The literature on effectual theory offers validated scales to measure effectual or causal logic in entrepreneurs' decision-making. However, there are no adequate scales to assess in advance the effectual or causal propensity of people with an entrepreneurial intention before the creation of their companies. We aim to determine the validity and reliability of an instrument to measure that propensity by first analysing those works that provide recognised validated scales with which to measure the effectual or causal logic in people who have already started up companies. Then, considering these scales, we designed a scale to evaluate the effectual or causal propensity in people who had not yet started up companies using a sample of 230 final-year business administration students to verify its reliability and validity. The validated scale has theoretical implications for the literature on potential entrepreneurship and entrepreneurial intention and practical implications for promoters of entrepreneurship who need to orient the behaviour of entrepreneurs, entrepreneurs of established businesses who want to implement a specific strategic orientation, entrepreneurs who want to evaluate the effectual propensity of their potential partners and workers, and academic institutions interested in orienting the entrepreneurial potential of their students.

This paper studies inference on treatment effects in panel data settings with unobserved confounding. We model outcome variables through a factor model with random factors and loadings. Such factors and loadings may act as unobserved confounders: when the treatment is implemented depends on time-varying factors, and who receives the treatment depends on unit-level confounders. We study the identification of treatment effects and illustrate the presence of a trade-off between time and unit-level confounding. We provide asymptotic results for inference for several Synthetic Control estimators and show that different sources of randomness should be considered for inference, depending on the nature of confounding. We conclude with a comparison of Synthetic Control estimators with alternatives for factor models.

Numerous studies use regression discontinuity design (RDD) for panel data by assuming that the treatment effects are homogeneous across all individuals/groups and pooling the data together. It is unclear how to test for the significance of treatment effects when the treatments vary across individuals/groups and the error terms may exhibit complicated dependence structures. This paper examines the estimation and inference of multiple treatment effects when the errors are not independent and identically distributed, and the treatment effects vary across individuals/groups. We derive a simple analytical expression for approximating the variance-covariance structure of the treatment effect estimators under general dependence conditions and propose two test statistics, one is to test for the overall significance of the treatment effect and the other for the homogeneity of the treatment effects. We find that in the Gaussian approximations to the test statistics, the dependence structures in the data can be safely ignored due to the localized nature of the statistics. This has the important implication that the simulated critical values can be easily obtained. Simulations demonstrate our tests have superb size control and reasonable power performance in finite samples regardless of the presence of strong cross-section dependence or/and weak serial dependence in the data. We apply our tests to two datasets and find significant overall treatment effects in each case.

A common approach to constructing a Synthetic Control unit is to fit on the outcome variable and covariates in pre-treatment time periods, but it has been shown by Ferman and Pinto (2021) that this approach does not provide asymptotic unbiasedness when the fit is imperfect and the number of controls is fixed. Many related panel methods have a similar limitation when the number of units is fixed. I introduce and evaluate a new method in which the Synthetic Control is constructed using a General Method of Moments approach where if the Synthetic Control satisfies the moment conditions it must have the same loadings on latent factors as the treated unit. I show that a Synthetic Control Estimator of this form will be asymptotically unbiased as the number of pre-treatment time periods goes to infinity, even when pre-treatment fit is imperfect and the set of controls is fixed. Furthermore, if both the number of pre-treatment and post-treatment time periods go to infinity, then averages of treatment effects can be consistently estimated and asymptotically valid inference can be conducted using a subsampling method. I conduct simulations and an empirical application to compare the performance of this method with existing approaches in the literature.

We introduce a new survey of professors at roughly 150 of the most research-intensive institutions of higher education in the US. We document seven new features of how research-active professors are compensated, how they spend their time, and how they perceive their research pursuits: (1) there is more inequality in earnings within fields than there is across fields; (2) institutions, ranks, tasks, and sources of earnings can account for roughly half of the total variation in earnings; (3) there is significant variation across fields in the correlations between earnings and different kinds of research output, but these account for a small amount of earnings variation; (4) measuring professors' productivity in terms of output-per-year versus output-per-research-hour can yield substantial differences; (5) professors' beliefs about the riskiness of their research are best predicted by their fundraising intensity, their risk-aversion in their personal lives, and the degree to which their research involves generating new hypotheses; (6) older and younger professors have very different research outputs and time allocations, but their intended audiences are quite similar; (7) personal risk-taking is highly predictive of professors' orientation towards applied, commercially-relevant research.

This paper proposes a new Bayesian machine learning model that can be applied to large datasets arising in macroeconomics. Our framework sums over many simple two-component location mixtures. The transition between components is determined by a logistic function that depends on a single threshold variable and two hyperparameters. Each of these individual models only accounts for a minor portion of the variation in the endogenous variables. But many of them are capable of capturing arbitrary nonlinear conditional mean relations. Conjugate priors enable fast and efficient inference. In simulations, we show that our approach produces accurate point and density forecasts. In a real-data exercise, we forecast US macroeconomic aggregates and consider the nonlinear effects of financial shocks in a large-scale nonlinear VAR.

We study equilibrium investment into bidding and latency reduction for different sequencing policies. For a batch auction design, we observe that bidders shade bids according to the likelihood that competing bidders land in the current batch. Moreover, in equilibrium, in the ex-ante investment stage before the auction, bidders invest into latency until they make zero profit in expectation. We compare the batch auction design to continuous time bidding policies (time boost) and observe that (depending on the choice of parameters) they obtain similar revenue and welfare guarantees.

This paper addresses a continuous-time contracting model that extends the problem introduced by Sannikov and later rigorously analysed by Possama\"{i} and Touzi. In our model, a principal hires a risk-averse agent to carry out a project. Specifically, the agent can perform two different tasks, namely to increase the instantaneous growth rate of the project's value, and to reduce the likelihood of accidents occurring. In order to compensate for these costly actions, the principal offers a continuous stream of payments throughout the entire duration of a contract, which concludes at a random time, potentially resulting in a lump-sum payment. We examine the consequences stemming from the introduction of accidents, modelled by a compound Poisson process that negatively impact the project's value. Furthermore, we investigate whether certain economic scenarii are still characterised by a golden parachute as in Sannikov's model. A golden parachute refers to a situation where the agent stops working and subsequently receives a compensation, which may be either a lump-sum payment leading to termination of the contract or a continuous stream of payments, thereby corresponding to a pension.

Dimensionality reduction has always been one of the most significant and challenging problems in the analysis of high-dimensional data. In the context of time series analysis, our focus is on the estimation and inference of conditional mean and variance functions. By using central mean and variance dimension reduction subspaces that preserve sufficient information about the response, one can effectively estimate the unknown mean and variance functions of the time series. While the literature presents several approaches to estimate the time series central mean and variance subspaces (TS-CMS and TS-CVS), these methods tend to be computationally intensive and infeasible for practical applications. By employing the Fourier transform, we derive explicit estimators for TS-CMS and TS-CVS. These proposed estimators are demonstrated to be consistent, asymptotically normal, and efficient. Simulation studies have been conducted to evaluate the performance of the proposed method. The results show that our method is significantly more accurate and computationally efficient than existing methods. Furthermore, the method has been applied to the Canadian Lynx dataset.