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arXiv2018

USDA Forecasts: A meta-analysis study

The primary goal of this study is doing a meta-analysis research on two groups of published studies. First, the ones that focus on the evaluation of the United States Department of Agriculture (USDA) forecasts and second, the ones that evaluate the market reactions to the USDA forecasts. We investigate four questions. 1) How the studies evaluate the accuracy of the USDA forecasts? 2) How they evaluate the market reactions to the USDA forecasts? 3) Is there any heterogeneity in the results of the mentioned studies? 4) Is there any publication bias? About the first question, while some researchers argue that the forecasts are unbiased, most of them maintain that they are biased, inefficient, not optimal, or not rational. About the second question, while a few studies claim that the forecasts are not newsworthy, most of them maintain that they are newsworthy, provide useful information, and cause market reactions. About the third and the fourth questions, based on our findings, there are some clues that the results of the studies are heterogeneous, but we didn't find enough evidences of publication bias.

econ.EM↗
arXiv2018

Capital Structure in U.S., a Quantile Regression Approach with Macroeconomic Impacts

The major perspective of this paper is to provide more evidence into the empirical determinants of capital structure adjustment in different macroeconomics states by focusing and discussing the relative importance of firm-specific and macroeconomic characteristics from an alternative scope in U.S. This study extends the empirical research on the topic of capital structure by focusing on a quantile regression method to investigate the behavior of firm-specific characteristics and macroeconomic variables across all quantiles of distribution of leverage (total debt, long-terms debt and short-terms debt). Thus, based on a partial adjustment model, we find that long-term and short-term debt ratios varying regarding their partial adjustment speeds; the short-term debt raises up while the long-term debt ratio slows down for same periods.

econ.GN↗
arXiv2018

Testing the Number of Regimes in Markov Regime Switching Models

Markov regime switching models have been used in numerous empirical studies in economics and finance. However, the asymptotic distribution of the likelihood ratio test statistic for testing the number of regimes in Markov regime switching models has been an unresolved problem. This paper derives the asymptotic distribution of the likelihood ratio test statistic for testing the null hypothesis of $M_0$ regimes against the alternative hypothesis of $M_0 + 1$ regimes for any $M_0 \geq 1$ both under the null hypothesis and under local alternatives. We show that the contiguous alternatives converge to the null hypothesis at a rate of $n^{-1/8}$ in regime switching models with normal density. The asymptotic validity of the parametric bootstrap is also established.

econ.EM↗
arXiv2018

Evolution of Regional Innovation with Spatial Knowledge Spillovers: Convergence or Divergence?

This paper extends endogenous economic growth models to incorporate knowledge externality. We explores whether spatial knowledge spillovers among regions exist, whether spatial knowledge spillovers promote regional innovative activities, and whether external knowledge spillovers affect the evolution of regional innovations in the long run. We empirically verify the theoretical results through applying spatial statistics and econometric model in the analysis of panel data of 31 regions in China. An accurate estimate of the range of knowledge spillovers is achieved and the convergence of regional knowledge growth rate is found, with clear evidences that developing regions benefit more from external knowledge spillovers than developed regions.

econ.EM↗
arXiv2018

Accurate Evaluation of Asset Pricing Under Uncertainty and Ambiguity of Information

Since exchange economy considerably varies in the market assets, asset prices have become an attractive research area for investigating and modeling ambiguous and uncertain information in today markets. This paper proposes a new generative uncertainty mechanism based on the Bayesian Inference and Correntropy (BIC) technique for accurately evaluating asset pricing in markets. This technique examines the potential processes of risk, ambiguity, and variations of market information in a controllable manner. We apply the new BIC technique to a consumption asset-pricing model in which the consumption variations are modeled using the Bayesian network model with observing the dynamics of asset pricing phenomena in the data. These dynamics include the procyclical deviations of price, the countercyclical deviations of equity premia and equity volatility, the leverage impact and the mean reversion of excess returns. The key findings reveal that the precise modeling of asset information can estimate price changes in the market effectively.

q-fin.GN↗
arXiv2018

Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data

This paper analyzes consumer choices over lunchtime restaurants using data from a sample of several thousand anonymous mobile phone users in the San Francisco Bay Area. The data is used to identify users' approximate typical morning location, as well as their choices of lunchtime restaurants. We build a model where restaurants have latent characteristics (whose distribution may depend on restaurant observables, such as star ratings, food category, and price range), each user has preferences for these latent characteristics, and these preferences are heterogeneous across users. Similarly, each item has latent characteristics that describe users' willingness to travel to the restaurant, and each user has individual-specific preferences for those latent characteristics. Thus, both users' willingness to travel and their base utility for each restaurant vary across user-restaurant pairs. We use a Bayesian approach to estimation. To make the estimation computationally feasible, we rely on variational inference to approximate the posterior distribution, as well as stochastic gradient descent as a computational approach. Our model performs better than more standard competing models such as multinomial logit and nested logit models, in part due to the personalization of the estimates. We analyze how consumers re-allocate their demand after a restaurant closes to nearby restaurants versus more distant restaurants with similar characteristics, and we compare our predictions to actual outcomes. Finally, we show how the model can be used to analyze counterfactual questions such as what type of restaurant would attract the most consumers in a given location.

econ.EM↗
arXiv2018

Quantifying Health Shocks Over the Life Cycle

We first show (1) the importance of investigating health expenditure process using the order two Markov chain model, rather than the standard order one model, which is widely used in the literature. Markov chain of order two is the minimal framework that is capable of distinguishing those who experience a certain health expenditure level for the first time from those who have been experiencing that or other levels for some time. In addition, using the model we show (2) that the probability of encountering a health shock first de- creases until around age 10, and then increases with age, particularly, after age 40, (3) that health shock distributions among different age groups do not differ until their percentiles reach the median range, but that above the median the health shock distributions of older age groups gradually start to first-order dominate those of younger groups, and (4) that the persistency of health shocks also shows a U-shape in relation to age.

econ.EM↗
arXiv2018

Ordered Kripke Model, Permissibility, and Convergence of Probabilistic Kripke Model

We define a modification of the standard Kripke model, called the ordered Kripke model, by introducing a linear order on the set of accessible states of each state. We first show this model can be used to describe the lexicographic belief hierarchy in epistemic game theory, and perfect rationalizability can be characterized within this model. Then we show that each ordered Kripke model is the limit of a sequence of standard probabilistic Kripke models with a modified (common) belief operator, in the senses of structure and the (epsilon-)permissibilities characterized within them.

econ.EM↗
arXiv2020

Nonseparable Sample Selection Models with Censored Selection Rules

We consider identification and estimation of nonseparable sample selection models with censored selection rules. We employ a control function approach and discuss different objects of interest based on (1) local effects conditional on the control function, and (2) global effects obtained from integration over ranges of values of the control function. We derive the conditions for the identification of these different objects and suggest strategies for estimation. Moreover, we provide the associated asymptotic theory. These strategies are illustrated in an empirical investigation of the determinants of female wages in the United Kingdom.

econ.EM↗
arXiv2018

Are `Water Smart Landscapes' Contagious? An epidemic approach on networks to study peer effects

We test the existence of a neighborhood based peer effect around participation in an incentive based conservation program called `Water Smart Landscapes' (WSL) in the city of Las Vegas, Nevada. We use 15 years of geo-coded daily records of WSL program applications and approvals compiled by the Southern Nevada Water Authority and Clark County Tax Assessors rolls for home characteristics. We use this data to test whether a spatially mediated peer effect can be observed in WSL participation likelihood at the household level. We show that epidemic spreading models provide more flexibility in modeling assumptions, and also provide one mechanism for addressing problems associated with correlated unobservables than hazards models which can also be applied to address the same questions. We build networks of neighborhood based peers for 16 randomly selected neighborhoods in Las Vegas and test for the existence of a peer based influence on WSL participation by using a Susceptible-Exposed-Infected-Recovered epidemic spreading model (SEIR), in which a home can become infected via autoinfection or through contagion from its infected neighbors. We show that this type of epidemic model can be directly recast to an additive-multiplicative hazard model, but not to purely multiplicative one. Using both inference and prediction approaches we find evidence of peer effects in several Las Vegas neighborhoods.

econ.EM↗
arXiv2018

How Can We Induce More Women to Competitions?

Why women avoid participating in a competition and how can we encourage them to participate in it? In this paper, we investigate how social image concerns affect women's decision to compete. We first construct a theoretical model and show that participating in a competition, even under affirmative action policies favoring women, is costly for women under public observability since it deviates from traditional female gender norms, resulting in women's low appearance in competitive environments. We propose and theoretically show that introducing prosocial incentives in the competitive environment is effective and robust to public observability since (i) it induces women who are intrinsically motivated by prosocial incentives to the competitive environment and (ii) it makes participating in a competition not costly for women from social image point of view. We conduct a laboratory experiment where we randomly manipulate the public observability of decisions to compete and test our theoretical predictions. The results of the experiment are fairly consistent with our theoretical predictions. We suggest that when designing policies to promote gender equality in competitive environments, using prosocial incentives through company philanthropy or other social responsibility policies, either as substitutes or as complements to traditional affirmative action policies, could be promising.

econ.EM↗
arXiv2018

Hyper-rational choice theory

The rational choice theory is based on this idea that people rationally pursue goals for increasing their personal interests. In most conditions, the behavior of an actor is not independent of the person and others' behavior. Here, we present a new concept of rational choice as a hyper-rational choice which in this concept, the actor thinks about profit or loss of other actors in addition to his personal profit or loss and then will choose an action which is desirable to him. We implement the hyper-rational choice to generalize and expand the game theory. Results of this study will help to model the behavior of people considering environmental conditions, the kind of behavior interactive, valuation system of itself and others and system of beliefs and internal values of societies. Hyper-rationality helps us understand how human decision makers behave in interactive decisions.

econ.EM↗
arXiv2018

Structural analysis with mixed-frequency data: A MIDAS-SVAR model of US capital flows

We develop a new VAR model for structural analysis with mixed-frequency data. The MIDAS-SVAR model allows to identify structural dynamic links exploiting the information contained in variables sampled at different frequencies. It also provides a general framework to test homogeneous frequency-based representations versus mixed-frequency data models. A set of Monte Carlo experiments suggests that the test performs well both in terms of size and power. The MIDAS-SVAR is then used to study how monetary policy and financial market volatility impact on the dynamics of gross capital inflows to the US. While no relation is found when using standard quarterly data, exploiting the variability present in the series within the quarter shows that the effect of an interest rate shock is greater the longer the time lag between the month of the shock and the end of the quarter

econ.EM↗
arXiv2018

Random taste heterogeneity in discrete choice models: Flexible nonparametric finite mixture distributions

This study proposes a mixed logit model with multivariate nonparametric finite mixture distributions. The support of the distribution is specified as a high-dimensional grid over the coefficient space, with equal or unequal intervals between successive points along the same dimension; the location of each point on the grid and the probability mass at that point are model parameters that need to be estimated. The framework does not require the analyst to specify the shape of the distribution prior to model estimation, but can approximate any multivariate probability distribution function to any arbitrary degree of accuracy. The grid with unequal intervals, in particular, offers greater flexibility than existing multivariate nonparametric specifications, while requiring the estimation of a small number of additional parameters. An expectation maximization algorithm is developed for the estimation of these models. Multiple synthetic datasets and a case study on travel mode choice behavior are used to demonstrate the value of the model framework and estimation algorithm. Compared to extant models that incorporate random taste heterogeneity through continuous mixture distributions, the proposed model provides better out-of-sample predictive ability. Findings reveal significant differences in willingness to pay measures between the proposed model and extant specifications. The case study further demonstrates the ability of the proposed model to endogenously recover patterns of attribute non-attendance and choice set formation.

econ.EM↗
arXiv2018

Prediction of Shared Bicycle Demand with Wavelet Thresholding

Consumers are creatures of habit, often periodic, tied to work, shopping and other schedules. We analyzed one month of data from the world's largest bike-sharing company to elicit demand behavioral cycles, initially using models from animal tracking that showed large customers fit an Ornstein-Uhlenbeck model with demand peaks at periodicities of 7, 12, 24 hour and 7-days. Lorenz curves of bicycle demand showed that the majority of customer usage was infrequent, and demand cycles from time-series models would strongly overfit the data yielding unreliable models. Analysis of thresholded wavelets for the space-time tensor of bike-sharing contracts was able to compress the data into a 56-coefficient model with little loss of information, suggesting that bike-sharing demand behavior is exceptionally strong and regular. Improvements to predicted demand could be made by adjusting for 'noise' filtered by our model from air quality and weather information and demand from infrequent riders.

econ.EM↗
arXiv2018

Long-Term Unemployed hirings: Should targeted or untargeted policies be preferred?

To what extent, hiring incentives targeting a specific group of vulnerable unemployed (i.e. long term unemployed) are more effective, with respect to generalised incentives (without a definite target), to increase hirings of the targeted group? Are generalized incentives able to influence hirings of the vulnerable group? Do targeted policies have negative side effects too important to accept them? Even though there is a huge literature on hiring subsidies, these questions remained unresolved. We tried to answer them, comparing the impact of two similar hiring policies, one oriented towards a target group and one generalised, implemented on the italian labour market. We used administrative data on job contracts, and counterfactual analysis methods. The targeted policy had a positive and significant impact, while the generalized policy didn't have a significant impact on the vulnerable group. Moreover, we concluded the targeted policy didn't have any indirect negative side effect.

econ.EM↗
arXiv2022

A Time-Varying Network for Cryptocurrencies

Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a dynamic covariate-assisted spectral clustering method to consistently estimate the latent community structure of cryptocurrencies network that accounts for both sets of information. We demonstrate that investors can achieve better risk diversification by investing in cryptocurrencies from different communities. A cross-sectional portfolio that implements an inter-crypto momentum trading strategy earns a 1.08% daily return. By dissecting the portfolio returns on behavioral factors, we confirm that our results are not driven by behavioral mechanisms.

stat.ME↗