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In this study, I present a theoretical social learning model to investigate how confirmation bias affects opinions when agents exchange information over a social network. Hence, besides exchanging opinions with friends, agents observe a public sequence of potentially ambiguous signals and interpret it according to a rule that includes confirmation bias. First, this study shows that regardless of level of ambiguity both for people or networked society, only two types of opinions can be formed, and both are biased. However, one opinion type is less biased than the other depending on the state of the world. The size of both biases depends on the ambiguity level and relative magnitude of the state and confirmation biases. Hence, long-run learning is not attained even when people impartially interpret ambiguity. Finally, analytically confirming the probability of emergence of the less-biased consensus when people are connected and have different priors is difficult. Hence, I used simulations to analyze its determinants and found three main results: i) some network topologies are more conducive to consensus efficiency, ii) some degree of partisanship enhances consensus efficiency even under confirmation bias and iii) open-mindedness (i.e. when partisans agree to exchange opinions with opposing partisans) might inhibit efficiency in some cases., Status: Accepted (Mathematical Social Sciences, Elsevier)

Changes in product characteristics on the extensive margin (the addition of new features and the removal of old ones) are an important and hitherto neglected dimension of quality change. Standard techniques for adjusting price indices for new goods cannot handle such changes satisfactorily, and this leads to an economically and statistically significant bias in the measurement of prices and real output. We combine insights from the theories of exact index numbers and demand for characteristics to develop a new method for incorporating changes on the extensive characteristic margin. Applied to U.K. data on new car sales, our method leads to revisions in estimated inflation rates for this commodity group that are both plausible and quantitatively important.

Decision models such as the drift-diffusion model (DDM) are widely used and broadly accepted for their ability to account remarkably well for binary choices between perceptual hypotheses about a stimulus or for preferential decisions between valued options (such as snacks or consumer products), and for their response time (RT) distributions, as a function of the stimulus or the option values. The DDM is built on an evidence accumulation to bound concept, where, in the value domain, a decision maker repeatedly samples the mental representations of the values of the options on offer until satisfied that there is enough evidence in favor of one option over the other. As the value signals that drive the DDM evidence are derived from value estimates that are not known to the decision maker with certainty, repeated sequential samples are necessary to average out noise. The classic DDM typically does not allow for different options to have different levels of variability in their value representations. However, recent value-based decision studies have shown that decision makers often report levels of certainty regarding value estimates that vary across choice options. There is therefore a need to extend the DDM to include an option-specific value certainty component. We present several such DDM extensions and validate them against empirical data from four previous studies. The data supports best a DDM version in which the drift of the accumulation is based on a sort of signal-to-noise ratio of value for each option (rather than a mere accumulation of samples from the corresponding value distributions). This DDM variant accounts for the positive impact of value certainty on choice consistency and for the negative impact of value certainty on RT.

This study examines the impact of house price on money demand in case of China for the period of 1988Q1-2017Q4. The present study used different estimators, which are robust to handling the endogeneity issues in the data, to capture the long-run effect of housing price index on money demand while controlling for interest rate, gross domestic product and consumer price index. The results suggest that house prices along with GDP, inflation and interest rate are important factors in explaining the money demand in China. To capture the vulnerability periods and identify the behaviour of the money demand and house price index variables in China, this study employs wavelet power spectrum. The results further show that there was a significant vulnerability in money demand between 2006 and 2012 in the short and medium terms. Besides, in order to identify the co-movement between house price and money demand in China, the continuous wavelet coherence approach is applied. The outcomes of continuous wavelet coherence approach reveal that there is strong linkage between money demand and house price index between 2002 and 2018 at both short and long terms. The results show that there is a positive and significant correlation between money demand and house price index in China. Thus, house price index can be a predictive factor for future money demand at low, middle and high frequencies.

This paper explores the microfoundations of consumption models and quantifies the macro implications of consumption heterogeneity. We propose a new empirical method to estimate the response of consumption to permanent and transitory income shocks for different groups of households. We then apply this method to administrative data from Denmark. The large sample size, along with detailed household balance sheet information, allows us to finely divide the population along relevant dimensions. We find that households that stand to lose from an interest rate hike are significantly more responsive to income shocks than those that stand to gain. Following a 1-percentage-point interest rate increase, we estimate that consumption growth decreases by a 1/4 percentage point through this interest rate exposure channel alone, making this channel substantially larger than the intertemporal substitution channel that is at the core of representative agent New Keynesian models.

This paper uses a uniquely granular online retail dataset that spans Russia's enormous currency depreciation in late 2014 to show that firms choose to offer lower quality products in response to rising input costs. We show that this reallocation is not driven by an income shock or "flight from quality". To explain quality downgrading, we construct a model of demand and firm dynamics where consumers value low prices and high qualities, and firms draw product cost and quality from a joint distribution on entry. We argue that a positive correlation between marginal cost and quality can explain quality downgrading. After verifying this correlation in the data, we calibrate the model and simulate a counterfactual cost shock. The model is able to generate a reallocation in offerings towards low cost, low quality products, and yields starkly different welfare implications following an exchange rate shock than a model with only cost heterogeneity.

We produce methodology for regression analysis when the geographic locations of the independent and dependent variables do not coincide, in which case we speak of misaligned data. We develop and investigate two complementary methods for regression analysis with misaligned data that circumvent the need to estimate or specify the covariance of the regression errors. We carry out a detailed reanalysis of Maccini and Yang (2009) and find economically significant quantitative differences but sustain most qualitative conclusions.

This paper analyzes Okun’s Law for Spain using regional data. The results confirm a very high Okun coefficient for Spain, with a high degree of regional heterogeneity. Furthermore, we find that panel data techniques provide notably more stable estimates than time series techniques applied to the same regional data. Finally, the results reveal a remarkable degree of regional heterogeneity in cyclical asymmetry in Okun’s Law for the Spanish case. Keywords: Unemployment; Okun’s Law; Economic Cycles; Regional Growth. JEL: E24, E32, R11.

We establish conditions under which forecasting performance can be improved by rotating between a set of underlying forecasts whose predictive accuracy is tracked using a set of time-varying monitoring instruments. We characterize the properties that the monitoring instruments must possess to be useful for identifying, at each point in time, the best forecast and show that these reflect both the accuracy of the predictors used by the underlying forecasting models and the strength of the monitoring instruments. Finite-sample bounds on forecasting performance that account for estimation error are used to compute the expected loss of the competing forecasts as well as for the dynamic rotation strategy. Finally, using Monte Carlo simulations and empirical applications to forecasting inflation and stock returns, we demonstrate the potential gains from using conditioning information to rotate between forecasts.

We estimate a structural model using data from a novel experiment investigating how investors’ preferred frequency of portfolio evaluations balance the opposing effects of ambiguity and loss aversion. Investors in the experiment face initial ambiguity concerning return distributions for an asset. They observe draws from the true return distribution of the asset, allowing them to reduce their ambiguity through time. We exploit portfolio choices and stated beliefs over possible return distributions to estimate preferences and ambiguity updating rules. We find that 70% of investors would opt for a high frequency of portfolio evaluations, reflecting the dominating effect of ambiguity aversion over loss aversion.

In this paper, we document the importance of memory in machine learning (ML)-based models relying on firm characteristics for asset pricing. We come to three empirical conclusions. First, the pure out-of-sample fit of the models is often poor: we find that most R^2 measures are negative, especially when training samples are short. Second, we show that poor fit does not necessarily matter from an investment standpoint: what actually counts are measures of cross-sectional accuracy, which are seldom reported in the literature. Third, memory is key. The accuracy of models is maximal when both labels and features are highly autocorrelated. Relatedly, we show that investments are the most profitable when they are based on models driven by strong persistence. Average realized returns are the highest when the size of training samples is large and when the horizon of the predicted variable is also long.

We introduce a new dataset of real gross domestic product (GDP) growth and core personal consumption expenditures (PCE) inflation forecasts produced by the staff of the Board of Governors of the Federal Reserve System. In contrast to the eight Greenbook forecasts a year the staff produces for Federal Open Market Committee (FOMC) meetings, our dataset has roughly weekly forecasts. We use these new data to study whether the staff forecasts efficiently and whether efficiency, or lack thereof, is time-varying. Prespecified regressions of forecast errors on forecast revisions show that the staff's GDP forecast errors correlate with its GDP forecast revisions, particularly for forecasts made more than two weeks from the start of a FOMC meeting, implying GDP forecasts exhibit time-varying inefficiency between FOMC meetings. We find some weaker evidence for inefficient inflation forecasts.

Bond returns are time-varying and predictable. What economic forces drive this variation? To answer this long-standing question, we propose a consumption-based model with recursive preferences, long-run risks, and inflation non-neutrality. Our model offers two important insights. First, our model matches well the post-1990 nominal upward-sloping U.S. Treasury yield curve. Second, consistent with our model's implication, variance risk premium based on the U.S. interest rate derivatives data emerges as a strong predictor for short-horizon Treasury excess returns, above and beyond the predictive power of other popular factors. In the model equilibrium, the variance risk premium is related to the short-run risks in the economy, while standard forward-rate-based factors are associated with long-run risks in the economy.

We analyse a long panel of households’ stock market beliefs to gain insights into the nature of the levels, dynamics, and informativeness of these expectations. In a first step, we classify respondents into one of five groups based on their beliefs data alone. In a second step, we estimate models of expectations at the group level so that belief levels, volatility, and response to information can vary freely across groups. At opposite extremes in terms of optimism we identify pessimists who expect substantially negative returns and financially sophisticated individuals whose expectations are close to the historical average. Two groups expect average returns around zero and differ only in how they respond to information: Extrapolators who become more optimistic following positive information and mean-reverters for whom the opposite is the case. The final group is characterised by its members being unable or unwilling to quantify their beliefs about future returns.

“Rational inattention” is becoming increasingly prominent in economic modeling, but there is little empirical evidence for its central premise-that the choice of attention results from a cost-benefit optimization. Observational data typically do not allow researchers to infer attention choices from observables. We fill this gap in the literature by exploiting a unique dataset of professional forecasters who update their inflation forecasts at days of their choice. In the data we observe how many forecasters update (extensive margin of updating), the magnitude of the update (intensive margin), and the objective of optimization (forecast accuracy). There are also “shifters” in incentives: A contest that increases the benefit of accurate forecasting, and the release of official data that reduces the cost of processing information. These features allow us to link observables to attention and incentive parameters. We structurally estimate a model where the decision to update and the magnitude of the update are endogenous and the latter is the outcome of a rational inattention optimization. The empirical findings provide support for the key implication of rational inattention that information-processing efforts react to changing incentives. Counterfactuals reveal that accuracy is maximized if the contest date coincides with the release of information, aligning higher benefits with lower costs of attention.

A regression-based algorithm is proposed that allows to construct arbitrarily many comparable, multi-annual, consistent time series on monthly, weekly, daily, hourly, and minute-by-minute search volume indices based on the scattered data obtained from Google Trends. The accuracy of the algorithm is illustrated using old datasets from Google that have been used previously in the literature. The algorithm is used to construct an index of prices searched online (IPSO). The IPSO improves monthly inflation forecasts for the United States and the Euro Area and helps to predict consumption loan growth in the Euro Area. The IPSO is also contemporaneously correlated with consumption loan growth in the Euro Area.

This paper argues that the canonical search-and-matching model cannot generate the observed cyclical asymmetry of the unemployment rate. In the United States, the unemployment rate raises quickly and abruptly at the onset of contractions and declines slowly and gradually during expansions. This pattern produces positive skewness in the distribution of unemployment rate changes, while the model produces a counterfactually negative skewness. The key feature of the model responsible for this counterfactual prediction is the convexity of hiring costs in aggregate employment, which leads to excessive responsiveness of job vacancies to positive shocks in periods of high unemployment. I argue that the inability of the model to replicate the cyclical asymmetry in the data stands regardless of its ability to generate realistic fluctuations in unemployment. Furthermore, high replacement rates and real wage rigidity (both fixed and downward rigid wages) - commonly used to enhance amplification of shocks - do not resolve the puzzle, rather they make it worse.

I estimate the causal effect of increasing displayed product ratings by a half star on Wayfair.com to be a five percent increase in demand for products. This increase is driven by an increase in sales, not a change in price. Products with more ratings or that are sold by lesser known brands see a large effect of star ratings on orders, while products with fewer ratings or that are sold by well known brands see no effect of ratings on orders. Furthermore, I find the causal effect of ratings to be heterogeneous across products with different ratings — with the highest rated products seeing the largest benefit to having a marginally higher rating — but not across customers with varying degrees of experience purchasing products from the site. This heterogeneity in demand effects shows that the benefits of higher ratings are not equivalent across old and new products and sellers, indicating potential policy responses by e-commerce sites.

We study the effects of firm-level microeconomic fluctuations on aggregate productivity in the United Kingdom. We show that a standard measure of residual productivity growth of the largest UK firms (the ‘granular residual’) produces results that are counter-intuitive and not statistically significant. To combat this, we introduce a unique production function approach to estimate firm-specific technology shocks, accounting for firm-level heterogeneity and common shocks. Using this measure, we find that firm-level shocks matter; the ‘granular residual’ explains around 30% of aggregate UK productivity dynamics. We also show that simplifications of our approach, which omit controlling for firm-level heterogeneity or do not account for common shocks, do not perform well, highlighting the importance of identifying firm-specific shocks correctly in order to properly test the ‘granularity hypothesis’.

Using proxy statement data describing the terms of compensation contracts, we examine how overlapping membership between compensation and audit committees influences the use of earnings metrics in compensation. Although research predicts that such overlap could either increase or decrease the reliance on earnings, we find that firms with overlapping directors rely less on earnings-based performance measures in incentive contracts without altering the overall level of performance-contingent cash bonuses. In addition, we provide evidence that firms substitute earnings measures with measures less subject to earnings management. Our findings are robust to potential alternative explanations, extend to an implicit relation between earnings and compensation for a larger sample, and are not driven by the tendency toward an overlapping committee structure more broadly. This paper was accepted by Suraj Srinivasan, accounting.

Transport costs are a crucial element of applied spatial models such as spatial macro-economic models or interaction models for e.g. trade, migration or commuting. However, good estimates of these costs are not available at lower levels of spatial aggregation. In applied work, the distance between two regions is often approximated by the distance between the largest city in each region. For costs within regions, researchers often resort to crude ad-hoc approximations relating internal distance to the area of the region assuming a uniform internal distribution. This paper improves on this by considering averages of transport costs calculated between extremely large random samples of centroids which are drawn from a population grid. This allows calculating distances, travel times and transport costs both between and within regions, while taking into account the unequal distribution of population within the regions. The use of a detailed road network and many auxiliary datasets allows performing policy analysis. We assess the impact on transport cost of an increase in fuel prices, and find that it has a relatively high effect on transport costs in Eastern Europe. We evaluate transport infrastructure investment of the European Cohesion Policy program 2014–2020. The largest decreases in transport costs are found in targeted regions in Eastern Europe while the effect is much smaller in targeted regions in Southern Europe, suggesting decreasing returns to further transport infrastructure in these regions. We find significant inter-regional spillovers to regions directly bordering the regions targeted by the policy in Eastern Europe, such as in Germany and Austria, but also Finland and Northern Italy. The positive spillovers to EU regions in Western Europe are quite small.

Functional-coefficient cointegrating models have become popular to model nonlinear nonstationarity in econometrics (Cai et al., 2009; Xiao, 2009). However, there is rare study on testing the existence of functional-coefficient cointegration. Consequently, functional-coefficient regressions involving nonstationary regressors may be spurious. This paper investigates the effect that spurious functional-coefficient regression has on the model diagnostics. We find that common characteristics of spurious regressions are manifest, including divergent local significance tests, random local goodness-of-fit, and local Durbin–Watson ratio converging to zero, complementing those discovered in spurious linear and nonparametric regressions (Phillips, 1986, 2009). In addition, spuriousness causes the divergences of the global significance tests proposed by Xiao (2009) and Sun et al. (2016), which are likely to produce misleading conclusions for practitioners when cointegration tests fail to reject a spurious regression. To resolve the problems, we propose a simple-to-implement inference procedure based on a semiparametric balanced regression, by augmenting regressors of the original spurious regression with lagged dependent variable and independent variables, with the aid of the marginal integration. This procedure achieves spurious regression detection via standard nonparametric inferential asymptotics, and is found robust to the true relationship between the integrated processes. The theoretical results are also corroborated by simulations.

The Repeat-Sales (RS) model controls quality by using the prices of the same items in various time periods. However, this methodology suffers from sample selection bias. To address this issue, a Heckman two-stage procedure has been applied to a sample of Picasso paintings from 1990 to 2016. Contrary to the literature — which argues sample selection bias comes from buy-ins — in this paper, bias is modelled to capture information on items sold only once. The research field frequently uses the standard (RS) index, yet empirical evidence shows systematic differences between the standard RS index and the Heckman RS index. These differences are relevant when buyers make decisions on art investments.

I measure the impact of rarity on price. The main challenge is that usually rarity is positively correlated with quality. In addition, sometimes price also affects rarity. To overcome these difficulties, I study an NBA digital collectibles market called NBA Top Shot where those concerns are well addressed. By analyzing novel and rich data sets, I find that the elasticity of price with respect to circulation is -0.74 on average. I also find that less popular products are more elastic than more popular products are.

The present study aims to evaluate the impact of foreign assistance on income inequality of selected developing economies. Panel co-integration technique is applied for the purpose of estimation. After confirming the pre-conditions for cointegration, we have applied the Fully Modified-OLS method to estimate the association between foreign aid and income inequality. Controlling for other variables, we find a positive and significant association between foreign aid and inequality. The empirical results are robust concerning the sub-samples of developing economies selected according to the income classification of the World Bank. The empirical results are also robust concerning the alternative measure of income inequality.

A fixed-effects logit model that accounts for feedback effects of the dependent variable on the covariates is proposed. The model is formulated by including leads of the predetermined covariates among the regressors and it is proved to satisfy certain theoretical properties under some regularity conditions on the distribution of the covariates. Estimation is based on the Conditional Maximum Likelihood ( cml ) method for the static logit model and the Pseudo- cml ( pcml ) method for the corresponding dynamic formulation. Both methods have good finite-sample properties even when the required regularity conditions are not satisfied. An application is provided about female labor supply where we jointly account for the predetermined number of children and husbands’ income. Differently from previous studies, it emerges that female employment history does not affect future fertility choices and the husband’s earnings, as no evidence of feedback effects is found.

We relate time-varying aggregate ambiguity about volatility (V-VSTOXX) to individual investor trading. We use the trading records of more than 100,000 individual investors from a large German online brokerage from March 2010 to December 2015. We find that an increase in ambiguity is associated with increased investor activity. It also leads to a reduction in risk-taking, which does not reverse over the following days. Ambiguity averse investors are more prone to ambiguity shocks. These results replicate when using the dispersion of professional forecasters as a long-term measure of ambiguity and are robust when controlling for newspaper- or market-based ambiguity measures.

In this paper, we study the impact of labor unions on product quality failures. We use a product recall as our measure of quality failure because it is an objective metric that is applicable to a broad cross-section of industries. Our analysis employs a union panel setting and close union elections in a regression discontinuity design framework to overcome identification issues. In the panel regressions, we find that firms that are unionized and those that have higher unionization rates experience a greater frequency of quality failures. The results obtain even at a more granular establishment level in a subsample in which we can identify the manufacturing establishment associated with the recalled product. When comparing firms in close elections, we find that firms with close union wins are followed by significantly worse product quality outcomes than those with close union losses. These results are amplified in non–right-to-work states, where unions have a relatively greater influence on the workforce. We find that unionization increases firms’ costs and operating leverage and, consequently, crowds out investments that potentially impact quality. We also find some suggestive evidence that unions may compromise quality by hurting employee morale and by resisting technological upgrades in the firm. Overall, our results suggest that unions have an adverse impact on product recalls, and thus, product quality is an important dimension along which unions impact businesses. This paper was accepted by Gustavo Manso, finance.

Diurnal fluctuations in volatility are a well-documented stylized fact of intraday price data. This warrants an investigation how this intraday periodicity (IP) affects both finite sample as well as asymptotic properties of several popular realized estimators of daily integrated volatility which are based on functionals of a finite number of intraday returns. It turns out that most of the estimators considered in this study exhibit a finite-sample bias due to IP, which can however get negligible when the number of intraday returns diverges to infinity. The appropriate correction factors for this bias are derived based on estimates of the IP. The adequacy of the new corrections is evaluated by means of a Monte Carlo simulation study and an empirical example.

Many policies allocate harms or benefits that are uncertain in nature: they produce distributions over the population in which individuals have different probabilities of incurring harm or benefit. Comparing different policies thus involves a comparison of their corresponding probability distributions, and we observe that in many instances the policies selected in practice are hard to explain by preferences based only on the expected value of the total harm or benefit they produce. In cases where the expected value analysis is not a sufficient explanatory framework, what would be a reasonable model for societal preferences over these distributions? Here we investigate explanations based on the framework of probability weighting from the behavioral sciences, which over several decades has identified systematic biases in how people perceive probabilities. We show that probability weighting can be used to make predictions about preferences over probabilistic distributions of harm and benefit that function quite differently from expected-value analysis, and in a number of cases provide potential explanations for policy preferences that appear hard to motivate by other means. In particular, we identify optimal policies for minimizing perceived total harm and maximizing perceived total benefit that take the distorting effects of probability weighting into account, and we discuss a number of real-world policies that resemble such allocational strategies. Our analysis does not provide specific recommendations for policy choices, but is instead interpretive in nature, seeking to describe observed phenomena in policy choices.

The niche and fitness differences of modern coexistence theory separate mechanisms into stabilizing and equalizing components. Although this decomposition can help us predict and understand species coexistence, the extent to which mechanistic inference is sensitive to the method used to partition niche and fitness differences remains unclear. We apply two alternative methods to assess niche and fitness differences to four well-known community models. We show that because standard methods based on linear approximations do not capture the full community dynamics, they can sometimes lead to incorrect predictions of coexistence and misleading interpretations of stabilizing and equalizing mechanisms. Specifically, they fail when both species occupy the same niche or in the presence of positive frequency dependence. Conversely, a more recently developed method to decompose niche and fitness differences, which accounts for the full non-linear dynamics of competition, consistently identifies the correct contribution of stabilizing and equalizing components. This approach further reveals that when the true complexity of the system is taken into account, essentially all mechanisms comprise both stabilizing and equalizing components and that local maxima and minima of stabilizing and equalizing mechanisms exist. Amidst growing interest in the role of non-additive and higher order interactions in regulating species coexistence, we propose that the effective decomposition of niche and fitness differences will become increasingly reliant on methods that account for the inherent non-linearity of community dynamics.

We provide plausibly identified evidence for the role of investor disagreement in asset pricing. Our natural experiment exploits the staggered implementation of EDGAR, which induces a reduction in investor disagreement with no accompanying changes in company fundamentals, disclosure quality, or earnings management. The reduction in disagreement leads to lower stock price crash risk. The effect is more pronounced for stocks with binding short-sale constraints and high investor optimism. The reduction in disagreement also leads to higher subsequent returns. Our results provide evidence consistent with models of investor disagreement.

This paper demonstrates state-of-the-art text sentiment analysis tools while developing a new time-series measure of economic sentiment derived from economic and financial newspaper articles from January 1980 to April 2015. We compare the predictive accuracy of a large set of sentiment analysis models using a sample of articles that have been rated by humans on a positivity/negativity scale. The results highlight the gains from combining existing lexicons and from accounting for negation. We also generate our own sentiment-scoring model, which includes a new lexicon built specifically to capture the sentiment in economic news articles. This model is shown to have better predictive accuracy than existing “off-the-shelf” models. Lastly, we provide two applications to the economic research on sentiment. First, we show that daily news sentiment is predictive of movements of survey-based measures of consumer sentiment. Second, motivated by Barsky and Sims (2012), we estimate the impulse responses of macroeconomic variables to sentiment shocks, finding that positive sentiment shocks increase consumption, output, and interest rates and dampen inflation.

We investigate the optimal portfolio choice problem for an investor who has a utility function of the smooth ambiguity model. We identify necessary and sufficient conditions for a given portfolio to be optimal for such an investor. We define the implied ambiguity of a portfolio as the smallest ambiguity aversion coefficient with which the portfolio is optimal, and the measure of ambiguity perception as the part of the variability in asset returns that can be attributed to the ambiguity. We show that there are one-to-one relations between the implied ambiguity, the Sharpe ratio, and the pricing errors when the portfolio is taken as the pricing portfolio, and that the measure of ambiguity perception is determined by the Sharpe ratio and the alpha. Based on the U.S. stock market data, we assess how ambiguity averse the representative investor is and what types of stocks the investor perceives as having more ambiguous returns than others. This paper was accepted by Manel Baucells, behavioral economics and decision analysis.

Ensuring aggregate food price stability requires a forward-looking assessment of the risk that unexpected deviations in individual food items’ inflation lead to large shocks in the aggregate food price inflation. To do so, we propose using a multivariate GARCH framework in combination with the Euler method to (1) estimate the conditional standard deviation and quantiles of the food price inflation shocks and (2) attribute the total risk to the underlying food items. For the FAO food price index, we find that even though meat inflation systematically has the highest weight in the aggregate index, cereal inflation is the main contributor to the total food price inflation risk over the period 1990–2018. The use of time series models and the Cornish-Fisher expansion make the risk characterization forward-looking and a potentially helpful tool for risk management.

The paper analyses the relationship between share pledging and firm performance and explores the influence of the monitoring mechanism on share pledging using the Indian dataset. The data set includes BSE Midcap firms from March 2009 to March 2019. Following Two-way Fixed Effect Model (2FE) with lagged dependent variables (LDV), we conclude that pledging activity is positively associated with stock volatility and institutional-holding serves as a monitoring mechanism for share pledging.

Gravity variables such as distance, adjacency, colony, free trade agreements or language are used to capture the effects of trade costs in empirical studies. By using actual data on trade costs, this paper decomposes the overall effects of such variables on trade into those through three gravity channels: duties/tariffs (DC), transportation-costs (TC), and dyadic-preferences (PC). When PC is ignored as is typical in existing studies in the literature, it is shown that nearly all gravity effects are due to distance, 29 percent through DC and 71 percent through TC. The tables turn as the additional channel of PC is introduced and shown to dominate other channels, with adjacency contributing about 45 percent, distance about 32 percent, colony about 14 percent, free trade agreements about 7 percent, and language about 2 percent. It is implied that gravity variables mainly capture the effects of demand shifters rather than supply shifters (as implied by the existing literature). The results are further connected to several existing discussions in the literature, such as welfare gains from trade and the distance puzzle.

Experimental studies in behavioral finance historically have confirmed that subjects are highly influenced by reference points when making economic decisions. A recent study by Baucells, Weber and Welfens (2011) analyzed the dynamics of reference price formation using experimental methods with subjects forming reference prices for stocks based on observed price sequences. Their study helps to clarify behavioral effects of past prices on reference price formation but it does not consider contextual information. We extend the BWW study by adding market price information as background to the experiment, and we investigate how this additional information affects reference point formation and updating. Our overarching hypothesis is that if the market background information has no impact then it should not alter the results of BWW; otherwise, the additional market information should be an explanatory variable for the reference point. Our results confirm the hypothesis that market information matters. We also investigate the impact of pessimism and optimism on reference prices by the combining the BWW framework with a model of disappointment aversion and anticipatory feelings developed by Gollier and Muerman (2010). Our study fills a void in the literature by providing new evidence on the impact of contextual market information on reference point formation in an investment setting.

The aim of this paper is to investigate whether inflation expectations in Brazil have characteristics and statistical properties that can be correlated (possibly in a causal way) with observed variables of interest and expectations about them. Our analysis covers the period from December 2001 to August 2018. We test the hypothesis of rational expectations in the formation of inflation expectations by the respondents of the official survey conducted by the Central Bank of Brazil, examining the behavior of their forecast errors. As these errors are biased and can be predicted, we reject the hypothesis of full rationality. We also test models of noisy and sticky information, and we cannot conclude that the deviations from full rationality can be explained by information rigidity. Additionally, with a vector error correction model, we find evidence that the expectations about the related macroeconomic variables respond to each other as predicted by a theoretically-grounded macroeconomic model. Therefore, inflation expectations in Brazil are to an important extent consistent with more general expectations about the future performance of the economy.

Objective Models of addiction often posit bidirectional and dynamic associations between constructs relevant to the etiology and maintenance of addictive behaviors. The cross-lagged panel model (CLPM) is commonly used in addiction research but has been critiqued for not appropriately adjusting for between-person variance. Alternatives to the CLPM have been suggested but remain underutilized. The primary purpose of this article is to highlight interpretational limitations of the CLPM and to provide examples of alternative models. Method We specified CLPM, Random-Intercept CLPM, and a Latent Curve Model with Structured Residuals using four waves of data from Project MATCH (n = 1,201). We modeled prospective relations among depression symptoms and temptation to drink. Substantive inferences and assumptions across models were compared. Results The CLPM provided the most evidence of significant cross-lagged paths but the poorest fit to the data compared to other models. Alternative models found little evidence of prospective within-person associations, and more evidence for between-person associations and wave-specific within-person relations between depression symptoms and temptation to drink. Conclusions This study highlights shortcomings of the CLPM and details alternative models to consider. Addiction researchers should consider alternatives to the CLPM to more optimally delineate relations among constructs across time. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

This paper shows that firms with longer debt maturities earn risk premia not explained by unconditional standard factor models. We develop a dynamic capital structure model and find that firms with long-term debt exhibit more countercyclical leverage, making them more highly levered in downturns, when the market price of risk is high. The induced covariance between risk exposure and the market price of risk generates a maturity premium which we estimate at 0.21% per month. Empirical results from a conditional CAPM as well as observed beta dynamics are consistent with the model. We also exploit exogenous variation of debt maturities at the onset of the financial crisis and find that firms with shorter debt maturities experienced a smaller increase in leverage during the crisis. Also, after an initial spike, the betas of short-maturity firms reverted to levels below those of long-maturity firms by the end of 2008.

This paper studies manipulation in cash-settled derivative contract markets. When traders hedge factor risk using cash-settled derivatives, which are settled based on the price of a spot good, traders can manipulate settlement prices by trading the spot good. In equilibrium, manipulation can make all agents worse off. I define two measures of manipulation-induced welfare losses, which can be estimated using commonly observed market data. Using these measures, I estimate how large manipulation-induced distortions would be if COMEX gold futures were cash-settled using the London Bullion Market Association gold price benchmark.

This research proposes a first approximation of Inequality of Opportunity (IOp) in Mexico based on a concept of ex-post compensation, fully consistent with Roemer s approach of In-equality of Opportunity. This framework considers the advantage reached by an individual to be determined by the circumstances at origin and by the effort exerted. Following Brunori and Neidhofer (2020), we construct a data-driven procedure using regression trees to identify types based on circumstances. To identify degrees of effort, an algorithm estimates the distribution of outcome in each type based on coefficients of Bernstein polynomials. Our results underline the differences, in terms of opportunities, faced by individuals, based on the territory in which they grew up, the household context, and their sex. We find that the education and the wealth of parents are the principal circumstances that shape their trajectories. Importantly, territorial variables are significant determinants of IOp among the most disadvantaged at origin, but they hold less importance for the most advantaged. IOp is lower in urban areas, in the Northern region and in Mexico City compared to other regions. The Southern region and rural territories are the most unequal. We also estimate that the weight of IOp in terms of total inequality varies according if we adopt an ex-post or an ex-ante approach, which can lead to different conclusions.

The inter-temporal Capital Asset Pricing Model (CAPM) assumes that investors are risk-averse. However, there is a very large body of empirical and experimental evidence documenting that many investors are not globally risk-averse: Prospect Theory and aspiration-level models are two well-known examples of this literature. This paper employs Stochastic Dominance criteria to generalize the inter-temporal CAPM for all investors with increasing utility functions. Another advantage of the proposed approach is its simplicity: it does not require dynamic programming, and it allows for ambiguous investment horizons.

This paper exploits cross-sectional variation at the level of U.S. counties to generate real-time forecasts for the 2020 U.S. presidential election. The forecasting models are trained on data covering the period 2000–2016, using high-dimensional variable selection techniques. Our county-based approach contrasts the literature that focuses on national and state level data but uses longer time periods to train their models. The paper reports forecasts of popular and electoral college vote outcomes and provides a detailed ex-post evaluation of the forecasts released in real time before the election. It is shown that all of these forecasts outperform autoregressive benchmarks. A pooled national model using One-Covariate-at-a-time-Multiple-Testing (OCMT) variable selection significantly outperformed all models in forecasting the U.S. mainland national vote share and electoral college outcomes (forecasting 236 electoral votes for the Republican party compared to 232 realized). This paper also shows that key determinants of voting outcomes at the county level include incumbency effects, unemployment, poverty, educational attainment, house price changes, and international competitiveness. The results are also supportive of myopic voting: economic fluctuations realized a few months before the election tend to be more powerful predictors of voting outcomes than their long-horizon analogs.

This paper proposes new bipolarization indices based on the Bonferroni and De Vergottini indices of inequality. These new indices follow the approach of Foster and Wolfson and are based on a new subgroup decomposition of the Bonferroni and De Vergottini indices of inequality. We also provide the conditions under which the new polarization indices satisfy the Increased Spread and Increased Bipolarity axioms. Finally, a simulation study is performed to compare the different sensitivity of the new bipolarization indices to progressive transfers. An empirical application based on EU-SILC data for Italy in the period 2007-2011 also shows the appeal of our proposal.

Leveraging the increasing availability of ”big data” to inform forecasts of labor market activity is an active, yet challenging, area of research. Often, the primary difficulty is finding credible ways with which to consistently identify key elasticities necessary for prediction. To illustrate, we utilize a state-level event-study focused on the costliest hurricanes to hit the U.S. mainland since 2004 in order to estimate the elasticity of initial unemployment insurance (UI) claims with respect to search intensity, as measured by Google Trends. We show that our hurricane-driven Google Trends elasticity leads to superior real-time forecasts of initial UI claims relative to other commonly used models. Our approach is also amenable to forecasting both at the state and national levels, and is shown to be well-calibrated in its assessment of the level of uncertainty for its out-of-sample predictions during the Covid-19 pandemic.

Emerging markets’ interest rate spreads display substantial time-varying volatility. We show that models with endogenous sovereign default risk a la Eaton and Gersovitz (1981) can account for such volatility, even in the absence of shocks to the second moments of the exogenous stochastic variables. In particular, these models feature a key non-linearity that allows them to replicate the stochastic volatility of interest rate spreads and its comovement with other important economic variables. Volatility correlates positively with the level of the spreads and the trade balance, negatively with output and consumption. Hence, sovereign default models endogenize the stochastic volatility of interest rates observed in emerging market economies.

In a series of experiments with 375 participants, we investigate the impact of biased polls on election outcomes, when voters have the opportunity to observe and learn about the bias by playing multiple voting rounds. While in control conditions, polls are unbiased, in treatment conditions, participants view only poll results where a particular candidate's vote share is the largest. We find that this candidate is consistently elected more often in the treatments than in the controls, because biased polls robustly distort voters' expectations about vote shares. Remarkably, this effect holds even when voters are \textit{explicitly} informed about the bias. Our results suggest that the anchoring effects of biased polls on participants' beliefs are stronger than potential reactance to biased information.

Although Bayesian linear mixed effects models are increasingly popular for analysis of within-subject designs in psychology and other fields, there remains considerable ambiguity on the most appropriate Bayes factor hypothesis test to quantify the degree to which the data support the presence or absence of an experimental effect. Specifically, different choices for both the null model and the alternative model are possible, and each choice constitutes a different definition of an effect resulting in a different test outcome. We outline the common approaches and focus on the impact of aggregation, the effect of measurement error, the choice of prior distribution, and the detection of interactions. For concreteness, three example scenarios showcase how seemingly innocuous choices can lead to dramatic differences in statistical evidence. We hope this work will facilitate a more explicit discussion about best practices in Bayes factor hypothesis testing in mixed models.