowcasts generated from our framework using high-frequency real-time data over the period 2000-2015