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Employee Profile

Daniel Kim

Assistant Professor - Department of Finance

Biography

I am an Assistant Professor of Finance at BI Norwegian Business School.

I graduated with a Ph.D. in Finance from The Wharton School at the University of Pennsylvania. My research interests are in topics related to empirical corporate finance, public finance and applied econometrics.

Please see my homepage for further information.

Publications

Elkahmi, Redouane; Kim, Daniel, Jo, Chanik & Salerno, Marco (2022)

Agency Conflicts and Investment: Evidence from a Structural Estimation

The Review of Corporate Finance Studies Doi: 10.1093/rcfs/cfac019

Chalak, Karim; Kim, Daniel, Miller, Megan & Pepper, John (2022)

Reexamining the evidence on gun ownership and homicide using proxy measures of ownership

Journal of Public Economics Doi: 10.1016/j.jpubeco.2022.104621

Chalak, Karim & Kim, Daniel (2019)

Measurement error in multiple equations: Tobin’s q and corporate investment, saving, and debt

Journal of Econometrics Doi: 10.1016/j.jeconom.2019.08.001 - Full text in research archive

Chalak, Karim & Kim, Daniel (2019)

Measurement Error Without the Proxy Exclusion Restriction

Journal of business & economic statistics Doi: 10.1080/07350015.2019.1617156 - Full text in research archive

This article studies the identification of the coefficients in a linear equation when data on the outcome, covariates, and an error-laden proxy for a latent variable are available. We maintain that the measurement error in the proxy is classical and relax the assumption that the proxy is excluded from the outcome equation. This enables the proxy to directly affect the outcome and allows for differential measurement error. Without the proxy exclusion restriction, we first show that the effects of the latent variable, the proxy, and the covariates are not identified. We then derive the sharp identification regions for these effects under any configuration of three auxiliary assumptions. The first weakens the assumption of no measurement error by imposing an upper bound on the noise-to-signal ratio. The second imposes an upper bound on the outcome equation coefficient of determination that would obtain had there been no measurement error. The third weakens the proxy exclusion restriction by specifying whether the latent variable and its proxy affect the outcome in the same or the opposite direction, if at all. Using the College Scorecard aggregate data, we illustrate our framework by studying the financial returns to college selectivity and characteristics and student characteristics when the average SAT score at an institution may directly affect earnings and serves as a proxy for the average ability of the student cohort.

Academic Degrees
Year Academic Department Degree
2019 The Wharton School, University of Pennsylvania Ph.D.
Work Experience
Year Employer Job Title
2019 - Present BI Norwegian Business School Assistant professor