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There exists recent formal theoretical evidence that, relative to true ages, Carbon-14 (¹⁴C) ages for fossils of living organisms (man or animals) are lacking in qualitative and quantitative robustness. This study examines the extent to which highlighted recent evidence extends to ¹⁴C ages for Paper, Ink that is derived from wood, Papyri, and Parchments. The formal axiomatic (mathematically theoretical) evidence establishes, relative to true ages for paper and ink, that ¹⁴C ages for either of paper or ink are qualitatively and quantitatively robust. While ¹⁴C ages for Papyri are not quite as robust as ¹⁴C ages for Paper, formal axioms establish that, asymptotically, and relative to true ages for Papyrus plants, ¹⁴C ages for Papyri are qualitatively and quantitatively robust. While ¹⁴C ages for Parchments originating within the same spatial location are shown to be (asymptotically) qualitatively and quantitatively robust, absent adjustments for spatial differences in rates of young carbon formation, the entire universe of ¹⁴C ages for Parchments is lacking in qualitative and quantitative robustness. The preliminary empirical evidence provides unequivocal support for study predictions. In aggregate, while there exist some concerns about robustness of ¹⁴C ages for Parchments, the evidence reveals boundedness of the concerns, with outcome it safely can be asserted that inferences derived from ¹⁴C ages for Paper, Ink that comes from wood, Papyri, and Parchments can be deemed to be robust.

The present pitch paper intends to pitch the research idea, its methodology, and possible outcomes by following Faff (2015) pitch my research template The rese

We claim that Shiller's analysis of the efficient market variance inequality suffers from statistical and conceptual errors which falsify his conclusions. We detail the errors and provide an analogy to demonstrate them.

We study implications of unpriced "granular measurement errors" -- idiosyncratic shocks to large firms that aren't well-diversified in market indices -- for asset pricing tests and propose alternative tests insensitive to them. We find stronger evidence of an intertemporal relation between the expected return and variance of the market after these corrections. Cross-sectionally, estimated market betas -- especially for small/high beta stocks -- are biased, the size anomaly disappears, and the price of risk for the market portfolio is consistent with theory. The granular residual is volatile and less informative about real activity than our adjusted index, potentially rationalizing lower/zero risk compensation.

This Internet Appendix contains mathematical and empirical results on the market timing induced bias in Jensen’s alpha using conditional models with time-varying skill in the spirit of Kacperczyk et al. (2014).Full paper available at https://ssrn.com/abstract=1253923

This paper studies the spillover effect of home mortgage delinquencies using a discrete-choice spatial network model. In our empirical study, a main challenge in estimating this model is that mortgage repayment decisions can only be observed for a sample of all the borrowers in the study region. We show that the nested pseudolikelihood (NPL) algorithm can be readily modified to accommodate this missing data issue. Monte Carlo simulations indicate that the proposed estimator works well in finite samples and ignoring this issue leads to a downward bias in the estimated spillover effect. We estimate the model using data on single-family residential mortgage delinquencies in Clark County of Nevada in 2010, and find strong evidence of spillover effects. We also conduct some counterfactual experiments to illustrate the policy relevance of the spillover effect.

Higher than average Price-to-Earnings (P/E) ratios and Cyclically-Adjusted Price-to-Earnings (CAPE) ratios have been tolerated by investors recently on the basis of low interest and inflation rates. This white paper analyzes what level of returns can reasonably be expected going forward from today’s elevated valuation multiples.

We consider a broad class of intertemporal economic problems and characterize the short-run and long-run responses of the demand for a good to a permanent increase in its market price. Depending on the interplay between self-productivity and time discounting, we show that dynamic substitution effects can generate price elasticities of opposite signs in the short run and in the long run. (JEL D11, D15, H20, J22, J24)

This paper provides invariance theorems that facilitate testing for the existence of an asset price bubble in a market where the price evolves as a Markov diffusion process. The test involves only the properties of the price process’ quadratic variation under the statistical probability. It does not require an estimate of either the equivalent local martingale measure or the asset’s drift. To augment its use, a new family of stochastic volatility price processes is also provided where the processes’ strict local martingale behavior can be characterized.

In this paper we discuss general identification results for Structural Vector Autoregressions (SVARs) with external instruments, considering the case in which r valid instruments are used to identify g Ï 1 structural shocks, where r Ï g. We endow the SVAR with an auxiliary statistical model for the external instruments which is a system of reduced form equations. The SVAR and the auxiliary model for the external instruments jointly form a "larger" SVAR characterized by a particularly restricted parametric structure, and are connected by the covariance matrix of their disturbances which incorporates the "relevance" and "exogeneity" conditions. We discuss identification results and likelihood-based estimation methods both in the "multiple shocks" approach, where all structural shocks are of interest, and in the "partial shock" approach, where only a subset of the structural shocks is of interest. Overidentified SVARs with external instruments can be easily tested in our setup. The suggested method is applied to investigate empirically whether commonly employed measures of macroeconomic and financial uncertainty respond on-impact, other than with lags, to business cycle fluctuations in the U.S. in the period after the Global Financial Crisis. To do so, we employ two external instruments to identify the real economic activity shock in a partial shock approach.

Full paper available at: https://ssrn.com/abstract=2840730 This supplementary material to "Which Risk Factors Drive Oil Futures Price Curves?" includes the derivation of the futures price expression, details of the Kalman Filter utilised, and the equation for the sensitivity of the expected backwardation to parameter shocks. Results plots for other data periods, and descriptive statistics of the data are also presented. Furthermore, results for the HMF model and stepwise HMF model fits are given for other data periods. Finally, results for the beta and gamma Versions of the HMF model are detailed.

Full paper is available at: https://ssrn.com/abstract=3015582 Identifying firm connections by shared analyst coverage, we find that a connected-firm (CF) momentum factor generates a monthly alpha of 1.68% (t = 9.67). In spanning regressions, the alphas of industry, geographic, customer, customer/supplier industry, single- to multi-segment, and technology momentum factors are insignificant/negative after controlling for CF momentum. Similar results hold in cross-sectional regressions and in developed international markets. Sell-side analysts incorporate news about linked firms sluggishly. These effects are stronger for complex and indirect linkages. Consistent with limited investor attention, these results indicate that momentum spillover effects are a unified phenomenon that is captured by shared analyst coverage.

Full paper available at https://ssrn.com/abstract=3141041 We first present simulations investigating some properties of the Inverse Product Differentiation Logit (IPDL) model. Next, we provide a range of general methods for building Generalized Logit (GL) models along with illustrative examples that go beyond the IPDL model.

A comparison of "Governance and Taxes" with a previous working paper version reveals a number of irregularities that together suggest the analysis was not performed as described in the published version. In particular, the two versions claim to use different main empirical specifications, but the numbers in all 11 tables are exactly the same in both versions. An assessment of other comparisons between the two versions suggests that it is unlikely that these irregularities are unintentional errors. Instead, a straightforward explanation of these anomalies is data falsification: the working paper version accurately describes the main specification, and the published version misstates the main specification. I conclude by demonstrating that the main result, when accurately described, fails a simple placebo test and is therefore spurious.

In his Nobel speech, Eugene Fama claimed that critics have failed to offer a complete alternative to the efficient market hypothesis (EMH). More specifically, Fama stated, “Most important, the behavioral literature has not put forth a full blown model for prices and returns that can be tested and potentially rejected – the acid test for any model proposed as a replacement for another model.” Here I argue that Fama’s complaint is too strong. The EMH can fail and there still be no model that meets Fama’s criteria. This short paper explains why and offers a more reasonable alternative to the EMH based on the viewpoint that behavioral biases, though common, are state dependent.

The Chicago Board of Options Exchange (CBOE) advocates linking variable annuity (VA) fees to its trademark VIX index in a white paper (CBOE, 2013a, b). It claims that the VIX-linked fee structure has several advantages over the traditional fixed percentage fee structure. However, the evidence presented in the white paper was largely based on non-parametric extrapolation of historical data on market prices. Our work lays out a theoretical basis with a parametric model to analyze the impact of the VIX-linked fee structure and put to test some claims from the CBOE white paper. In a Heston-type stochastic volatility setting, we jointly model the dynamics of an equity index underlying VA policyholders’ accounts and the VIX index. In this framework, we price a guaranteed minimum maturity benefit (GMMB) with VIX-linked fees. Through numerical examples, we show that the VIX-linked fee reduces the sensitivity of the insurer’s liability to market volatility, when compared to a VA with the traditional fixed fee rate.

Studies about innovation find evidence of a positive relationship between technological advancement and firm performance, in particular when the innovative effort is continuous. This paper aims to further the analysis on the duration of R&D investment at the firm level. The contribution of this study is threefold: first, we extend M'a~nez et al. [2014], Triguero et al. [2014] analysis for Spain to the Italian case: we use a panel of manufacturing and service companies, thus enlarging the view of R&D duration within the European countries. Secondly, from a methodological point of view, we employ both discrete- and continuous-time duration models, in order to test the Proportional Hazards (PH) assumption, i.e. the assumption that the hazard rate is equivalent over time across groups. Last, but not least, we assess whether a firm?s likelihood of continuing investment in R&D depends on the market power of companies. We test alternative measures for market power: the classical price-cost margin and a new proxy for the firm demand elasticity, obtained from a specific survey question. Results are in line with the hypothesis that R&D presents considerable temporal spill overs and strong persistence, even once unobserved heterogeneity is controlled for. Also, we argue that the appropriate proxy for market power is the firm demand elasticity, and we find support for the Schumpeterian hypothesis.

The momentum premium is pervasive across international markets and different asset classes; however, the drivers of this premium are yet to be established. This paper contributes to the literature by examining the association between a leading economic indicator, namely return dispersion, and the momentum premium. This association is examined across four regional momentum strategies and a global momentum strategy. We document a strong association between return dispersion and the momentum premium using both ex-post and ex-ante empirical methods. This association is robust to the inclusion of a set of control variables and an alternate specification of return dispersion. We test a conditional momentum strategy that scales the unconditional momentum strategy by the level of return dispersion and find that the conditional momentum strategy outperforms the unconditional momentum strategy in all regions. The results presented in this paper document the dynamic association between risk and the momentum premium.

Random utility models are widely used to estimate preference parameters. In the case of intertemporal choice, the two most common models are the discounted logit and one we call the discounted Luce. Due to their apparent similarity, the choice to use one model or the other seems irrelevant. In this paper, we argue that the discounted Luce is superior to the discounted logit in two significant aspects. First, in relevant applications, the discounted Luce is monotone in the sense of Apesteguia and Ballester (2018), while the discounted logit is not. Second, we show that the discounted logit is incompatible with a property of choice probabilities we call “weak stationarity”. The latter is compatible with common assumptions on the random nature of choices and is often not falsifiable with commonly available data. Fitting a logit model to weakly stationary choice probabilities biases the time-preference estimates. On the contrary, the discounted Luce can be safely used when choice probabilities are weakly stationary. An application to an existing dataset supports the theoretical results.

The online Supplement presents the proof the auxiliary Lemmas 1-6, the entire set of tables with results from the Monte Carlo and the empirical studies, and further discussion on selected topics. Full paper is available at: https://ssrn.com/abstract=2707176

This Appendix contains details on several technical points and additional empirical results. Sections in this Appendix are indexed by letters and formulas/tables/figures by a letter followed by a number (e.g. A.1). Sections and formulas/tables/figures of the paper are referenced by numbers. In Section A, we expose the estimation procedure of the models employed in the paper. In Section B, we explain the approximation used to avoid the path dependence problem in Markov Switching models. In Section C, we provide the formulas of the marginal impact of volatility on correlations for each VDCC model. In Section D, we detail the estimation method and the results behind our findings about the long-run and short-run effects of the volatility on the correlations. In Section E, we report the application to the three stock data set (and other subsets of stocks) of the method explained in Section 4.1 to evaluate the performance of the constrained RSDC models. In Section F, we include and comment the estimates of the VDCC models for the thirty stock data set used in Section 4.2. Full paper is available at: https://ssrn.com/abstract=2965487

This codebook and the related dataset and statistical code accompanies Bernard Black, Antonio Gledson de Carvalho, Vikramaditya Khanna, Woochan Kim and B. Burcin Yurtoglu, Methods for Multicountry Studies of Corporate Governance: Evidence from the BRIKT Countries, 183 Journal of Econometrics 230-240 (2014), and explains the accompanying replication dataset and Stata code. There is a link to this codebook, dataset, and code from the working paper version of this article, at http://ssrn.com/abstract=2219525, and also from an “expanded working paper” version of this article, at http://ssrn.com/abstract=2359126. Comments on the dataset, the code, and this overview are welcome; please direct them to Prof. Black at bblack@northwestern.edu.

Mean-Variance portfolios are optimal in-sample, however they tend to perform poorly out-of-sample (even worse than the 1/N naive portfolio!) We introduce a new portfolio construction method that substantially improves the Out-Of-Sample performance of diversified portfolios.The full paper is available at: http://ssrn.com/abstract=2708678.

Central banks are increasingly communicating their economic outlook in an effort to manage the public and financial market participants’ expectations. We provide original causal evidence that the information communicated and the assumptions underlying a central bank’s projection can matter for expectation formation and aggregate stability. Using a between-subject design, we systematically vary the central bank’s projected forecasts in an experimental macroeconomy where subjects are incentivized to forecast the output gap and inflation. Without projections, subjects exhibit a wide range of heuristics, with the modal heuristic involving a significant backward-looking component. Ex-Ante Rational dual projections of the output gap and inflation significantly reduce the number of subjects’ using backward-looking heuristics and nudge expectations in the direction of the rational expectations equilibrium. Ex-Ante Rational interest rate projections are cognitively challenging to employ and have limited effects on the distribution of heuristics. Adaptive dual projections generate unintended inflation volatility by inducing boundedly-rational forecasters to employ the projection and model-consistent forecasters to utilize the projection as a proxy for aggregate expectations. All projections reduce output gap disagreement but increase inflation disagreement. Central bank credibility is significantly diminished when the central bank makes larger forecast errors when communicating a relatively more complex projection. Our findings suggest that inflation-targeting central banks should strategically ignore agents’ irrationalities when constructing their projections and communicate easy-to-process information. Electronic supplementary material The online version of this article (10.1007/s10683-020-09684-6) contains supplementary material, which is available to authorized users.

Can the potential availability of unemployment insurance (UI) affect the behavior of employed workers and the duration of their employment spells? I apply a regression kink design (RKD) to address this question using linked employer-employee data from the Brazilian labor market. Exploiting the UI schedule, I find 1% higher potential benefit level increases job duration by around 0.3%. Such result is driven by the fact that higher UI decreases the probability of job quits, which are not covered by UI in Brazil. These estimates are robust to permutation tests and a number of falsification tests. I develop a simple model to assess the economic relevance of this finding. It shows that the positive effect on employment duration implies that the optimal benefit level is higher than otherwise. More importantly, the model delivers a simple welfare formula based on sufficient statistics which can be easily linked to the data. A simple calibration exercise shows that this elasticity impacts welfare with a similar magnitude to the well-known elasticity of unemployment duration to benefit level.

This Appendix contains additional empirical results with respect to the published article. In Section 1, the posterior results for the HDP parameters of the IHMS- ARMA models are presented for the U.S. GDP growth rate and inflation series. In Section 2, we report additional in-sample and forecasting results for the same series. In Section 3, some results for a different truncation choice of the number of regimes in the approximate model are reported. Full paper available at: https://ssrn.com/abstract=2965441

We propose a Markov-switching dynamic factor model to construct an index of global business cycle conditions, to perform short-term forecasts of world GDP quarterly growth in real time and to compute real-time business cycle probabilities. To overcome the real-time forecasting challenges, the model accounts for mixed frequencies, for asynchronous data publication and for leading indicators. Our pseudo real-time results show that this approach provides reliable and timely inferences of the world quarterly growth and of the world state of the business cycle on a monthly basis.

This paper finds positive evidence of return predictability and investment gains for individual corporate bonds for an extended period from 1973 to 2017. Our sample consists of both public and private company bond observations. We have implemented multiple machine learning methods and designed a Fama-Macbeth-type predictive performance evaluation. In addition to robust predictability evidence, there are four main findings. First of all, we find the lagged corporate bond market return as the most important predictor, suggesting a short-term market reversal story. Second, this paper concludes that equity information is conditionally redundant for similar public and private company bond performance. Third, a model-forecast-implied long-short strategy delivers 1.48% monthly returns and 1.4% alpha during the last two decades, which substantially drops if we do not consider private company bonds. Finally, the return predictability is mainly due to the cash flow component instead of the discount rate component.

Large-scale shocks directly affect some firms and households and indirectly affect others through general equilibrium spillovers. In this paper, I describe how researchers can directly estimate spillovers using quasi-experimental or experimental variation. I then argue that spillover estimates suffer from distinct sources of mechanical bias that standard empirical tools cannot resolve. These biases are particularly relevant in finance and macroeconomics, where multiple spillover channels and nonlinear effects are common. I offer guidance on how to detect and overcome mechanical biases. An application and several examples highlight that the suggested methods are broadly relevant and can inform policy and multiplier calculations. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Despite the growing interest in global inequality, assessing inequality trends is a major challenge becauseindividual data on income or consumption is not often available. Nevertheless, the periodic release of cer-tain summary statistics of the income distribution has become increasingly common. Hence, groupeddata in form of income shares have been conventionally used to construct inequality trends based onlower bound approximations of inequality measures. This approach introduces two potential sourcesof measurement error: first, these estimates are constructed under the assumption of equality of incomeswithin income shares; second, the highest income earners are not included in the household surveysfrom which grouped data is obtained. In this paper, we propose to deploy a flexible parametric model,which addresses these two issues in order to obtain a reliable representation of the income distributionand accurate estimates of inequality measures. This methodology is used to estimate the recent evolutionof global interpersonal inequality from 1990 to 2015 and to examine the effect of survey under-coverageof top incomes on the level and direction of global inequality. Overall, we find that item non-response atthe top of the distribution substantially biases global inequality estimates, but, more importantly, itmight also affect the direction of the trends. The authors hereby acknowledge UNU-WIDER and the project World Inequality where an earlier version of this study was published. The authors are grateful to Stephen Jenkins, Branko Milanovic, Nora Lustig, Juan Gabriel Rodriguez, Gustavo Marrero, Roy Van der Weide and participants at the UM Sustainability and Development Conference, Seventh ECINEQ Meeting, the 33th Annual Congress of the European Economic Association, and UNU-WIDER internal seminar series for helpful comments on earlier versions of this paper. Vanesa Jorda wishes to acknowledge financial support from the Ministerio de Economía y Competitividad (Project ECO2016-76203-C2-1-P).

The impact of technological progress on jobs and wages has been subject to much empirical and some theoretical work e.g. on skill-biased technical progress. Most of this literature has focused on two causal channels; the jobs-displacing substitution effect, and the job-creating demand effect. This paper shows that a further channel shapes the quantitative and qualitative effects of technical change. This is a general-equilibrium effect, depending on the interplay between the factors and sectors impacted by change. The paper integrates these three channels and derives explicit expressions for the effects of different types of technical change on wages and sectoral employment.

At the aggregate level, the evidence that deviations from purchasing power parity (PPP) are too persistent to be explained solely by nominal rigidities has long been a puzzle (Rogoff, 1996). Another puzzle from the micro price evidence of the law of one price (LOP), which is the basic building block of PPP, is that LOP deviations are less persistent than PPP deviations. To reconcile the empirical evidence, we adapt the model of behavioral inattention in Gabaix (2014, 2020) to a simple two-country sticky-price model. We propose a simple test of behavioral inattention and find strong evidence in its favor using micro price data from US and Canadian cities. Calibrating behavioral inattention with our estimates, we show that our model reconciles the two puzzles relating to the PPP and LOP. First, the PPP deviations are more than twice as persistent as PPP deviations explained only by sticky prices. Second, the LOP deviations decrease to less than two-thirds of the PPP deviations in the degree of persistence. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Using a sample of listed non-financial firms in China from 2009 to 2019, we show that financial derivatives usage increases the firms’ (discretionary) accruals earnings smoothing (artificial or intentional smoothing). We find that state ownership, overseas operations, and public scrutiny play the moderating roles between derivatives usage and earnings smoothing. Moreover, these three moderating factors have unambiguous interactions inter se: the mitigation effects of public scrutiny would be stronger for the firms owned by the state or the firms with overseas operations; and the private firms without overseas operations are the “worst-hit areas” of the earnings smoothing. In addition, our results imply that the futures might be the only type of derivatives that are used to smooth earnings. This paper also finds that the adoption of IFRS 7 and 13 in 2014 failed to mitigate the derivatives smoothing. Overall, our paper reveals the relation of derivatives usage to intentional earnings smoothing in China, which is partially contrary to the evidence in the U.S., and the detailed dynamics of the relation.

This paper proposes a reengineered and robust approach to optimal economic capital allocation, in a Liquidity-Adjusted Value at Risk (LVaR) framework, and particularly from the perspective of trading portfolios that have both long and short trading positions and disallowing both long-only positions and borrowing constraints. This paper expands previous approaches by explicitly modeling the liquidation of trading portfolios with the aid of an appropriate scaling of the multiple assets’ LVaR matrix along with GARCH-M technique to forecast conditional volatility and expected return.

The design and conduct of climate change policy necessarily confronts uncertainty along multiple fronts. We explore the consequences of ambiguity over various sources and configurations of models that impact how economic opportunities could be damaged in the future. We appeal to decision theory under risk, model ambiguity and misspecification concerns to provide an economically motivated approach to uncertainty quantification. We show how this approach reduces the many facets of uncertainty into a low dimensional characterization that depends on the uncertainty aversion of a decision-maker or fictitious social planner. In our computations, we take inventory of three alternative channels of uncertainty and provide a novel way to assess them. These include i) carbon dynamics that capture how carbon emissions impact atmospheric carbon in future time periods; ii) temperature dynamics that depict how atmospheric carbon alters temperature in future time periods; iii) damage functions that quantify how temperature changes diminish economic opportunities. We appeal to geoscientific modeling to quantify the first two channels. We show how these uncertainty sources interact for a social planner looking to design a prudent approach to the social pricing of carbon emissions. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

The ambition of this paper is to catch hidden information inside the Securities and Exchange Commission’s (SEC)13F public holding data in order to construct an equity portfolio that maximizes returns. The 13F ling data give us the quarterly stock trading decisions of included funds, but we’re not given any insight on how they made their decisions or if information has been shared between funds. To remedy this lack of knowledge, this paper used feature extraction in order to lter out the best performing funds through several criteria. We propose a method employing powerful machine learning techniques (Deep Reinforcement Learning) to try to catch the missing pieces of information behind the decision process and use them as a prediction tool to construct quarterly equity portfolios. This approach reached an annualized return of 21% with a sharpe ratio of 1.8 outperforming the S&P 500 both in returns and stability through historical backtesting.

Stochastic processes is one of the key operations research tools for analysis of complex phenomenon. This paper has a unique application to the study of mean changing models in stock markets. The idea is to enter and exit stock markets like Apple Computer and the broad S&P500 index at good times and prices (long and short). Research by Chopra and Ziemba showed that mean estimation was far more important to portfolio success than variance or co-variance estimation. The idea in the stochastic process model is to determine when the mean changes and then reverse the position direction. This is applied to Apple Computer stock in 2012 when it rallied dramatically then had a large fall and Apple Computer and the S&P500 in the 2020 Covid-19 era. The results show that the mean changing model greatly improves on a buy and hold strategy even for securities that have has large gains over time but periodic losses which the model can exploit. This type of model is also useful to exit bubble-like stock markets and a number of these in the US, Japan, China and Iceland are described. An innovation in this paper is the exit entry long short feature which is important in financial markets.

Bartik regressions use locations' differential exposure to nationwide sector-level shocks as an instrument to estimate the effect of a location-level treatment on an outcome. In the canonical Bartik design, locations' differential exposure to industry-level employment shocks are used as an instrument to measure the effect of their employment evolution on their wage evolution. Some recent papers studying Bartik designs have assumed that the sector-level shocks are exogenous and all have the same expectation. This second assumption may sometimes be implausible. For instance, there could be industries whose employment is more likely to grow than that of other industries. We replace that second assumption by parallel trends assumptions. Under our assumptions, Bartik regressions identify weighted sums of location-specific effects, with weights that may be negative. Accordingly, such regressions may be misleading in the presence of heterogeneous effects, an issue that was not present under the assumptions maintained in previous papers. Estimating the weights attached to Bartik regressions is a way to assess their robustness to heterogeneous effects. We also propose an alternative estimator that is robust to location-specific effects. Finally, we revisit two applications. In both cases, Bartik regressions have fairly large negative weights attached to them. Our alternative estimator is substantially different from the Bartik regression coefficient in one application.

Standard Fama-French-Carhart models define ‘winners’ as those funds that generate highest excess returns given the factor risks involved; however they do not provide information on whether such winners are outperforming their prospectus benchmark or their peer-group. In addition, existing literature relying on these models by and large does not find evidence of persistence in performance. In this paper, we argue that true (unbiased) winners should be defined differently: they are funds placed in the top-ranking group (quartile, quintile, etc.), which generate the highest factor-risk-adjusted performance relative to the benchmark and the peer-group simultaneously. We prove in this paper that using this definition and selecting true winner funds based on benchmark- and peer-group-adjusted alphas jointly lead to better performance than selecting the funds using either of these two alphas separately. Utilising both adjustments at the same time results in a strong predictive ability, leading to a selection of funds that persist in performance: our true winner funds have statistically significantly superior benchmark-adjusted alphas, peer-group adjusted alphas and Sharpe ratios one-year-ahead, which are significantly different from those generated by the true loser funds. The results are robust to extended investment horizon, and alpha estimation method, and they are not driven by outliers, size of fund-sorts, or any particular period within our sample.

This paper revisits the issue of estimating the returns to schooling within a framework that allows multiple unobserved skills with potentially time-varying prices where both skills and prices are possibly correlated with schooling. A common approach to addressing the problem of ability bias emanating from the unobservability of skills is to augment the earnings-schooling regression with various measures of cognitive and non-cognitive ability as proxies for these skills. While the adequacy of such measures has long been questioned, a rigorous assessment of their effectiveness in estimating the magnitude of ability bias has received relatively less attention. This paper undertakes a formal evaluation of the proxy approach by adopting a methodology that models the skills and their prices using an unobserved factor structure in which the factor loadings represent the latent skills and the common factors their associated prices. Our factor model approach allows consistent estimation of the returns to schooling without the need to rely on proxies thereby providing a flexible test bed for quantifying the contribution of the proxies to the aggregate least squares bias. The factor model estimators may also be viewed as implicitly estimating the measurement error inherent in the proxies. Our empirical results using panel data from the NLSY79 show that the estimated ability bias lies between 39-51% for our most general specification. A bias decomposition analysis indicates that the commonly used proxies can explain very little of the estimated bias. Direct tests for the viability of the proxies further confirm their inadequacy in capturing the underlying latent skills.

This paper empirically examines the relationship between economic growth and structural change in India over recent decades. It first estimates panel regressions with state-level data, to examine the impact of structural change on growth and vice versa. Bidirectional causality is found, which holds across different specifications and estimation methods: each variable positively impacts the other, in a positive feedback loop. The second part of the analysis relaxes assumptions of constant impacts across states and over time, estimating time-varying auto-regressive models for each state and each pair of variables. The impacts of structural change and growth on each other seem to vary across different states, as well as over time, and are differentially affected by state per capita income levels and national growth rates. Similar variability across states and over time is found in an alternative method: labor productivity decompositions using employment data. Some possible reasons for these differences are discussed. The analysis suggests that national economic policy formulation may benefit from closer consideration of the structural differences across states, and the resulting differences in growth responses. The paper concludes with some possible implications for post-pandemic recovery.

This paper analyzes the use of Reinforcement Learning in trading Agricultural ETFs. The first section of the paper examines the performance of using a naive trading strategy to be used in a later comparison with Reinforcement Learning strategies. Here, returns were very volatile, with agents both severely under- and over-performing index benchmarks. The second part of this paper examines the use of the Monte Carlo Policy Gradient algorithm in trading. Finally, results of performing Dynamic Time Warping are presented for side-by-side comparisons between naive trading and trading using reinforcement learning algorithms. Overall, reinforcement learning produces more volatile results with a positive Sharpe ratio as compared to naive trading, with negative average returns.

Corporate FX risk management has gained complexity with an increased number of currencies involved and varying correlations among them. Existing literature has highlighted the need to account for cross-currency correlations when optimizing hedge ratios for portfolio management (Dowd, 1999). In this paper, we propose a Value-at-Risk (VaR) based model to estimate the optimal hedge ratio for a multi-national corporate that aims to minimize the cost of hedging at a given tolerance level of expected loss arising out of FX movement. The paper illustrates both parametric and historical methods of VaR estimation at a portfolio level as the first step in risk management. As a second step, an efficient-frontier is derived based on the expected VaR level at various hedge ratios and compared with associated hedge cost. The benefits of this approach include: identification of net exposures after correlations among currencies are accounted for in order to avoid duplication of hedges, and condensation of the parameters governing hedging decision into a single, intuitively-appealing number. The paper also highlights the need to frequently update the model’s assumptions as currency correlations and corporate exposures remain dynamic. JEL Classification Codes: C10, F31, G32, M20.

The paper deals with the problem of determinants of the auction prices of the works of art. A special attention is given to the attributes of works of art i.e.: the name of artist, time of creation, format (size), theme, technique, signature and their influence on the level of auction prices. The author of the paper uses statistical methods to verify empirically the relationship between variables. The dependences have been explained by implementation of three methods ie.: analysis of variance ANOVA, multiple regression models and classification trees, with the use of the computer program STATISTICA. The analysis is based on the cross-section data on art auction results of several auction sessions in Poland. The empirical studies have shown that name of artist, year of creation and technique determine the auction prices of works of art. The influence of technique and signature is not important statistically.

We propose a new approach to automated market making (AMM) by synchronizing two constant function market makers (CFMM). Our methodology combines the advantages of each individual CFMM and reduces the downside risk for liquidity providers (LP). The approach can be extended to any combination of AMMs. The application of our technique described in this paper is suitable for assets with high volatility that can deviate from the entry price for LPs. The deviation risk, compared to a buy-and-hold strategy and often referred to as impermanent loss can counterbalance the benefit of liquidity mining, that is the earning of associated trading fees. We show in this paper how our approach provides a way to largely limit the impact of such risk and compare the behavior of the new AMM to the well known exchanges, such as Uniswap and Balancer.

Problem definition: This paper investigates the threshold effects on the consideration set formation for consumers and the associated pricing decisions for firms. Academic/practical relevance: In the class of random utility maximization choice models, consumers choose the alternative with the largest utility after resolving utility uncertainties for all options. However, in practice, consumers may not inspect all available alternatives due to bounded rationality or search cost. Methodology: This paper incorporates bounded rationality into consumer choice behavior: in the first stage, a representative consumer forms her consideration set by removing alternatives whose nominal utility is lower than the largest by a threshold; in the second stage, she examines all alternatives in her consideration set and chooses the one with the highest realized utility. Results: For the Gumbel-distributed random utilities, we derive the two-stage threshold multinomial logit model with a consideration set. For the pricing problem, we show the quasi-same-markup/same-utility policy is optimal: high-cost products charge the prices such that their profit markups are the same, and low-cost products charge prices such that their nominal utilities are the same. For the price competition, there may exist zero, one, two, or infinite Nash equilibria, depending on the magnitude of the threshold effects. Managerial implications: Our analysis shows that the consideration set and bounded rationality play an important role in consumer choice behavior, so they should be taken into account in firms’ decision making.

This paper establishes bounds on the performance of empirical risk minimization for large-dimensional linear regression. We generalize existing results by allowing the data to be dependent and heavy-tailed. The analysis covers both the cases of identically and heterogeneously distributed observations. Our analysis is nonparametric in the sense that the relationship between the regressand and the regressors is assumed to be unknown. The main results of this paper indicate that the empirical risk minimizer achieves the optimal performance (up to a logarithmic factor) in a dependent data setting.

To estimate the spatial spillover effect of China’s carbon emissions trading (CET) launched in 2013 on carbon emissions reduction, this paper conducts a quasi-natural experiment with the combination of difference-in-differences method (DID) and spatial doberman model (SDM). Moreover, this paper decomposes the policy effect into direct part and spatial spillover part, further analyzes the mechanism from the perspective of energy use, energy structure and energy efficiency. The empirical results show that: (1) From 2013 to 2016, carbon emissions in pilots achieve reduction significantly through CET. Both the results of the robustness test and the endogenous analysis support the above conclusion. (2) Carbon emissions in neighbouring region of pilots are also reduced, which proves the existence of spatial spillover effects from CET. Compared with direct part, the spatial spillover part accounted for smaller proportion in spatial carbon emissions reduction effect. (3) The emission reduction effect of CET continues to increase over the long term, while the spatial spillover effect gradually decreases as distance increases. (4) The decline of fossil energy use is main path to reduce carbon emissions, while the CET fails to increase the use of clean energy, optimize energy use structure or improve energy efficiency.

This paper takes a critical look at the effect of firm size on the profitability of Ghanaian Insurance brokerage firms. Specifically, the paper examined the effect of firm size (measured by total assets) on firm profitability (measured by ROA and ROE) from a lagged perspective (i.e. the lagged effect of size), non-linear perspective, and across quantiles, using fixed effects, random effects, robust and PCSE estimation techniques. Analyzing a unique data of 64 insurance brokers from 2007 to 2015, the findings show that firm size exhibited a significant and positive short term effect on firm profitability but the relationship turned negative in the long term showing a non-linear relationship of size on profitability, with an inflection point above the mean firm size. However, the non-linear effect was evident in the 50th and above percentile of brokers but not in the lower quantiles. The lagged values of size also significantly affected firm profitability but it was not as pronounced as the short-term effects. The study recommends larger Ghanaian insurance brokerage firms take a staggered and reflective approach in its growth measures.

Summary: Kermack and McKendrick’s SIR compartment model (1927) has been widely used to analyze various epidemic phenomena including the current COVID-19 pandemic. Unfortunately, this model is of limited use if the duration of the epidemic prolongs. This paper uses a slightly modified compartment model, the SEIR(D) model, to analyze the time-varying transmission dynamics of the COVID-19 epidemic in Korea in multiple stages of its development. This multi-stage estimation of the model parameters offers a better model fit and shows how the COVID-19’s transmission dynamics change over time, primarily depending on, the effectiveness of the public health authority’s non-pharmaceutical interventions (NPIs). Method: This paper uses the SEIR(D) compartment model to simulate and estimate the parameters for three distinctive stages of the COVID-19 epidemic in Korea, using a compiled COVID-19 dataset from Feb. 18, 2020 to Feb. 08, 2021. The paper identifies three distinct stages of the COVID-19 epidemic in Korea, conduct multi-stage estimations of model parameters, and carefully infer context-dependent meaning of the estimation results to better understand the unique characteristics of the transmission dynamics of the COVID-19 in each stage. Findings: The original SIR compartment model may produce a poor and even misleading result if it is used to cover the entire period of the epidemic, because underlying assumptions behind this model - i.e., homogeneity of the susceptible population and invariant parameters - are easily defied by the real-world complexity. However, if we use a carefully calibrated model in distinctive stages of the COVID-19 epidemic, we can find useful insights that we can use to evaluate the effectiveness of the government’s non-pharmaceutical interventions over time.