A Model of the Consumption Response to Fiscal Stimulus Payments

August 31, 2011

By Greg Kaplan and Giovanni Violante

http://d.repec.org/n?u=RePEc:nbr:nberwo:17338&r=dge

A wide body of empirical evidence, based on randomized experiments, finds that 20-40 percent of fiscal stimulus payments (e.g. tax rebates) are spent on nondurable household consumption in the quarter that they are received. We develop a structural economic model to interpret this evidence. Our model integrates the classical Baumol-Tobin model of money demand into the workhorse incomplete-markets life-cycle economy. In this framework, households can hold two assets: a low-return liquid asset (e.g., cash, checking account) and a high-return illiquid asset (e.g., housing, retirement account) that carries a transaction cost. The optimal life-cycle pattern of wealth accumulation implies that many households are “wealthy hand-to-mouth” : they hold little or no liquid wealth despite owning sizeable quantities of illiquid assets. They therefore display large propensities to consume out of additional income. We document the existence of such households in data from the Survey of Consumer Finances. A version of the model parameterized to the 2001 tax rebate episode is able to generate consumption responses to fiscal stimulus payments that are in line with the data.

The extant literature exploits the timing of the mailing of the stimulus checks to determine the marginal propensity to consume. But all recipients were expecting those checks to come, so what is measured is really the impact of differences in liquidity. What Kaplan and Violante do is build a realistic model of liquidity management that yields the measured predictions in order to back out from the model what the true propensity to consume is. A very subtle exercise.


Two papers on policy uncertainty and learning

August 27, 2011

This week’s crop had a lot of good papers, so it was difficult to choose a particular one. I chose two related papers, which both in some way address the current fiscal policy uncertainty in the United States.

The first one shows that the policy uncertainty can have a substantial impact, and one could thus consider policy uncertainty as a policy in itself. The most important channel is through expectations on capital income taxes. Reducing uncertainty on other taxes appears less crucial.

The second looks at how agents try to learn about their new environment after a fiscal policy change. The learning process itself induces swings and overshooting, which of course would be reduced with better information or more certainty.

Fiscal Volatility Shocks and Economic Activity

By Jesus Fernandez-Villaverde, Pablo Guerron-Quintana, Keith Kuester and Juan Rubio-Ramirez

http://d.repec.org/n?u=RePEc:pen:papers:11-022&r=dge

We study the effects of changes in uncertainty about future fiscal policy on aggregate economic activity. Fiscal deficits and public debt have risen sharply in the wake of the financial crisis. While these developments make fisscal consolidation inevitable, there is considerable uncertainty about the policy mix and timing of such budgetary adjustment. To evaluate the consequences of this increased uncertainty, we first estimate tax and spending processes for the U.S. that allow for time-varying volatility. We then feed these processes into an otherwise standard New Keynesian business cycle model calibrated to the U.S. economy. We find that fiscal volatility shocks have an adverse effect on economic activity that is comparable to the effects of a 25-basis-point innovation in the federal funds rate.

Policy Change and Learning in the RBC Model

By Kaushik Mitra, George W. Evans and Seppo Honkapohja

http://d.repec.org/n?u=RePEc:san:cdmawp:1111&r=dge

What is the impact of surprise and anticipated policy changes when agents form expectations using adaptive learning rather than rational expectations? We examine this issue using the standard stochastic real business cycle model with lump-sum taxes. Agents combine knowledge about future policy with econometric forecasts of future wages and interest rates. Both permanent and temporary policy changes are analyzed. Dynamics under learning can have large impact effects and a gradual hump-shaped response, and tend to be prominently characterized by oscillations not present under rational expectations. These fluctuations reflect periods of excessive optimism or pessimism, followed by subsequent corrections.


Volatility, Persistence and Nonlinearity of Simulated DSGE Real Exchange Rates

August 20, 2011

By Yamin Ahmad, Ming Chien Lo and Olena Mykhaylova

http://d.repec.org/n?u=RePEc:uww:wpaper:11-01&r=dge

This paper investigates the time series properties of real exchange rates series produced by DSGE models. We simulate a variety of new open economy DSGE models that incorporate features such as local currency pricing, home bias, non-traded goods and incomplete markets. We attempt to ascertain whether the dynamics of the real exchange rate in this class of models are consistent with those found in the time series literature using data from the current floating period. Although none of the basic specifications we consider match the volatility in the raw data, our findings suggest that home bias in consumption and non-traded goods are the key components of DSGE models that are able to generate persistent real exchange rates, comparable to that in the data. Moreover, we find that some of the structural micro-level nonlinearity embedded within DSGE models may be represented as macro-level nonlinearity in the form of a smooth transition autoregressive process, which has previously been found to parsimoniously characterize the dynamics of real exchange rates in the time series literature.

Explaining exchange rates has always been a challenge, even for purely statistical models. It is to be expected that standard fare open economy DSGE model will have difficulties as well, as they assume a world with far less frictions than there actually are. Previous attempts have only tried to introduced one imperfection at a time. This paper tries to look at all in one go and finds that we have still substantial ground to cover, but the paper can suggest some more promising avenues.


Consumer Misperceptions, Uncertain Fundamentals, and the Business Cycle

August 11, 2011

By Patrick Hürtgen

http://d.repec.org/n?u=RePEc:bon:bonedp:bgse10_2011&r=dge

This paper explores the importance of shocks to consumer misperceptions, or “noise shocks”, in a quantitative business cycle model. I embed imperfect information as in Lorenzoni (2009) into a new Keynesian model with price and wage rigidities. Agents learn about the components of labor productivity by only observing aggregate productivity and a noisy signal. Noise shocks lead to expectational errors about the true fundamentals triggering aggregate fluctuations. Estimating the model with Bayesian methods on US data shows that noise shocks contribute to 20 percent of consumption fluctuations at short horizons. Wage rigidity is pivotal for the importance of noise shocks.

We are probably experiencing very large noise shocks these days. Maybe this paper can help us understand what is going on. Too bad there is no capital in the model, which could have given us some hints about the current stock market behavior.


Financial intermediation, investment dynamics and business cycle fluctuations

August 5, 2011

By Andrea Ajello

http://d.repec.org/n?u=RePEc:pra:mprapa:32447&r=dge

How important are financial friction shocks in business cycles fluctuations? To answer this question, I use micro data to quantify key features of US financial markets. I then construct a dynamic equilibrium model that is consistent with these features and fit the model to business cycle data using Bayesian methods. In my micro data analysis, I establish facts that may be of independent interest. For example, I find that a substantial 33% of firm investment is funded using financial markets. The dynamic model introduces price and wage rigidities and a financial intermediation shock into Kiyotaki and Moore (2008). According to the estimated model, the financial intermediation shock explains around 40% of GDP and 55% of investment volatility. The estimation assigns such a large role to the financial shock for two reasons: (i) the shock is closely related to the interest rate spread, and this spread is strongly countercyclical and (ii) according to the model, the response in consumption, investment, employment and asset prices to a financial shock resembles the behavior of these variables over the business cycle.

This is the latest in a new direction for business cycle models where the model itself is used to estimate the shock process, the goal being to figure out with shocks are more important. Contrast this with the traditional literature that tried to see how far one could get with an exogenously calibrated shock or two. And this particular exercise shows that financial frictions are important. Of course, this result could disappear by applying the same data to a different model, but the fact that one needs such shocks to obtain countercyclical interest rate spreads is quite compelling.


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