About me

I am Bart Keijsers, an Assistant Professor in Econometrics at the University of Amsterdam.

My primary research area is applied time series econometrics. My research interests include financial econometrics, state space modeling and Bayesian econometrics. I have applied these methods to credit risk, asset allocation and forecasting macroeconomic data. My work has been published at the Journal of Applied Econometrics.

Working Papers

Long-term investing under uncertain paramater instability (2018)

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The relationship between excess returns and the dividend-price ratio is known to be unstable. However, there is no consensus on the type of instability. We investigate the consequences of different types of break processes for a long-term investor. Differences in parameter instability affect the long-term investor in particular, as misspecification errors are exacerbated as the investment horizon increases. The break process is inferred with a mixture innovation model which is estimated using Bayesian methodology. The advantages of this approach are that we can estimate the frequency and size of breaks, and allow for separate break processes for the regression coefficients and the error variances. The estimated parameters show quite some instability, though less than if we were to assume a model with continuous breaks. We show that assuming constant parameters can lead to enormous losses for the long-term investor, even if in reality the break probability is small. The costs of ignoring uncertainty regarding the instability are smaller, but non-negligible. 

Uncertainty and the macroeconomy: An out-of-sample evaluation (2018)   |   with Dick van Dijk

Link coming soon


Many economic uncertainty measures have been proposed in last decade. They are all imperfect proxies of a latent entity and are supposed to be related to output variables. This paper identifies and classifies 15 monthly uncertainty measures based on how they measure economic uncertainty. We show that there is an underlying factor structure and identify two interpretable factors. First, a general economic uncertainty factor with a slight tilt toward financial conditions. Second, a consumer/media confidence index which remains elevated after recessions. Furthermore, we conduct a real-time out-of-sample forecasting analysis on the mean and quantiles of the four coincident variables (industrial production, employment, personal income, and manufacturing and trade sales) to assess whether the relationship with output variables holds out-of-sample. The uncertainty factors hold little predictive power for the mean, but are informative when forecasting the lower quantiles — the left tail — of industrial production and in particular employment. This suggests a nonlinear relationship between economic uncertainty and output.


Cyclicality in losses on bank loans (2018) Journal of Applied Econometrics, 33(4), 533-552   |   with Bart Diris and Erik Kole

Link to SSRN


Based on unique data we show that macro variables, the default rate and loss given default of bank loans share common cyclical components. The innovation in our model is the distinction between loans with either severe or mild losses. The variation in the proportion of these two types drives the cyclic behavior of the loss given default, and constitutes the links with the default rate and macro variables. These links vary according to loan and borrower characteristics. During downturns, the proportion of defaults with severe losses increases, but the distribution of losses conditional on their being mild or severe does not change. Though loans are monitored more closely than bonds and are more senior, the cyclical variation in their losses resembles those for bonds, albeit around a lower average level. This variation leads to an increase in the capital reserves required for loan portfolios.

Winner of research grant at Europlace Institute for Finance



PhD in Econometrics
2019 | Erasmus University Rotterdam

MSc in Econometrics
2012 | Erasmus University Rotterdam

Research Interests

  • Applied time series econometrics
  • Financial econometrics
  • Forecasting
  • Bayesian econometrics


UvA | Roetersstraat 11, Amsterdam
Room number E4.27