Search Results
Working Paper
Robust inference in linear asset pricing models
We derive new results on the asymptotic behavior of the estimated parameters of a linear asset pricing model and their associated t-statistics in the presence of a factor that is independent of the returns. The inclusion of this "useless" factor in the model leads to a violation of the full rank (identification) condition and renders the inference nonstandard. We show that the estimated parameter associated with the useless factor diverges with the sample size but the misspecification-robust t-statistic is still well-behaved and has a standard normal limiting distribution. The asymptotic ...
Report
A Jackknife Variance Estimator for Panel Regressions
We introduce a new jackknife variance estimator for panel-data regressions. Our variance estimator can be motivated as the conventional leave-one-out jackknife variance estimator on a transformed space of the regressors and residuals using orthonormal trigonometric basis functions. We prove the asymptotic validity of our variance estimator and demonstrate desirable finite-sample properties in a series of simulation experiments. We also illustrate how our method can be used for jackknife bias-correction in a variety of time-series settings.
Working Paper
Forecasts of inflation and interest rates in no-arbitrage affine models
In this paper, we examine the forecasting ability of an affine term structure framework that jointly models the markets for Treasuries, inflation-protected securities, inflation derivatives, and oil future prices based on no-arbitrage restrictions across these markets. On the methodological side, we propose a novel way of incorporating information from these markets into an affine model. On the empirical side, two main findings emerge from our analysis. First, incorporating information from inflation options can often produce more accurate inflation forecasts than those based on the Survey of ...
Working Paper
Minimum distance estimation of possibly non-invertible moving average models
This paper considers estimation of moving average (MA) models with non-Gaussian errors. Information in higher-order cumulants allows identification of the parameters without imposing invertibility. By allowing for an unbounded parameter space, the generalized method of moments estimator of the MA(1) model has classical (root-T and asymptotic normal) properties when the moving average root is inside, outside, and on the unit circle. For more general models where the dependence of the cumulants on the model parameters is analytically intractable, we consider simulation-based estimators with two ...
Working Paper
Analytical solution for the constrained Hansen-Jagannathan distance under multivariate ellipticity
We provide an in-depth analysis of the theoretical properties of the Hansen-Jagannathan (HJ) distance that incorporates a no-arbitrage constraint. Under a multivariate elliptical distribution assumption, we present explicit expressions for the HJ-distance with a no-arbitrage constraint, the associated Lagrange multipliers, and the SDF parameters in the case of linear SDFs. This approach allows us to analyze the benefits and costs of using the HJ-distance with a no-arbitrage constraint to rank asset pricing models.
Working Paper
Multivariate return decomposition: theory and implications
In this paper, we propose a model based on multivariate decomposition of multiplicative?absolute values and signs?components of several returns. In the m-variate case, the marginals for the m absolute values and the binary marginals for the m directions are linked through a 2m-dimensional copula. The approach is detailed in the case of a bivariate decomposition. We outline the construction of the likelihood function and the computation of different conditional measures. The finite-sample properties of the maximum likelihood estimator are assessed by simulation. An application to predicting ...
Discussion Paper
The Persistent Compression of the Breakeven Inflation Curve
Breakeven inflation, defined as the difference in the yield of a nominal Treasury security and a Treasury Inflation-Protected Security (TIPS) of the same maturity, is closely watched by market participants and policymakers alike. Breakeven inflation rates provide a signal about the expected path of inflation as perceived by market participants although they are also affected by risk and liquidity premia. In this post, we scrutinize the dynamics of breakeven inflation, highlighting some intriguing behavior which has persisted for a number of years and even through the pandemic. In particular, ...
Discussion Paper
How Uncertain Is the Estimated Probability of a Future Recession?
Since World War II, the U.S. economy has experienced twelve recessions—one every sixty-four months, on average. Though infrequent, these contractions can cause considerable pain and disruption, with the unemployment rate rising by at least 2.5 percentage points in each of the past four recessions. Given the consequences of an economic downturn, businesses and households are perennially interested in the near-term probability of a recession. In this post, we describe our research on a related issue: how much uncertainty is there around recession probability estimates from economic models?
Working Paper
Spurious Inference in Unidentified Asset-Pricing Models
This paper studies some seemingly anomalous results that arise in possibly misspecified and unidentified linear asset-pricing models estimated by maximum likelihood and one-step generalized method of moments (GMM). Strikingly, when useless factors (that is, factors that are independent of the returns on the test assets) are present, the models exhibit perfect fit, as measured by the squared correlation between the model's fitted expected returns and the average realized returns, and the tests for correct model specification have asymptotic power that is equal to the nominal size. In other ...
Working Paper
Minimum Distance Estimation of Dynamic Models with Errors-In-Variables
Empirical analysis often involves using inexact measures of desired predictors. The bias created by the correlation between the problematic regressors and the error term motivates the need for instrumental variables estimation. This paper considers a class of estimators that can be used when external instruments may not be available or are weak. The idea is to exploit the relation between the parameters of the model and the least squares biases. In cases when this mapping is not analytically tractable, a special algorithm is designed to simulate the latent predictors without completely ...