Limited Asset Market Participation and the Optimal Fiscal and Monetary Policies

November 11, 2014

By Lorenzo Menna and Patrizio Tirelli

In the workhorse DSGE model, the optimal steady state inflation rate is near to zero or slightly negative and inflation is almost completely stabilized along the business cycle (Schmitt-Grohé and Uribe, 2011). We reconsider the issue, allowing for agent heterogeneity in the access to the market for interest bearing assets. We show that inflation reduces inequality and that LAMP can justify relatively high optimal inflation rates. When we calibrate the share of constrained agents to fit the wealth Gini index for the US, the optimal inflation rate is well above 2%. The optimal response to shocks is also affected. Rather than using public debt to smooth tax distortions, the Ramsey planner front loads tax rates and reduces public debt variations in order to limit the redistributive effects of debt service payments.

Intriguing paper that, for once, does not rely on downward nominal price rigidities to justify positive inflation. It also implies that a little dose of debt monetization is to some extent justified. This has to get a few people thinking.

Dynamic Prediction Pools: An Investigation of Financial Frictions and Forecasting Performance

November 5, 2014

By Marco Del Negro, Raiden Hasegawa and Frank Schorfheide

We provide a novel methodology for estimating time-varying weights in linear prediction pools, which we call Dynamic Pools, and use it to investigate the relative forecasting performance of DSGE models with and without financial frictions for output growth and inflation from 1992 to 2011. We find strong evidence of time variation in the pool’s weights, reflecting the fact that the DSGE model with financial frictions produces superior forecasts in periods of financial distress but does not perform as well in tranquil periods. The dynamic pool’s weights react in a timely fashion to changes in the environment, leading to real-time forecast improvements relative to other methods of density forecast combination, such as Bayesian Model Averaging, optimal (static) pools, and equal weights. We show how a policymaker dealing with model uncertainty could have used a dynamic pools to perform a counterfactual exercise (responding to the gap in labor market conditions) in the immediate aftermath of the Lehman crisis.

Much work has be done in recent years to estimate DSGE model for forecasting purposes. Despite the fact that they are designed for these purposes, they turn out to be surprisingly useful. This paper continues in this line of research and shows that in fact DSGE models are most useful when other model are most likely to fail: when things are out of the ordinary. In hindsight this makes absolute sense. This is when you get out of the comfort zone of small fluctuations around a trend and theory can help you determine how agent react to event that are out of the sample. It turns out that instead of ditching DSGE models during the last crisis, as many have advocated, we should have used them even more than usual!

Optimal Life Cycle Unemployment Insurance

October 28, 2014

By Claudio Michelacci and Hernan Ruffo

We argue that US welfare would rise if unemployment insurance were increased for younger and decreased for older workers. This is because the young tend to lack the means to smooth consumption during unemployment and want jobs to accumulate high-return human capital. So unemployment insurance is most valuable to them, while moral hazard is mild. By calibrating a life cycle model with unemployment risk and endogenous search effort, we find that allowing unemployment replacement rates to decline with age yields sizeable welfare gains to US workers.

This paper may seem obvious, but 1) it quantifies that this is economically significant, and 2) did you think about this? Targeting transfers to the population that needs them most is always going to improve things. This paper highlights one such target.

The zero lower bound and parameter bias in an estimated DSGE model

October 25, 2014

By Yasuo Hirose and Atsushi Inoue

This paper examines how and to what extent parameter estimates can be biased in a dynamic stochastic general equilibrium (DSGE) model that omits the zero lower bound (ZLB) constraint on the nominal interest rate. Our Monte Carlo experiments using a standard sticky-price DSGE model show that no significant bias is detected in parameter estimates and that the estimated impulse response functions are quite similar to the true ones. However, as the probability of hitting the ZLB increases, the parameter bias becomes larger and therefore leads to substantial differences between the estimated and true impulse responses. It is also demonstrated that the model missing the ZLB causes biased estimates of structural shocks even with the virtually unbiased parameters.

Beyond the issue that this paper highlights, we should remember that for any estimation that covers the past decade we likely cannot use the standard methods that assume symmetric responses for positive and negative deviations from the mean in the data. I suspect we are going to see a lot of lousy regressions that blindly throw variables into a non-structural estimation and pretend to get reliable results.

The effects of a money-financed fiscal stimulus

October 23, 2014

By Jordi Galí

I analyze the effects of an increase in government purchases financed entirely through seignorage, in both a classical and a New Keynesian framework, and compare them with those resulting from a more conventional debt-financed stimulus. My findings point to the importance of nominal rigidities in shaping those effects. Under a realistic calibration of such rigidities, a money-financed fiscal stimulus is shown to have very strong effects on economic activity, with relatively mild inflationary consequences. If the steady state is sufficiently inefficient, an increase in government purchases may increase welfare even if such spending is wasteful.

On a regular basis, I get emails enquiring how to publish on RePEc some new system that will solve all of the world’s economic problems. Usually, this involves distributing money. This paper is also about distributing money to solve economic problems, but there are two major differences: Jordi Galí is no lunatic, and he only seeks to deal with temporary economic problems as they arise during a business cycle. The key here that nominal rigidities in prices and wages can do their magic and increase aggregate demand. Of course, prices eventually rise and the stimulus dissipates, but the temporary boosts is valuable when it matters most, given a level of rigidity that is plausible. The good old unemployment-inflation trade-off, only with a lag. I wonder though what will happen to the ridigity of prices and wages in a world where money policy becomes active in such ways. Wouldn’t they become more flexible in anticipation of more frequent money stimuli?

Exploiting the monthly data flow in structural forecasting

October 5, 2014

By Domenico Giannone, Francesca Monti and Lucrezia Reichlin

This paper shows how and when it is possible to obtain a mapping from a quarterly dynamic stochastic general equilibrium (DSGE) model to a monthly specification that maintains the same economic restrictions and has real coefficients. We use this technique to derive the monthly counterpart of the well-known DSGE model by Galí, Smets and Wouters (GSW) for the US economy. We then augment it with auxiliary macro indicators which, because of their timeliness, can be used to obtain a nowcast of the structural model. We show empirical results for the quarterly growth rate of GDP, the monthly unemployment rate and GSW’s welfare-relevant output gap. Results show that the augmented monthly model does best for nowcasting.

While temporal interpolation at higher frequencies is not new, it is nifty that a structural model is used here instead of a purely statistical setup. It is significantly more complicated than traditional methods, though.

Illiquidity and its Discontents: Trading Delays and Foreclosures in the Housing Market

October 2, 2014

By Aaron Hedlind

This paper investigates the macroeconomic effects of search risk in the housing market. To do so, I introduce a tractable directed search model of housing with multidimensional buyer and seller heterogeneity. I incorporate this framework in an incomplete markets macroeconomic model with long-term mortgages and equilibrium default. I show that search risk spills over into higher foreclosure risk by creating a debt overhang problem. Heavily indebted sellers post high selling prices, take a long time to sell, and frequently end up in foreclosure. As a result, search risk increases mortgage default premia and tightens credit constraints, thus exacerbating the debt overhang problem by making refinancing more difficult. This mechanism establishes a novel link between housing and mortgage markets based on the illiquidity of housing.

The illiquidity of the house market, along with the high correlation of house prices with local income, has always made me wonder why homeownership is so much encouraged. This paper gives a further argument why homeownership is a poor investment vehicle. And this paper applies to the US, where house markets are remarkably liquid in international comparison.


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