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Working Paper
Reconsidering the Fed’s Forecasting Advantage
Previous studies show the Fed has a forecast advantage over the private sector, either because it devotes more resources to forecasting or because it has an informational advantage in knowing the path of future monetary policy. We evaluate the Fed’s forecast advantage to determine how much of it results from the Fed’s knowledge of the conditioning path. We develop two tests—an instrumental variable encompassing test and a path-dependent encompassing test—to equalize the Fed’s information set with the private sector’s. We find that, generally, the Fed does not encompass the private ...
Working Paper
Lags, Leave-Outs and Fixed Effects
To avoid endogeneity, financial economists often construct regressors and/or instruments using values from other observations, with lagged and leave-out variables being common examples. We examine the use of such variables in common settings with fixed effects and show that it can induce bias and distort inference. We illustrate the severity of this problem via simulations and with patent examiner data. Even when scrambling the patent examiners, thus removing any instrument validity, the bias leads to a first-stage F-statistic over 1,000. General and case-specific solutions are provided.
Working Paper
A Robust Test for Weak Instruments with Multiple Endogenous Regressors
We extend the popular bias-based test of Stock and Yogo (2005) for instrument strength in linear instrumental variables regressions with multiple endogenous regressors to be robust to heteroskedasticity and autocorrelation. Equivalently, we extend the robust test of Montiel Olea and Pflueger (2013) for one endogenous regressor to the general case with multiple endogenous regressors. We describe a simple procedure for applied researchers to conduct our generalized first-stage test of instrument strength and provide efficient and easy-to-use Matlab code for its implementation. We demonstrate ...
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A Robust Test for Weak Instruments with Multiple Endogenous Regressors
We extend the popular bias-based test of Stock and Yogo (2005) for instrument strength in linear instrumental variables regressions with multiple endogenous regressors to be robust to heteroskedasticity and autocorrelation. Equivalently, we extend the robust test of Montiel Olea and Pflueger (2013) for one endogenous regressor to the general case with multiple endogenous regressors. We describe a simple procedure for applied researchers to conduct our generalized first-stage test of instrument strength and provide efficient and easy-to-use Matlab code for its implementation. We demonstrate ...
Working Paper
Weak Instrument Bias in Impulse Response Estimators
We approximate the finite-sample distribution of impulse response function (IRF) estimators that are just-identified with a weak instrument using the conventional local-to-zero asymptotic framework. Since the distribution lacks a mean, we assess bias using the mode and conclude that researchers prioritizing robustness against weak instrument bias should favor vector autoregressions (VARs) over local projections (LPs). Existing testing procedures are ill-suited for assessing weak instrument bias in IRF estimates, and we propose a novel simple test based on the usual first-stage F-statistic. We ...
Working Paper
Dynamic Identification Using System Projections on Instrumental Variables
We propose System Projections on Instrumental Variables (SP-IV) to estimate structural relationships using regressions of structural impulse responses obtained from local projections or vector autoregressions. Relative to IV with distributed lags of shocks as instruments, SP-IV imposes weaker exogeneity requirements and can improve efficiency and increase effective instrument strength relative to the typical 2SLS estimator. We describe inference under strong and weak identification. The SP-IV estimator outperforms other estimators of Phillips Curve parameters in simulations. We estimate the ...
Working Paper
A Robust Test for Weak Instruments with Multiple Endogenous Regressors
We generalize the popular bias-based test of Stock and Yogo (2005) for instrument strength in two-stage least-squares models with multiple endogenous regressors to be robust to heteroskedasticity and autocorrelation. Equivalently, we extend the robust test of Montiel Olea and Pflueger (2013) for a single endogenous regressor to the general case with multiple endogenous regressors. We describe a simple procedure for applied researchers to conduct our generalized first-stage test of instrument strength, and provide fast Matlab code for its implementation. In simulations, our test controls size ...
Working Paper
In-migration and Dilution of Community Social Capital
Consistent with predictions from the literature, we find that higher levels of in-migration dilute multiple dimensions of a community's level of social capital. The analysis employs a 2SLS methodology to account for potential endogeneity of migration.
Working Paper
Understanding the Estimation of Oil Demand and Oil Supply Elasticities
This paper examines the advantages and drawbacks of alternative methods of estimating oil supply and oil demand elasticities and of incorporating this information into structural VAR models. I not only summarize the state of the literature, but also draw attention to a number of econometric problems that have been overlooked in this literature. Once these problems are recognized, seemingly conflicting conclusions in the recent literature can be resolved. My analysis reaffirms the conclusion that the one-month oil supply elasticity is close to zero, which implies that oil demand shocks are the ...
Working Paper
Local Projections, Autocorrelation, and Efficiency
It is well known that Local Projections (LP) residuals are autocorrelated. Conventional wisdom says that LP have to be estimated by OLS with Newey-West (or some type of Heteroskedastic and Autocorrelation Consistent (HAC)) standard errors and that GLS is not possible because the autocorrelation process is unknown and/or because the GLS estimator would be inconsistent. I derive the autocorrelation process of LP and show that it can be corrected for using a consistent GLS estimator. Estimating LP with GLS has three major implications: 1) LP GLS can be less biased, more efficient, and generally ...