Economics

Discrete choice models (DCMs) and neural networks (NNs) can complement each other. We propose a neural network embedded choice model - TasteNet-MNL, to improve the flexibility in modeling taste heterogeneity while keeping model interpretability. The hybrid model consists of a TasteNet module: a feed-forward neural network that learns taste parameters as flexible functions of individual characteristics; and a choice module: a multinomial logit model (MNL) with manually specified utility. TasteNet and MNL are fully integrated and jointly estimated. By embedding a neural network into a DCM, we exploit a neural network's function approximation capacity to reduce specification bias. Through special structure and parameter constraints, we incorporate expert knowledge to regularize the neural network and maintain interpretability. On synthetic data, we show that TasteNet-MNL can recover the underlying non-linear utility function, and provide predictions and interpretations as accurate as the true model; while examples of logit or random coefficient logit models with misspecified utility functions result in large parameter bias and low predictability. In the case study of Swissmetro mode choice, TasteNet-MNL outperforms benchmarking MNLs' predictability; and discovers a wider spectrum of taste variations within the population, and higher values of time on average. This study takes an initial step towards developing a framework to combine theory-based and data-driven approaches for discrete choice modeling.

We introduce a novel parametrization of the correlation matrix. The reparametrization facilitates modeling of correlation and covariance matrices by an unrestricted vector, where positive definiteness is an innate property. This parametrization can be viewed as a generalization of Fisther's Z-transformation to higher dimensions and has a wide range of potential applications. An algorithm for reconstructing the unique n x n correlation matrix from any d-dimensional vector (with d = n(n-1)/2) is provided, and we derive its numerical complexity.

We introduce a new approach for comparing the predictive accuracy of two nested models that bypasses the difficulties caused by the degeneracy of the asymptotic variance of forecast error loss differentials used in the construction of commonly used predictive comparison statistics. Our approach continues to rely on the out of sample MSE loss differentials between the two competing models, leads to nuisance parameter free Gaussian asymptotics and is shown to remain valid under flexible assumptions that can accommodate heteroskedasticity and the presence of mixed predictors (e.g. stationary and local to unit root). A local power analysis also establishes its ability to detect departures from the null in both stationary and persistent settings. Simulations calibrated to common economic and financial applications indicate that our methods have strong power with good size control across commonly encountered sample sizes.

A novel approach to price indices, leading to an innovative solution in both a multi-period or a multilateral framework, is presented. The index turns out to be the generalized least squares solution of a regression model linking values and quantities of the commodities. The index reference basket, which is the union of the intersections of the baskets of all country/period taken in pair, has a coverage broader than extant indices. The properties of the index are investigated and updating formulas established. Applications to both real and simulated data provide evidence of the better index performance in comparison with extant alternatives.

In this study, we consider a pairwise network formation model in which each dyad of agents strategically determines the link status between them. Our model allows the agents to have unobserved group heterogeneity in the propensity of link formation. For the model estimation, we propose a three-step maximum likelihood (ML) method. First, we obtain consistent estimates for the heterogeneity parameters at individual level using the ML estimator. Second, we estimate the latent group structure using the binary segmentation algorithm based on the results obtained from the first step. Finally, based on the estimated group membership, we re-execute the ML estimation. Under certain regularity conditions, we show that the proposed estimator is asymptotically unbiased and distributed as normal at the parametric rate. As an empirical illustration, we focus on the network data of international visa-free travels. The results indicate the presence of significant strategic complementarity and a certain level of degree heterogeneity in the network formation behavior.

Conventional financial models fail to explain the economic and monetary properties of cryptocurrencies due to the latter's dual nature: their usage as financial assets on the one side and their tight connection to the underlying blockchain structure on the other. In an effort to examine both components via a unified approach, we apply a recently developed Non-Homogeneous Hidden Markov (NHHM) model with an extended set of financial and blockchain specific covariates on the Bitcoin (BTC) and Ether (ETH) price data. Based on the observable series, the NHHM model offers a novel perspective on the underlying microstructure of the cryptocurrency market and provides insight on unobservable parameters such as the behavior of investors, traders and miners. The algorithm identifies two alternating periods (hidden states) of inherently different activity -- fundamental versus uninformed or noise traders -- in the Bitcoin ecosystem and unveils differences in both the short/long run dynamics and in the financial characteristics of the two states, such as significant explanatory variables, extreme events and varying series autocorrelation. In a somewhat unexpected result, the Bitcoin and Ether markets are found to be influenced by markedly distinct indicators despite their perceived correlation. The current approach backs earlier findings that cryptocurrencies are unlike any conventional financial asset and makes a first step towards understanding cryptocurrency markets via a more comprehensive lens.

We introduce tools for controlled variable selection to economists. In particular, we apply a recently introduced aggregation scheme for false discovery rate (FDR) control to German administrative data to determine the parts of the individual employment histories that are relevant for the career outcomes of women. Our results suggest that career outcomes can be predicted based on a small set of variables, such as daily earnings, wage increases in combination with a high level of education, employment status, and working experience.

We introduce a route choice model that incorporates the notion of choice aversion in transportation networks. Formally, we propose a recursive logit model which incorporates a penalty term that accounts for the dimension of the choice set at each node of the network. We make three contributions. First, we show that our model overcomes the correlation problem between routes, a common pitfall of traditional logit models. In particular, our approach can be seen as an alternative to the class of models known as Path Size Logit (PSL). Second, we show how our model can generate violations of regularity in the path choice probabilities. In particular, we show that removing edges in the network can decrease the probability of some existing paths. Finally, we show that under the presence of choice aversion, adding edges to the network can make users worse off. In other words, a type of Braess's paradox can emerge even in the case of uncongested networks. We show that these phenomena can be characterized in terms of a parameter that measures users' degree of choice aversion.

A multivariate score-driven model is developed to extract signals from noisy vector processes. By assuming that the conditional location vector from a multivariate Student's \emph{t} distribution changes over time, we construct a robust filter which is able to overcome several issues that naturally arise when modeling heavy-tailed phenomena and, more in general, vectors of dependent non-Gaussian time series. We derive conditions for stationarity and invertibility and estimate the unknown parameters by maximum likelihood (ML). Strong consistency and asymptotic normality of the estimator are proved and the finite sample properties are illustrated by a Monte-Carlo study. From a computational point of view, analytical formulae are derived, which consent to develop estimation procedures based on the Fisher scoring method. The theory is supported by a novel empirical illustration that shows how the model can be effectively applied to estimate consumer prices from home scanner data.

In this paper we propose a semi-parametric Bayesian Generalized Least Squares estimator. In a generic GLS setting where each error is a vector, parametric GLS maintains the assumption that each error vector has the same covariance matrix. In reality however, the observations are likely to be heterogeneous regarding their distributions. To cope with such heterogeneity, a Dirichlet process prior is introduced for the covariance matrices of the errors, leading to the error distribution being a mixture of a variable number of normal distributions. Our methods let the number of normal components be data driven. Two specific cases are then presented: the semi-parametric Bayesian Seemingly Unrelated Regression (SUR) for equation systems; as well as the Random Effects Model (REM) and Correlated Random Effects Model (CREM) for panel data. A series of simulation experiments is designed to explore the performance of our methods. The results demonstrate that our methods obtain smaller posterior standard deviations than the parametric Bayesian GLS. We then apply our semi-parametric Bayesian SUR and REM/CREM methods to empirical examples.