I build a Supply–Demand Ambiguity Index measuring forecaster disagreement over whether inflation is supply- or demand-driven. On U.S. quarterly data from 1991 to 2024, higher ambiguity goes with lower S&P 500 excess returns, controlling for the VIX and policy uncertainty. It also compresses Treasury yields and term premia, and a SUR joint test confirms the cross-asset effect. The interaction with inflation news is insignificant, though a lagged channel shows up in bonds. The findings are consistent with ambiguity aversion lowering valuations when inflation’s source is unclear, though the reduced-form evidence cannot fully separate it from risk-off and Bayesian-disagreement channels.

This paper investigates the effects of uncertainty spillovers on emerging markets. We focus on COVID-19-related news as news about global uncertainty and estimate the dynamic response of high-frequency risk measures in emerging markets. Using heteroskedasticity-based estimation and aggregate emerging market indices, we show that heightened uncertainty increases government bond and CDS spreads and decreases stock prices. Using seven emerging markets, we show that country-level risk measures respond to uncertainty consistently with aggregate measures. We argue that the results are consistent with standard consumption-based asset pricing theory.

For the academic audience, this paper presents the outcome of a well-identified, large change in the monetary policy rule from the lens of a standard New Keynesian model and asks whether the model properly captures the effects. For policymakers, it presents a cautionary tale of the dismal effects of ignoring basic macroeconomics. In doing so, it also clarifies how neo-Fisherian disinflation may work or fail, in theory and in practice. The Turkish monetary policy experiment of the past decade, stemming from a belief of the government that higher interest rates cause higher inflation, provides an unfortunately clean exogenous variance in the policy rule. The mandate to keep rates low, and the frequent policymaker turnover orchestrated by the government to enforce this, led to the Taylor principle not being satisfied and eventually a negative coefficient on inflation in the policy rule. In such an environment, was the exchange rate still a random walk? Was inflation anchored? Does the "standard model" suffice to explain the broad contours of macroeconomic outcomes in an emerging economy with large identifying variance in the policy rule? There are no surprises for students of open-economy macroeconomics; the answers are no, no and yes.

Central banks unexpectedly tightening policy rates often observe the exchange value of their currency depreciate, rather than appreciate as predicted by standard models. We document this for Fed and ECB policy days using event studies and ask whether an information effect, where the public attributes the policy surprise to an unobserved state of the economy that the central bank is signaling by its policy may explain the abnormality. It turns out that many informational assumptions make a standard two- country New Keynesian model match this behavior. To identify the particular mechanism, we condition on multiple asset prices in the event study and model implications for these. We find that there is heterogeneity in this dimension in the event study and no model with a single regime can match the evidence. Further, even after conditioning on possible information effects driving longer term interest rates, there appear to be other drivers of exchange rates. Our results show that existing models have a long way to go in reconciling event study analysis with model-based mechanisms of asset pricing.

Recently some authors have argued that a New Keynesian model with simple modifications can match the nominal term structure of interest rates. In this paper, I investigate how well these models do in matching the term structure of real rates using TIPS data. I find that a standard New Keynesian model that is successful in matching nominal term structure properties cannot match real yield curve features. Then I investigate the model's relative success in fitting the nominal term structure and show that the model generates implausibly volatile inflation expectations and an implausibly high inflation risk premium to fit the nominal yield curve to compensate for the lack of fit to real yields. I study various potential extensions of the benchmark model and find that incorporating labor market frictions, long-run productivity risks, and preference shocks is not helpful in matching real term structure features.

Macroeconomic news announcements are elaborate and multidimensional. We consider a framework in which jumps in asset prices around announcements reflect both the response to observed surprises in headline numbers and to latent factors, reflecting other news in the release. Non-headline news, for which there are no expectations surveys, is unobservable to the econometrician but nonetheless elicits a market response. We estimate the model by the Kalman filter, which efficiently combines OLS and heteroskedasticity-based event study estimators in one step. With the inclusion of a single latent surprise factor, essentially all yield curve variance in event windows are explained by news. (JEL C51, E43, E52, G12, G14)

This paper investigates high frequency movements of the yield curve around macroeconomic announcements by combining event studies and a no-arbitrage affine term structure model in a new Keynesian model with partial (or imperfect) information. I show that the model fits bond yields and macroeconomic announcement surprises well. The model can generate the empirical response of bond yields to surprises and the standard deviations of the bond yields around the announcement days. The decomposition of long term nominal zero coupon bond yields shows that the high frequency variation in the long term bond yields is due to a combination of changes in the term premium and revisions to expected short rates. The model estimates imply that around macroeconomic announcement days, average expected short rates and term premia are highly variable with a strong negative correlation. I show that the model implied term premium estimates are strongly correlated with estimates of different reduced form models. The model implies that the behavior of the long term rates in the conundrum period can be explained by a lower term premium.

This paper investigates high frequency movements of the yield curve around macroeconomic announcements by combining event studies and a no-arbitrage affine term structure model in a new Keynesian model with partial (or imperfect) information. I show that the model fits bond yields and macroeconomic announcement surprises well. The model can fit the empirical responses of bond yields to surprises and the standard deviations of the bond yields around the announcement days. The decomposition of long term nominal zero coupon bond yields shows that the high frequency variation in the long term bond yields is due to a combination of changes in the term premium and revisions to expected short rates. In particular, changes in term premia are as volatile as revisions to expected future short rates. The model estimates imply that around macroeconomic announcement days, average expected short rates and term premia are correlated around announcements. I show that the model implied term premium estimates are strongly correlated with estimates of different reduced form models. The model implies that the behavior of the long term rates in the conundrum period can be explained by a lower term premium. I show that most announcement responses of bond yields are due to changes in the expectations about the output gap.

Recently, it has been suggested that macroeconomic forecasts from estimated DSGE models tend to be more accurate out-of-sample than random walk forecasts or Bayesian VAR forecasts. Del Negro and Schorfheide(2013) in particular suggest that the DSGE model forecast should become the benchmark for forecasting horse races. We compare the real-time forecasting accuracy of the Smets and Wouters DSGE model with that of several reduced form time series models. We first demonstrate that none of the forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically simple AR models are most accurate at short horizons and DSGE models are most accurate at long horizons when forecasting output growth, while for inflation forecasts the results are reversed. Moreover, the relative accuracy of all models tends to evolve over time. Third, we show that there is no support the common practice of using large-scale Bayesian VAR models as the forecast benchmark when evaluating DSGE models. Indeed,low-dimensional unrestricted AR and VAR forecasts may forecast more accurately.

We study the forecasting ability of the standard estimated medium scale dynamic stochastic general equilibrium model. We show that although over the Great Moderation period the model forecasts have good relative forecasting ability, in an absolute sense their forecasting ability is poor. However, we argue that average forecasting ability during the Great Moderation is not a good metric to judge a model’s validity given that this is a period that is well-known to be characterized by a lack of persistent fluctuations in the data. We then consider the forecast performance of the DSGE model prior to the Great Moderation – a period that is known to be characterized by persistent and thereby forecastable fluctuations in the data generating process – and find notably better absolute forecasting performance. We then offer alternative ways of using forecasts to judge the empirical validity of the model. In particular, we suggest that looking at whether the model captures well the forecastability versus nonforecastability of the data upon which the model is estimated is a more fitting question than simply whether the model forecasts well. As part of the empirical analysis that we undertake to address our key questions, we also uncover and document the importance of data and sample choices in the model’s forecasting ability. ---VERY PRELIMINARY AND VERY INCOMPLETE-----PLEASE DO NOT CITE---

This paper examines the effects of forward guidance at the zero lower bound on the term structure of interest rates in a shadow-rate macro-finance term structure model. The effects on the yield curve are found to depend on the type of forward guidance and on the current level of the shadow rate. The more negative the shadow rate, and so the further away liftoff is, the less effective is forward guidance. Forward guidance affects both the expected path of future short rates, but also term premia. Our model allows us to estimate these effects separately. We also conduct an event-study in which we break out FOMC announcements into surprises concerning the future path of the funds rate, and uncertainty around that path, and then estimate the impacts of each on equity and currency markets.