This study re-examines the effects of monetary policy using Japanese data and a new approach, the shift-share regressor and instrumental variables, which has not been used in previous vector autoregression analyses. We find that consumption and investment respond negatively to interest rates,...

A correlation between regressors and disturbances presents challenging problems in linear regression. Issues like omitted variables, measurement error and simultaneity render ordinary least squares (OLS) biased and inconsistent. In the context of heteroscedastic linear regression, this note...

Ignoring consideration sets in modeling customer purchase decisions may lead to biased estimation of customer preferences, yet consideration sets are difficult to infer in brick-and-mortar contexts. We show that the challenge of estimating consideration set models in brick-and-mortar contexts...

We consider instrumental variables estimation of a possibly infinite order dynamic panel autoregressive (AR) process with individual effects. The estimation is based on the sieve AR approximation with its lag order increasing with sample size. Transforming the variable to eliminate individual...

In this study, we investigate a sequential procedure for the early detection of parameter changes in conditionally heteroscedastic time series models. We introduce the detectors based on the cumulative sum of score vectors and residuals for this procedure. The asymptotic properties of the...

In this paper, we investigate the calibration of the smoothing parameter in an exponentially weighted moving average (EWMA) for realized covariance matrices. Although it is the crucial determinant in steering the dynamics of the EWMA, little attention is drawn on its calibration in many...

We investigate how a cautious decision maker might aggregate various opinions of experts who face model misspecification. We first consider a more general case in which both the decision maker’s and experts’ preference relations are variational introduced by Maccheroni et al. (2006). We show...

One lingering challenge of randomized controlled trials (or A/B tests) is to estimate the effects of long-term treatments at an early stage of the experiment. Learning such effects is crucial for management, as product updates (e.g., new UIs or algorithms) are intended to remain in the system...

Methodologies such as situational importance and Shapley Additive explanations (SHAP) are increasingly used to explain machine learning (black-box) models predictions. Based on parametric inference, we develop a methodological framework that uses Shapley values to generalize the data-generating...

This paper estimates the distribution-led regime of the US economy for the period 1947–2019. We use a time varying parameter model, which allows for continuous changes in the regime over time. To the best of our knowledge this is the first paper that has attempted to do this. This innovation...