Ted Juhl

Time in purgatory: Examining the grant lag for U.S. patent applications

Abstract As patent applications increase, and the range of patentable technologies increases, the length of time it takes for an invention to go through the examination process at the U.S. Patent Office has increased. Concerns over the distributional effects of these changes have been expressed during policy debates. We use data on U.S. patent applications and grants to ask who is affected by longer grant lags. We augment this analysis with interviews of patent examiners, leading to a better understanding of the examination process. Our analysis finds that differences across technology are most important. These differences do not erode over time, suggesting that learning effects alone will not reduce grant lags. Inventor characteristics have statistically significant effects, but the magnitudes are small.

Covered Interest Arbitrage: Then versus Now

We introduce a new weekly database of spot and forward US-UK exchange rates as well as interest rates to examine the integration of forward exchange markets during the classical gold standard period (1880-1914). Using threshold autoregressions (TAR), we estimate the transactions cost band of covered interest differentials (CIDs) and compare our results to studies of more recent periods. Our findings indicate that CIDs for the US-UK rate were generally larger during the classical gold standard than any period since. We argue that slower information and communications technology during the gold standard period led to fewer short-term financial flows, higher transactions costs, and larger CIDs.

Partially Linear Models with Unit Roots

This paper studies the asymptotic properties of a nonstationary partially linear regression model. In particular, we allow for covariates to enter the unit root (or near unit root) model in a nonparametric fashion, so that our model is an extension of the semiparametric model analyzed in Robinson (1988). It is proven that the autoregressive parameter can be estimated at rate N even though part of the model is estimated nonparametrically. Unit root tests based on the semiparametric estimate of the autoregressive parameter have a limiting distribution which is a mixture of a standard normal and the Dickey-Fuller distribution. A Monte Carlo experiment is conducted to evaluate the performance of the tests for various linear and nonlinear specifications.

POWER FUNCTIONS AND ENVELOPES FOR UNIT ROOT TESTS

This paper studies power functions and envelopes for covariate augmented unit root tests. The power functions are calculated by integrating the characteristic function, allowing accurate evaluation of the power envelope and the power functions. Using the power functions, we study the selection among point optimal invariant unit root tests. An “optimal†point optimal test is proposed based on minimizing the integrated power difference. We find that when there are covariate effects, optimal tests use a local alternative where the power envelope has an approximate value of 0.75.We thank Pentti Saikkonen and two referees for helpful comments.

A Test for Slope Heterogeneity in Fixed Effects Models

Typical panel data models make use of the assumption that the regression parameters are the same for each individual cross-sectional unit. We propose tests for slope heterogeneity in panel data models. Our tests are based on the conditional Gaussian likelihood function in order to avoid the incidental parameters problem induced by the inclusion of individual fixed effects for each cross-sectional unit. We derive the Conditional Lagrange Multiplier test that is valid in cases where N → ∞ and T is fixed. The test applies to both balanced and unbalanced panels. We expand the test to account for general heteroskedasticity where each cross-sectional unit has its own form of heteroskedasticity. The modification is possible if T is large enough to estimate regression coefficients for each cross-sectional unit by using the MINQUE unbiased estimator for regression variances under heteroskedasticity. All versions of the test have a standard Normal distribution under general assumptions on the error distribution as N → ∞. A Monte Carlo experiment shows that the test has very good size properties under all specifications considered, including heteroskedastic errors. In addition, power of our test is very good relative to existing tests, particularly when T is not large.

Nonparametric Tests of Moment Condition Stability

This paper considers testing for moment condition instability for a wide variety of models that arise in econometric applications. We propose a nonparametric test based on smoothing the moment conditions over time. The resulting test takes the form of a U-statistic and has a limiting normal distribution. The proposed test statistic is not affected by changes in the distribution of the data, so long as certain simple regularity conditions hold. We examine the performance of the test through a small Monte Carlo experiment.

A Nonparametric Test of the Predictive Regression Model

This article considers testing the significance of a regressor with a near unit root in a predictive regression model. The procedures discussed in this article are nonparametric, so one can test the significance of a regressor without specifying a functional form. The results are used to test the null hypothesis that the entire function takes the value of zero. We show that the standardized test has a normal distribution regardless of whether there is a near unit root in the regressor. This is in contrast to tests based on linear regression for this model where tests have a nonstandard limiting distribution that depends on nuisance parameters. Our results have practical implications in testing the significance of a regressor since there is no need to conduct pretests for a unit root in the regressor and the same procedure can be used if the regressor has a unit root or not. A Monte Carlo experiment explores the performance of the test for various levels of persistence of the regressors and for various linear and nonlinear alternatives. The test has superior performance against certain nonlinear alternatives. An application of the test applied to stock returns shows how the test can improve inference about predictability.

Policy Heterogeneity in Empirical Corporate Finance

Standard econometric methods can overlook the issue of heterogeneity in corporate policy making, generating biased estimates. We propose ways to identify and address the firm policy heterogeneity bias in practice. In doing so, we introduce a new test determining whether standard firm-fixed effects estimations are subject to heterogeneity biases in corporate applications. Examining investment models to showcase our approach, we show that heterogeneity bias-robust methods identify cash flow as a more important driver of investment than previously reported. Our study demonstrates analytically, via simulations, and empirically the importance of carefully accounting for firm heterogeneity in drawing conclusions about corporate policy.

Testing for Slope Heterogeneity Bias in Panel Data Models

ABSTRACT Standard econometric methods can overlook individual heterogeneity in empirical work, generating inconsistent parameter estimates in panel data models. We propose the use of methods that allow researchers to easily identify, quantify, and address estimation issues arising from individual slope heterogeneity. We first characterize the bias in the standard fixed effects estimator when the true econometric model allows for heterogeneous slope coefficients. We then introduce a new test to check whether the fixed effects estimation is subject to heterogeneity bias. The procedure tests the population moment conditions required for fixed effects to consistently estimate the relevant parameters in the model. We establish the limiting distribution of the test and show that it is very simple to implement in practice. Examining firm investment models to showcase our approach, we show that heterogeneity bias-robust methods identify cash flow as a more important driver of investment than previously reported. Our study demonstrates analytically, via simulations, and empirically the importance of carefully accounting for individual specific slope heterogeneity in drawing conclusions about economic behavior.

Cointegration analysis using M estimators

Abstract Tests for cointegration are developed using multivariate M estimators. The tests are based on analyzing the singular values of the parameter estimates standardized by the covariance matrix and do not require a reduced rank estimator.