The EconomicDynamics Newsletter

                  Volume 13, Issue 2, April 2012

A free electronic supplement to the Review of Economic Dynamics
distributed through the EconomicDynamics mailing list and
also available on the web at http://www.EconomicDynamics.org/

In this issue:

- The Research Agenda: Stijn Van Nieuwerburgh on Housing and the Macroeconomy 
- EconomicDynamics interviews Frank Schorfheide on DSGE Model Estimation 
- Book Review: Nosal and Rocheteau's "Money, Payments, and Liquidity" 
- Impressum
- Subscribing/Unsubscribing/Address change

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The Research Agenda: Stijn Van Nieuwerburgh on Housing and the Macroeconomy

Stijn Van Nieuwerburgh is Professor of Finance at New York University's 
Stern School of Business. His research interests lie in housing, 
macroeconomics, and finance.

1. Introduction

An important part of my research focuses on the intersection of real 
estate, the largest financial asset for most households, asset markets, 
and the real economy. In the US, aggregate household residential real 
estate wealth is currently about $18 trillion and residential mortgage 
debt about $13 trillion. A common theme in my work is that housing plays a 
key role as collateral against which households can borrow. In several 
papers, I model the extent to which households use their house to insure 
against income shocks and study how changes in the value of housing 
affects interest rates and rates of return on risky assets. The main 
message from this research agenda is that, through its effect on risk 
sharing, fluctuations in housing collateral wealth can help explain 
puzzling features of stock returns, house prices, interest rates, and the 
cross-sectional dispersion in households' consumption. The research speaks 
to the dramatic swings in real estate markets we observed in the last 
fifteen years. In this overview I take the opportunity to report on some 
of my ongoing work in this area and to review some of the main findings of 
earlier research.

2. The Housing Boom and Bust: Time-Varying Risk Premia

An important challenge in the housing literature is to explain why house 
prices are so volatile relative to fundamentals such as rent (rental cost) 
and why price-to-rent ratios exhibit slow-moving boom-bust cycles all over 
the world. The unprecedented amplitude of the boom-bust cycle between the 
years 2000 and 2010 in particular begs for a coherent explanation.

In Favilukis, Ludvigson and Van Nieuwerburgh (2010), we generate booms and 
busts in house price-to-rent ratios that quantitatively match those 
observed in U.S. data in a model that accounts for the observed equity 
risk premium and risk-free rate behavior. A large preceding literature 
makes clear that this is a difficult task, especially in a model with 
production and realistic business cycle properties like ours (e.g., Davis 
and Heathcote 2005, Jermann 1998).

Specifically, we study a two-sector general equilibrium model of housing 
and non-housing production where heterogeneous households face limited 
risk-sharing opportunities as a result of incomplete financial markets. A 
house in the model is a residential durable asset that provides utility to 
the household, is illiquid (expensive to trade), and can be used as 
collateral in debt obligations. The model economy is populated by a large 
number of overlapping generations of households who receive utility from 
both housing and non-housing consumption and who face a stochastic 
life-cycle earnings profile. We introduce market incompleteness by 
modeling heterogeneous agents who face idiosyncratic and aggregate risks 
against which they cannot perfectly insure, and by imposing collateralized 
borrowing constraints on households (standard down-payment constraints). 
Within this context, we focus on the macroeconomic consequences of three 
systemic changes in housing finance, with an emphasis on how these factors 
affect risk premia in housing markets, and how risk premia in turn affect 
home prices. First, we investigate the impact of changes in housing 
collateral requirements. Second, we investigate the impact of changes in 
housing transactions costs. Third, we investigate the impact of an influx 
of foreign capital into the domestic bond market.

These changes are meant to capture important changes to the U.S. economy 
over the last fifteen years. Taken together, the first two factors 
represent the theoretical counterpart to the relaxation of credit 
standards in mortgage lending that took place in the real world between 
the late 1990s and the peak of the housing market in 2006, and the 
subsequent tightening of credit standards after 2006. We refer to these 
two changes as financial market liberalization (FML) and its reversal. 
During the boom years, the U.S. mortgage market saw a massive increase in 
the use of subprime mortgages, negative amortization and teaser rate 
loans, and low or no-documentation loans. It also saw a massive increase 
in the incidence and dollar volume of second mortgages and home equity 
lines of credit, and with it a large rise in the fraction of borrowers 
with combined loan-to-value ratios above 95 or even above 100%. Finally, 
the transaction costs associated with mortgage borrowing, home equity 
extraction, and mortgage refinancing fell rapidly while borrowers' 
awareness of the opportunities to tap into one's home equity rose. During 
the housing crisis and to this day, mortgage credit constraints tightened 
substantially, costs of tapping into one's home equity rose and both 
reverted to their pre-boom levels. Favilukis, Kohn, Ludvigson, and Van 
Nieuwerburgh (2011) provide detailed evidence as well as references to 
this literature.

The last 15 years were also marked by a sustained depression of long-term 
interest rates that coincided with a vast inflow of capital into U.S. safe 
bond markets. While in 1997 foreigners only held $1.6 trillion in U.S. 
Treasury and Agency bonds, that number had grown to $5.2 trillion by June 
2010, representing nearly half of the amounts outstanding. Interestingly, 
foreign purchases of safe U.S. securities not only rose sharply during the 
housing boom, but the inflows continued unabated during the housing bust. 
The vast bulk of these foreign purchases over this period (80%) were made 
by foreign official institutions, mostly Asian central banks. The increase 
in foreign purchases of U.S. safe assets accounts for the entire rise in 
the U.S. net foreign liability position in all securities, because the net 
position in risky securities hovers around zero.

The main impetus for rising price-rent ratios in the model in the boom 
period is the simultaneous occurrence of positive economic (TFP) shocks 
and a relaxation of credit standards, phenomena that generate an 
endogenous decline in risk premia on housing and equity assets. As risk 
premia fall, the aggregate house price index relative to aggregate rent 
rises. A FML reduces risk premia for two reasons, both of which are 
related to the ability of heterogeneous households to insure against 
aggregate and idiosyncratic risks. First, lower collateral requirements 
directly increase access to credit, which acts as a buffer against 
unexpected income declines. Second, lower transactions costs reduce the 
expense of obtaining the collateral required to increase borrowing 
capacity and provide insurance. These factors lead to an increase in 
risk-sharing, or a decrease in the cross-sectional variance of marginal 
utility. The housing bust is caused by a reversal of the FML, negative 
economic shocks, and an endogenous decrease in borrowing capacity as 
collateral values fall. These factors lead to an accompanying rise in 
housing risk premia, driving the house price-rent ratio down. Thus, in 
contrast with the literature, housing risk premia play a crucial role in 
house price fluctuations.

It is important to note that the rise in price-rent ratios caused by a FML 
in our study must be attributed to a decline in risk premia and not to a 
fall in interest rates. Indeed, the very changes in housing finance that 
accompany a FML drive the endogenous interest rate up, rather than down. 
It follows that, if price-rent ratios rise after a FML, it must be because 
the decline in risk premia more than offsets the rise in equilibrium 
interest rates that is attributable to the FML. This aspect of a FML 
underscores the importance of accounting properly for the role of foreign 
capital over the housing cycle. Without an infusion of foreign capital, 
any period of looser collateral requirements and lower housing 
transactions costs (such as that which characterized the housing boom) 
would be accompanied by an increase in equilibrium interest rates, as 
households endogenously respond to the improved risk-sharing opportunities 
afforded by a FML by reducing precautionary saving.

To model capital inflows, the third structural change in the model, we 
introduce foreign demand for the domestic riskless bond into the market 
clearing condition. We model foreign capital inflows as driven by foreign 
governments who inelastically place all of their funds in U.S. riskless 
bonds. Krishnamurty and Vissing-Jorgensen (2012) estimate that such 
foreign governmental holders, such as central banks, have a zero price 
elasticity for U.S. Treasuries, because they are motivated by reserve 
currency or regulatory motives (Kohn, 2002).

Our model implies that a rise in foreign purchases of domestic bonds, 
equal in magnitude to those observed in the data from 2000-2010, leads to 
a quantitatively large decline in the equilibrium real interest rate. Were 
this decline not accompanied by other, general equilibrium, effects, it 
would lead to a significant housing boom in the model. But the general 
equilibrium effects imply that a capital inflow is unlikely to have a 
large effect on house prices even if it has a large effect on interest 
rates. One reason for this involves the central role of time-varying 
housing risk premia. In models with constant risk premia, a decline in the 
interest rate of this magnitude would be sufficient by itself to explain 
the rise in price-rent ratios observed from 2000-2006 under reasonable 
calibrations. But with time-varying housing risk premia, the result can be 
quite different. Foreign purchases of U.S. bonds crowd domestic savers out 
of the safe bond market, exposing them to greater systematic risk in 
equity and housing markets. In response, risk premia on housing and equity 
assets rise, substantially offsetting the effect of lower interest rates 
and limiting the impact of foreign capital inflows on home prices. There 
is a second offsetting general equilibrium effect. Foreign capital inflows 
also stimulate residential investment, raising the expected stock of 
future housing and lowering the expected future rental growth rate. Like 
risk premia, these expectations are reflected immediately in house prices 
(pushing down the national house price-rent ratio), further limiting the 
impact of foreign capital inflows on home prices. The net effect of all of 
these factors is that a large capital inflow into safe securities has only 
a small positive effect on house prices.

In summary, there are two opposing forces simultaneously acting on housing 
risk. During the housing boom, there is both a FML and a capital inflow. 
The FML lowers risk premia, while foreign purchases of domestic safe 
assets raise risk premia. Under the calibration of the model, the decline 
in risk premia resulting from the FML is far greater than the rise in risk 
premia resulting from the capital inflow. The decline in risk premia on 
housing assets is the most important contributing factor to the increase 
in price-rent ratios during the boom. During the bust, modeled as a 
reversal of the FML but not the capital inflows, risk premia unambiguously 
rise while risk-free interest rates remain low. The rise in risk premia 
drives the decline in house-price rent ratios. Time variation in risk 
premia is the distinguishing feature that permits our model to explain not 
just the housing boom, but also the housing bust. Moreover, the model 
underscores the importance of distinguishing between interest rate changes 
(which are endogenous) and exogenous changes to credit supply. In the 
absence of a capital inflow, an expansion of credit supply in the form of 
lower collateral requirements and lower transactions costs should lead, in 
equilibrium, to higher interest rates, rather than lower, as households 
respond to the improved risk-sharing/insurance opportunities by reducing 
precautionary savings. Instead we observed low real interest rates, 
generated in our model by foreign capital inflows, but the inflows 
themselves are not the key factor behind the housing boom-bust.

Our model is silent on the origins of the relaxation of credit constraints 
and its subsequent tightening, but it is worthwhile to briefly digress and 
consider some possibilities. A first possibility is that mortgage lenders 
were confronted with exogenous changes in technology that affected 
mortgage finance. The boom period witnessed the birth of private-label 
securitization, collateralized debt obligations, credit default swaps, as 
well as automated underwriting and new credit scoring techniques employed 
in that underwriting (Poon, 2009). These innovations have been linked to 
the boom in mortgage credit and house price growth by Mian and Sufi (2009) 
and Keys, Seru, Piskorski and Vig (2012). Second, there was substantial 
legislative action that gave banks much more leeway to relax lending 
standards: Mian, Sufi and Trebbi (2010) mention 700 housing-related 
legislative initiatives that Congress voted on between 1993 and 2008 while 
Boz and Mendoza (2010) highlight the 1999 Gramm-Leach-Bliley and the 2000 
Commodity Futures Modernization Acts. Third, in this period, regulatory 
oversight over investment banks and mortgage lenders weakened 
substantially (Acharya and Richardson, 2009). For example, the regulatory 
treatment of AA or better rated private label residential mortgage-backed 
securities (MBS) was lowered in 2002 to the same low regulatory capital 
level as that applied to MBS issued by the Agencies since 1988. Also, 
since 2004 investment banks were allowed to use their internal models to 
assess the risk of the MBS and capital requirements fell even further. 
Regulatory capital rules were relaxed on guarantees that banks extended to 
the special purpose vehicles they set up and that housed a good fraction 
of mortgage credit (Acharya, Schnabel, and Suarez, 2012). These changes 
took place in an environment where private sector mortgage lenders where 
engaged in a race to the bottom with the government-sponsored enterprises, 
who themselves were substantially affected by regulatory changes and 
implicit government guarantees (Acharya, Richardson, Van Nieuwerburgh and 
White, 2011). Faced with such changes in their economic environment, 
mortgage lenders formed expectations of higher future house price growth, 
justifying more and riskier mortgages as in the optimal contracting 
framework of Piskorski and Tchystyi (2010). The bust saw a tightening of 
regulatory oversight and the Dodd-Frank Act (Acharya, Cooley, Richardson 
and Walter, 2011), to which lenders responded by cutting back on credit.

3. International Evidence and the Role of Capital Flows in the Housing 
Boom and Bust

In follow-up empirical work, Favilukis, Kohn, Ludvigson and Van 
Nieuwerburgh (2011) study the empirical relationship between house prices, 
foreign capital flows, and a direct measure of credit standards for a 
cross-section of countries. Across countries, we find a positive 
correlation between house price growth and foreign capital inflows 
(current account deficits) during the boom period, but a negative 
correlation during the bust. For a smaller subset of countries we have a 
direct measure of the tightness of credit constraints from senior loan 
officers' surveys on banks' standards of supplying mortgage credit to 
households. In a panel regression for 11 countries for a sample that spans 
the boom and bust, we find a strong positive association between the 
fraction of banks that eases credit standards and house price growth. Over 
the same sample, such a relationship is absent between current account 
deficits and house price growth. These results are robust to alternative 
measures of capital flows. Longer time series evidence for the U.S. 
suggests that more than 50% of variability in house price growth is 
accounted for by changes in credit standards, and very little by the 
dynamics of the current account. Our measure of credit standards is 
positively related to the ratio of non-conforming to conforming mortgage 
originations. In sum, the time series and cross-country data seem 
supportive of the notion that changes in international capital flows 
played, at most, a small role in driving house prices during this time, 
both in the U.S. and around the world.

4. Foreign Holdings of U.S. Safe Assets: Welfare Effects for U.S. 
Households

In Falukis, Ludvigson and Van Nieuwerburgh (2012), we use a similarly rich 
framework to evaluate the implications of the dramatic rise on foreign 
holdings of U.S. safe assets for the welfare of U.S. households. Despite a 
vigorous academic debate on the question of whether global imbalances are 
a fundamentally benign or detrimental phenomenon (see Gourinchas (2006) 
Mendoza, Quadrini and Rios-Rull (2007), Caballero, Fahri, and Gourinchas 
(2008a), Caballero, Fahri and Gourinchas (2008b), Obstfeld and Rogoff 
(2009), and Caballero (2009)), little is known about the potential welfare 
consequences of these changes in international capital flows, or of 
foreign ownership of U.S. safe assets in particular. We argue in this 
paper that a complete understanding of the welfare implications requires a 
model with realistic heterogeneity, life-cycle dynamics, and plausible 
financial markets. The model has a special role for housing as a 
collateral asset.

The model economy implies that foreign purchases (or sales) of the safe 
asset have quantitatively large distributional consequences, reflecting 
sizable tradeoffs between generations, and between economic groups 
distinguished by wealth and income. Indeed, the results suggest that a 
sell-off of foreign government holdings of U.S. safe assets could be 
tremendously costly for some individuals, while the possible benefits to 
others are many times smaller in magnitude.

Welfare outcomes are influenced by the endogenous response of asset 
markets to fluctuations in foreign holdings of the safe asset. Foreign 
purchases of the safe asset act like a positive economic shock and have an 
economically important downward impact on the risk-free interest rate, 
consistent with empirical evidence. Although lower interest rates boost 
output, equity and home prices relative to measures of fundamental value, 
foreign purchases of the domestic riskless bond also reduce the effective 
supply of the safe asset, thereby exposing domestic savers to greater 
systematic risk in equity and housing markets. In response, risk premia on 
housing and equity assets rise, substantially (but not fully) offsetting 
the stimulatory impact of lower interest rates on home and equity prices. 
These factors imply that the young and the old generations experience 
welfare gains from a capital inflow, while middle-aged savers suffer. The 
young benefit from higher wages and from lower interest rates, which 
reduce the costs of home ownership and of borrowing in anticipation of 
higher expected future income. On the other hand, middle-aged savers are 
hurt because they are crowded out of the safe bond market and exposed to 
greater systematic risk in equity and housing markets. Although they are 
partially compensated for this in equilibrium by higher risk premia, they 
still suffer from lower expected rates of return on their savings. By 
contrast, retired individuals suffer less from lower expected rates of 
return, since they are drawing down assets at the end of life. They also 
receive social security income that is less sensitive to the current 
aggregate state than is labor income, making them more insulated from 
systematic risk. Taken together, these factors imply that the oldest 
retirees experience a significant net gain even from modest increases in 
asset values that may accompany a capital inflow.

The magnitude of these effects for some individuals is potentially quite 
large. For example, in the highest quintile of the external leverage 
distribution, the youngest working-age households would be willing to give 
up over 2% of life time consumption in order to avoid just one year of a 
typical annual decline in foreign holdings of the safe asset (which 
amounts to about 2% of U.S. trend GDP). This effect could be several times 
larger for a greater-than-typical decline, and many times larger for a 
series of annual declines in succession or spaced over the remainder of 
the household's lifetime. By contrast, the absolute value of the 
equivalent variation welfare measure we study is often one-tenth of the 
size (and in general of the opposite sign) for sixty year-olds than it is 
for the youngest or oldest households. Thus, middle-aged households often 
stand to gain from an outflow, but their gain is much smaller in magnitude 
than are the losses for the youngest and oldest.

We also compute welfare consequences for groups that vary according to 
total wealth, housing wealth, and income, as well as an ex-ante measure 
for agents just being born. The latter provides one way of summarizing the 
expected welfare effects over the life cycle, as experienced by a newborn 
whose stochastic path of future earnings and foreign capital inflows is 
unknown. Under the veil of ignorance, newborns benefit from foreign 
purchases of the safe asset and would be willing to forgo up to 18% of 
lifetime consumption in order to avoid a large capital outflow.

Our study focuses on the effect of a reserve-driven upward trend in the 
U.S. net foreign debtor position over time on the macroeconomy and 
welfare. Our model is silent on the economic implications of gross flows, 
and we do not study cyclical fluctuations in the value of net foreign 
holdings of other securities which, unlike net foreign holdings of U.S. 
safe assets, show no upward trend (Favilukis, Kohn, Ludvigson and Van 
Nieuwerburgh, 2011). By contrast, Gourinchas and Rey (2007) and Maggiori 
(2011) investigate how the net foreign asset position of the U.S. invested 
in risky securities varies cyclically across normal and crisis times, as 
well as how gross flows are affected. On the other hand, these papers are 
silent on the reasons for the large and growing net foreign debtor 
position of the U.S. in good times, and on its upward trend over time. We 
view these studies as complementary to our study. Integrating both aspects 
of foreign flows in one model seems like a priority for future research.

5. Housing Collateral, Financial Market Puzzles, and Measures of Risk 
Sharing

My earlier work explores the role of housing as a collateral asset in 
models of limited commitment, along the lines of Krueger (1999), Alvarez 
and Jermann (2000), and Chien and Lustig (2010). Lustig and Van 
Nieuwerburgh (2005) predicts that households are less keen to take on 
financial risks, and therefore demand a higher return for bearing these 
risks, when housing collateral is scarce. In U.S. aggregate data, we show 
that a decrease in housing collateral is followed by higher future stock 
returns, in excess of the risk-free rate and that this relationship is 
statistically significant. The cross-sectional prediction of the model is 
that assets whose returns covary more positively with the value of housing 
must offer their investors higher returns relative to other assets. In 
contrast, assets whose value increases when housing collateral is scarce 
are a valuable hedge against the risk of being borrowing-constrained. This 
additional benefit induces the holders of these assets to accept lower 
returns. In the data, this mechanism explains more than 80% of the 
cross-sectional difference between average returns on value (high 
book-to-market ratio) and growth stocks (low book-to-market ratio). Its 
pricing errors compare favorably to those of competing asset pricing 
models. The model upon which these empirical results are based, spelled 
out in Lustig and Van Nieuwerburgh (2007), also provides an explanation 
for why short-duration assets, whose risky cash flows accrue in the near 
future, have higher risk premia than long-duration assets, an empirical 
fact highlighted by Binsbergen, Brandt and Koijen (2011). The second piece 
of evidence on the housing collateral and risk sharing mechanism comes 
from quantity data for U.S. metropolitan areas. Lustig and Van 
Nieuwerburgh (2010) measures the degree of risk sharing as the 
cross-sectional variance of consumption relative to the cross-sectional 
variance of income. The model aggregates heterogeneous, 
borrowing-constrained households into regions characterized by a common 
housing market and solves for the equilibrium consumption dynamics. It 
generates a lower degree of risk sharing when housing collateral is scarce 
to an extent similar to what we find in the data.

6. Regional Variation in Housing Prices

My interest in regional variations across housing markets led to a project 
that explores why house prices differ across regions and over time. The 
spatial location model in Van Nieuwerburgh and Weill (2010) is one of the 
first dynamic versions of the seminal Rosen (1979) and Roback (1982) model 
in urban economics. Regions differ in their productivity levels and 
therefore the wages paid to their resident workers. Since workers are free 
to move across regions, house prices must adjust to make them indifferent 
between living in any region. Regions which experience fast wage growth 
attract new households who bid up house prices. Housing supply regulation 
constrains the number of new units that can be built per period in each 
area; muting the response of quantities amplifies price changes. By 
feeding realized regional wages into a calibrated version of the model, we 
can explain the magnitude of the increase in average house prices and the 
increase in the dispersion of house prices across regions over the 
1975-2005 period. Interestingly, a tightening in housing supply regulation 
by itself -an alternative candidate explanation for the observed changes 
in the house price distribution- does not generate much of an increase in 
the price level or its dispersion in the model because households can 
relocate.

While the paper produces rich patterns for house prices across time and 
space and matches important features of the data over the sample period of 
study, it would fall short in accounting for house prices over the recent 
boom and bust period described above. This is because the model does not 
generate time variation in risk premia associated with relaxing and 
tightening credit constraints. An important research challenge going 
forward is to enrich the spatial housing models so that they imply richer 
asset pricing dynamics. This would allow us to understand better the 
heterogeneous house price experience of U.S. metropolitan areas over the 
last decade.

7. Conclusion

In light of the recent events, there has never been a more relevant time 
to work on housing and its implications for macroeconomics and asset 
markets at large. There is a flurry of exciting research in progress by 
established and young researchers alike, studying a range of interesting 
questions. How can we account for the magnitude and dynamics of mortgage 
foreclosures and how do they affect the macro-economy? How successful are 
the government's mortgage modification programs in getting the U.S. 
economy back on track? What are the macro-economic implications of the 
credit crunch that is currently taking place in mortgage markets in the 
U.S.? What should the future architecture of the U.S. housing finance 
system look like and what can we learn from other countries? Finally, 
commercial real estate remains a largely unexplored asset class in the 
macro-finance literature despite its size and importance to the 
macroeconomy. These are some of the questions I hope the profession will 
continue to make progress on going forward.

Selected references to the papers mentioned are available in
the web version of this newsletter at
http://www.EconomicDynamics.org/newsletter.htm

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EconomicDynamics Interviews Frank Schorfheide on DSGE Model Estimation

Frank Schorfheide is Professor of Economics at the University of 
Pennsylvania. He is interested in the estimation of DSGE models, Bayesian 
methods, vector autoregressions.


EconomicDynamics: DSGE model used to be exclusively calibrated. Your work 
was a major contributor in bringing estimation to this literature. Where 
do you see the major advantages of estimating a DSGE model?

Frank Schorfheide: When I started my research as a PhD student in the 
mid 1990s there seemed to be strong misconceptions among calibrators about 
what econometrics can deliver and among econometricians about what it 
means to calibrate. Both camps seemed to engage in some sort of trench 
warfare launching grenades at what were poor incarnations of econometrics 
and calibration analysis. The stereotype among calibrators was that 
econometrics requires "true" models and the stereotype among 
econometricians was that calibrators pick parameters in an arbitrary way, 
disregarding empirical evidence. While this made for good pub 
conversations, it didn't exactly facilitate progress in empirical 
macroeconomics.

My personal interest, when I started to work on econometric methods 
for the analysis of DSGE models, was to develop a formal statistical 
framework (Schorfheide 2000) that captures some of the reservations of 
calibrators: the framework should be able to account for misspecification 
of DSGE models and it should recognize that objective functions for the 
determination of parameters should be derived from loss functions that are 
connected to the decision problems that the model is supposed to solve.

Once one recognizes that econometrics does not need to rely on the 
"Axiom of Correct DSGE Model Specification" it offers a lot of tools that 
are useful to summarize parameter uncertainty, uncertainty associated with 
model implications, forecasts, and policy predictions, and it provides 
coherent measures of fit for the comparison and weighting of competing 
models. My favorite approach of dealing with DSGE model misspecification 
is to use the models to construct priors for VARs or other flexible time 
series models. Starting in 2004, I have explored this idea in several 
co-authored papers with Marco Del Negro. We called the resulting hybrid 
model DSGE-VAR.

In the past decade the time series fit of (representative agent) DSGE 
models has improved considerably, an example is the celebrated 
Smets-Wouters model, such that the initial concerns about inappropriate 
probabilistic structures of the model became less relevant. In turn, the 
use of formal econometric tools is much more attractive now than it was 20 
years ago. I have discussed some of the progress and the challenges in the 
area of DSGE model estimation in Schorfheide (2010).


ED: Can a case still be made for calibration?

FS: A few years ago my colleague Victor Rios-Rull and I engaged into 
the following computational/educational experiment. At the time Victor was 
teaching his quantitative macro class and I was teaching my time series 
econometrics class. We both asked our students to use a stochastic growth 
model to measure the importance of technology shocks for business cycle 
fluctuations of hours and output. Victor's students were supposed to 
calibrate the model and my students were supposed to estimate it with 
Bayesian methods.

Together with some of our students we later turned the results into a 
paper. While the two of us favor different empirical strategies, the paper 
emphasizes the most important aspect of the empirical analysis is how the 
key parameters of the model can be identified based on the available data. 
Once there is some agreement on plausible sources of identification, these 
sources can be incorporated into either an estimation objective function 
or a calibration objective function.

In fact, Bayesian estimation and calibration are much closer than many 
people think. The steps taken when prior distributions for DSGE model 
parameters are elicited are often quite similar to the steps involved in 
the calibration. Moreover, both calibration and estimation tend to 
condition on the data and are not concerned about repeated sampling. The 
main difference is that Bayesians tend to utilize the information in the 
likelihood function, whereas in a calibration analysis the information in 
the autocovariances of macroeconomic time series is often deliberately 
ignored when it comes to the determination of parameters.

Coming back to the question, calibration is particularly attractive in 
models that have a complicated structure, e.g. heterogeneous agent 
economies, and are costly (in terms of computational time) to solve 
repeatedly for different parameter values. However, it is important to 
clearly communicate how the data are used to determine the model 
parameters and to what extent the model is consistent or at odds with 
salient features of the data.


ED: DSGE models and calibration were a response to the Lucas Critique. 
Isn't the estimation of inherently abstract models a step backwards in 
this respect?

FS: Not at all. Let me modify your statement as follows: DSGE models 
were a response to the Lucas Critique and, at the early stage of 
development, calibration was a way of parameterizing DSGE models in view 
of their stylized structure.

The Lucas Critique was concerned with the lack of policy-invariance of 
estimated decision rules, e.g. consumption equations, investment 
equations, or labor supply equations. In turn, macroeconomists specified 
their models in terms of agents' "preferences and technologies" and 
derived the decision rules as solutions to intertemporal optimization 
problems, imposing a dynamic equilibrium concept. Counterfactual policy 
analyses could then be conducted by re-solving for the equilibrium under 
alternative policy regimes and comparing the outcomes.

Arguably, the more stylized the DSGE model, the less convincing the 
claim that the preference and technology parameters are indeed 
policy-invariant, which undermines the credibility of the counterfactual 
policy analysis. In Schorfheide, Chang and Kim (forthcoming) we provide 
some simulation evidence that the aggregate labor supply elasticity and 
the aggregate level of total factor productivity in a representative agent 
model is sensitive to changes in the tax rate if the representative agent 
model is an approximation of a heterogeneous agent economy. In turn, 
policy predictions with the representative agent model tend to be 
inaccurate.


ED: Forecasting has traditional been limited to purely statistical models. 
You have started evaluating the forecasting performance of estimated DSGE 
models. Can the limitations from theory still allow them to compete with 
models fitted for forecasting?

FS: Marco Del Negro and I recently wrote a chapter for a forthcoming 
second volume of the Elsevier Handbook of Economic Forecasting and we used 
the following analogy: while a successful decathlete may not be the 
fastest runner or the best hammer thrower, he certainly is a well-rounded 
athlete. In this analogy the DSGE model is supposed to be the decathlete 
that competes in various disciplines such as forecasting, policy analysis, 
story telling, etc., and it has to compete on the one hand with purely 
statistical forecast models that are optimized to predict a particular 
series, e.g. inflation, and on the other hand with less quantitative and 
more specialized applied theory models that highlight, say, particular 
frictions in financial intermediation or in the housing market.

Our general reading of the literature and the finding in our own work 
is that (i) DSGE models, in particular models that have been tailored to 
fit the data well -- such as the Smets and Wouters (2007) model, are 
competitive with statistical models in terms of forecast accuracy. But 
when push comes to shove elaborate statistical models can certainly beat 
DSGE models. (ii) The use of real-time information, e.g. treating nowcasts 
of professional forecasters as current quarter observations, can 
drastically improve short-run forecasting performance. (iii) Anchoring 
long-run inflation dynamics in the DSGE model with observations on 10-year 
inflation expectations improves inflation forecasts. (iv) Relaxing the 
DSGE model restrictions a little bit by using the DSGE model to generate a 
prior for the coefficients for a VAR also helps to boost forecast 
performance.


ED: Recent economic history gives good reasons to believe non-linear 
phenomena may be at play at business cycle frequencies. How should we 
study this?

FS: Nonlinearities tend to be compelling ex post but are often elusive 
ex ante. In the time series literature there exists an alphabet soup of 
reduced-form nonlinear models. Many of these models have been developed to 
explain certain historical time series pattern ex post, but most of them 
do not perform better than linear models in a predictive sense (though 
there are some success stories).

When I looked at real-time forecasts from linearized DSGE models and 
vector autoregressions during the 2007-09 recession I was surprised how 
well these models did -- in the following sense: of course they did not 
predict the large drop in output in the second half of 2008, but neither 
did, say, professional forecasters. However, in early 2009, the models 
were back on track. So, for a nonlinear model to beat these models in a 
predictive sense, it would have had to predict the 2008:Q4 downturn, say, 
in July.

Of course, the ex-post story of a linear model for the recent 
recession is that it was caused by large shocks with a magnitude of 
multiple standard deviations. This may not be particularly compelling -- 
since the narrative for the financial crisis involves problems in the 
Mortgage market that lead to a severe disruption of financial 
intermediation and economic activity. A model that captures this mechanism 
has to be inherently nonlinear and the development of models with 
financial frictions is an important area of current research.

Our standard stochastic growth model as well as the typical New 
Keynesian DSGE model are actually fairly linear (at least for 
parameterizations that can replicate post-war U.S. business cycle 
fluctuations). However, researchers have been adding mechanisms to these 
models that can generate nonlinear dynamics, including stochastic 
volatility, learning mechanisms, borrowing constraints, non-convex 
adjustment costs, a zero-lower-bound on nominal interest rates, to name a 
few. This is certainly an important direction for future research.

Aruoba, Bocola and Schorfheide (2012) started to work on the 
development of a class of nonlinear time series models that can be used to 
evaluate DSGE models with nonlinearities -- in the same way that we have 
used VARs to evaluate linearized DSGE models. With a simple univariate 
version of this nonlinear time series model one can pick up some 
interesting empirical features, e.g. asymmetries across recessions and 
expansions in GDP growth and zero-lower-bound dynamics of interest rates. 
We are currently working on multivariate extensions.


ED: We tend to focus on deviations from a trend or steady state. But in 
many ways trends matter more. Is there any work on estimating trends in 
DSGE models, and if not, should there be?

FS: Most estimated DSGE model nowadays have the trend incorporated 
into the model. For instance, in a stochastic growth model, a trend in the 
endogenous variables can be generated by assuming that the technology 
process has a deterministic trend or a stochastic trend (e.g., random walk 
with drift). The advantage of this method is that one does not have to 
detrend the macroeconomic time series prior to fitting the DSGE model. The 
disadvantage is that the DSGE model imposes very strong co-trending 
restrictions that are to some extent violated in the data.

For instance, the basic stochastic growth model implies that output, 
consumption, investment, and real wages have a common trend, whereas hours 
worked is stationary. However, in the data the "great ratios," e.g. 
consumption-output or investment-output are not exactly stationary. 
Moreover, hours worked are often very persistent and exhibit 
unit-root-type dynamics. As a result, some of the estimated shock 
processes tend to be overly persistent because they have to absorb the 
trend misspecification. This might distort the subsequent analysis with 
the model.

In general, this is an important topic but there is no single solution 
that is completely satisfactory. Neither the old method of detrending each 
series individually and then modeling deviations from trends with a model 
that has clear implications about long-run equilibrium relationships, nor 
the newer method of forcing misspecified trends on the data are entirely 
satisfactory. More careful research on this topic would be very useful.

In my own (co-authored) work (Del Negro and Schorfheide forthcoming, 
and Aruoba and Schorfheide 2011), one of the more successful attempts to 
dealing with trends -- not in real series, but in nominal series -- was to 
include time-varying target inflation rates into the model and to "anchor" 
the inflation target by observations on long-run inflation expectations. 
This really helps the fit and the forecast performance and has the 
appealing implication that low-frequency movements in inflation and 
interest rates are generated by changes in monetary policy.


ED: Is theory still ahead of measurement?

FS: The phrase "theory ahead of measurement" is connected to 
Prescott's (1986) conjecture that (some of) the discrepancy between 
macroeconomic theories and data could very well disappear "if the economic 
variables were measured more in conformity with theory." The phrase 
becomes problematic if it is used as an excuse not to subject 
macroeconomic models to a careful empirical evaluation.

In general we are facing the problem that in order to keep or models 
tractable we have to abstract from certain real phenomena. Take for 
instance seasonality. We could either build models that can generate 
fluctuations at seasonal frequencies or we could remove these fluctuations 
from the data. Most people would probably agree that matching a model that 
is not designed to generate seasonal fluctuations to seasonally-unadjusted 
data is not a good idea. The profession has converged to an equilibrium in 
which seasonality is removed from the data and not incorporated into DSGE 
models.

However, in other dimensions there is more of a disagreement among 
economists: what are the right measures of price inflation, wage 
inflation, hours worked, and interest rate spreads that should be used in 
conjunction with aggregate DSGE models? In general, a careful measurement 
of key economic concepts is very important, but that does not mean that 
theory is ahead of measurement.

I do agree with the following statement in Prescott's (1986) 
conclusion: "Even with better measurement, there will likely be 
significant deviations from theory which can direct subsequent theoretical 
research. This feedback between theory and measurement is the way mature, 
quantitative sciences advance."


Selected references to the papers mentioned are available in
the web version of this newsletter at
http://www.EconomicDynamics.org/newsletter.htm

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Book Review: Nosal and Rocheteau's "Money, Payments, and Liquidity"
Money, Payments, and Liquidity
by Ed Nosal and Guillaume Rocheteau

Monetary theory has made rapid progress with a new field opening up within 
the past decade, money search. These new developments are somewhat 
difficult to follow for the outsider as there is no work that would 
summarize what it is about and what the main results are, except for a 
very recent handbook chapter by Steve Williamson and Randall Wright.

Ed Nosal and Guillaume Rocheteau fill this void with a book that tries to 
take the most modern approach to monetary theory. This is a book that is 
also meant to be a textbook for graduate classes. It uses as a starting 
point the Lagos-Wright model with alternating market structure. Each 
subsequent chapter builds on it to study the impact of credit, credit 
frictions, pricing mechanisms, and the properties of money. The books then 
expands on monetary policy, the coexistence of money and credit, and 
ultimately also other assets and how trading frictions impact asset 
markets, prices and liquidity.

The strength of the book is the unified framework. The same basic model is 
used to touch many issues, which also demonstrates the versatility of the 
approach. This comes at the cost of alternative approaches, which may be 
better suited for some questions, and which may prevail in this afterall 
very young literature. The pedagogy, however, dominates and delivers a 
very readable introduction into money search.

Money, Payments, and Liquidity is published by MIT Press.

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Impressum

The EconomicDynamics Newsletter is a free supplement to the Review of
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The EconomicDynamics Newsletter is published twice a year in April and
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