arXiv subjects

Antti J. Tanskanen

Publications and source records attributed to Antti J. Tanskanen.

3 recordsLinked to original sources

arXiv2025

A rich life cycle model of labor supply in Finland

A life cycle model of consumption and labor supply describes employment decisions of a collection of individuals during their lifetime. We develop a life cycle model describing a heterogeneous population operating in Finland under a wide variety of employment states and life situations. A rich life cycle model requires a large state space representing the possible states of simulated agents. The results demonstrate that the model reproduces a number of statistics of the Finnish employment market such as the age structures of employment rate and unemployment rate, distributions of observed effective marginal tax rates and participating tax rates, and proportion of part time work. As an application of analysis of a reform, we analyze how the program of Orpo government influences employment and public finances in Finland.

econ.GN↗

arXiv2022

Deep reinforced learning enables solving rich discrete-choice life cycle models to analyze social security reforms

Discrete-choice life cycle models of labor supply can be used to estimate how social security reforms influence employment rate. In a life cycle model, optimal employment choices during the life course of an individual must be solved. Mostly, life cycle models have been solved with dynamic programming, which is not feasible when the state space is large, as often is the case in a realistic life cycle model. Solving a complex life cycle model requires the use of approximate methods, such as reinforced learning algorithms. We compare how well a deep reinforced learning algorithm ACKTR and dynamic programming solve a relatively simple life cycle model. To analyze results, we use a selection of statistics and also compare the resulting optimal employment choices at various states. The statistics demonstrate that ACKTR yields almost as good results as dynamic programming. Qualitatively, dynamic programming yields more spiked aggregate employment profiles than ACKTR. The results obtained with ACKTR provide a good, yet not perfect, approximation to the results of dynamic programming. In addition to the baseline case, we analyze two social security reforms: (1) an increase of retirement age, and (2) universal basic income. Our results suggest that reinforced learning algorithms can be of significant value in developing social security reforms.

econ.GN↗

arXiv2018

Random selection of factors preserves the correlation structure in a linear factor model to a high degree

In a very high-dimensional vector space, two randomly-chosen vectors are almost orthogonal with high probability. Starting from this observation, we develop a statistical factor model, the random factor model, in which factors are chosen at random based on the random projection method. Randomness of factors has the consequence that covariance matrix is well preserved in a linear factor representation. It also enables derivation of probabilistic bounds for the accuracy of the random factor representation of time-series, their cross-correlations and covariances. As an application, we analyze reproduction of time-series and their cross-correlation coefficients in the well-diversified Russell 3,000 equity index.

q-fin.ST↗