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This study investigates how commercial paper rates respond to the innovations in stock market risk premiums. The unrestricted vector autoregression (VAR) analysis of monthly data from 1997:1 to 2012:M6 shows that the changes in the one-, two-, and three-month non-financial and financial commercial paper rates positively respond to the innovations in the excess returns on the CRSP value weighted index. The response is especially strong during the first few months following shocks to stock market risk premiums. The Granger-causality test results show the changes in commercial paper rates can be predicted by the excess returns on the CRSP value weighted index. The findings from this study provide evidence that there is a link between equity market and commercial paper market.

Understanding how prior outcomes affect risk attitudes is critical for the study of choice under uncertainty. A large literature documents the influence of prior losses on subsequent risk attitudes. The findings appear contradictory: some studies find that people become more risk seeking after a loss, whereas others assert the opposite -- that they become more risk averse. In this paper, we show that these seemingly inconsistent findings can be explained by individuals' differential responses to realized versus paper losses. Following a realized loss, individuals avoid risk; if the loss has not been realized -- a paper loss -- individuals are more likely to chase their losses and take on even greater risk. We provide support for our framework using existing data and across two experiments. We also show that giving individuals flexibility in choosing when to realize losses can lead to lower earnings in environments where loss-chasing decreases expected returns. Drawing a distinction between paper and realized losses reconciles prior findings, and has important implications for contract design and optimal monitoring.

This paper adumbrates a theory of what might be going wrong in the monetary SVAR literature and provides supporting empirical evidence. The theory is that macroeconomists may be attempting to identify structural forms that do not exist, given the true distribution of the innovations in the reduced-form VAR. The paper shows that this problem occurs whenever (1) some innovation in the VAR has an infinite-variance distribution and (2) the matrix of coefficients on the contemporaneous terms in the VAR’s structural form is nonsingular. Since (2) is almost always required for SVAR analysis, it is germane to test hypothesis (1). Hence, in this paper, we fit a -stable distributions to VAR residuals and, using a parametric-bootstrap method, test the hypotheses that each of the error terms has finite variance.

The paper discusses alternative Research Assessment Measures (RAM), with an emphasis on the Thomson Reuters ISI Web of Science database (hereafter ISI). The various ISI RAM that are calculated annually or updated daily are defined and analysed, including the classic 2-year impact factor (2YIF), 5-year impact factor (5YIF), Immediacy (or zero-year impact factor (0YIF)), Eigenfactor score, Article Influence, C3PO (Citation Performance Per Paper Online), h-index, Zinfluence, and PI-BETA (Papers Ignored - By Even The Authors). The ISI RAM data are analysed for 8 leading econometrics journals and 4 leading statistics journals. The application to econometrics can be used as a template for other areas in economics, for other scientific disciplines, and as a benchmark for newer journals in a range of disciplines. In addition to evaluating high quality research in leading econometrics journals, the paper also compares econometrics and statistics, alternative RAM, highlights the similarities and differences in alternative RAM criteria, finds that several ISI RAM capture similar performance characteristics for the leading econometrics and statistics journals while the new PI-BETA criterion is not highly correlated with any of the other ISI RAM, and hence conveys additional information regarding ISI RAM, highlights major research areas in leading journals in econometrics, and discusses some likely future uses of RAM.

This paper makes an econometric testing of one-minute paper used as a tool to manage and assess instruction in my statistics class. One of our findings is that the one minute paper when I have tested it by using an OLS estimate in a controlled Vs experimental design framework is found to statistically significant and effective in enhancing students’ knowledge. It is found to be equally effective when I have tested it by using a seemingly unrelated regression that allows the error terms to be correlated across separate but related regressions. This is irrespective of students’ ability levels as is measured by GPA in both cases.

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)

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.

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.

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.

In this paper, we propose a DSGE model with the term structure of interest rates drawing on the framework introduced by Andrés et al. (2004) and Marzo et al. (2008). In particular, we reproduce segmentation in financial markets by introducing bonds of different maturities and bond adjustment costs non-zero at the steady state, introducing a structural liquidity frictions among bonds with different maturities: agents are assumed to pay a cost whenever they trade bonds. As a result, the model is able to generate a non-zero demand for bonds of different maturities, which become imperfect substitutes, due to differential liquidity conditions. The main properties of the model are analysed through both simulation and estimation exercises. The importance of the results are twofold. On one hand, the calibrated model is able to replicate the stylized facts regarding the yield curve and the term premium in the US over the period 1987:3-2011:3, without compromising its ability to match macro dynamics. On the other hand, the estimation, besides providing an empirical support to the theoretical setting, highlights the potentialities of the model to analyze the term premium in a microfounded macro framework. The results match very closely the behavior of actual yields, reflecting the recent activity of the Fed on longer maturities bonds.

The paper documents elements of work on the dynamic stochastic general equilibrium (DSGE) SOEPL model that has been carried out in recent years at the National Bank of Poland. In 2009 a new version of the model was developed (called SOEPL−2009) which in 2010 is to support an econometric model and experts’ forecasts in mid-term forecasting of inflation and economic activity. The paper consists of three basic parts. The first part is introductory and briefly outlines the development of macroeconometric methods which brought about the creation of new-keynesian models specified within the dynamic stochastic general equilibrium approach. The remaining two parts of the paper report specification, estimation results and some properties of the SOEPL−2009 DSGE model.

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.

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.

In this paper we derive analytic implicit form conditions for the qualitative analysis of government spending multipliers and the optimal level of government spending in presence of non-separability between private and public components of aggregate demand. Using the simplest neo-classical flexible price model with no capital accumulation, we show that Edgeworth dependence is not a suitable condition to automatically assess the signs of the consumption and income multipliers, for which a more complex analysis must be carried out. We propose a detailed investigation of the form and the characteristics of the involved utility functions, which are crucial to such evaluation. We also show that if Edgeworth complementarity is strong enough, a public spending stimulus can raise at the same time private consumption and real activity. In order to reconcile our general framework with existing literature, we discuss recent examples of non-separable functional forms from the standpoint of our results, and argue that their consistency relies on specific assumptions about steady- state points.

Amid controversies surrounding aid effectiveness, an increasing number of empirical studies find support for the idea that aid can spur growth and that the aid-growth relationship is nonlinear. Lensink and White propose a model to illustrate the possible existence of what has been labeled an "aid Laffer curve." This short paper highlights the model's weaknesses and suggests that the model does not fulfill the purpose of illustrating the possible existence of negative returns to aid.

This study revisits the sectoral shifts hypothesis for the US for the period 1948 to 2011. A quantile regression approach is employed in order to investigate the asymmetric nature of the relationship between sectoral employment and unemployment. Significant asymmetries emerge. Lilien’s dispersion index is significant only for relatively high levels of unemployment and becomes insignificant for low levels suggesting that reallocation effects unemployment only when the latter is relative high. More job reallocation is associated with higher unemployment.

Matched employer-employee (panel) data sets are gaining increasing importance in the analysis of labour markets. In collaboration with Statistics Austria we recently initiated the set up of a matched employer-employee panel data set for Austria, which covers the years 2002-2005. The aim of the paper is to introduce the data set to a broader audience. We first present the set up of the panel data, indicating in more detail the data sources and matching procedure underlying the matched employer-employee data set for Austria. In a second step we show descriptive statistics of the main variables included in our data set. These various statistics encompass three levels of analysis: the aggregate level (i.e. the entire sample), firm level and individual (employee) level.

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.

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.

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.

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.

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.

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.

We provide the first at-scale estimate of electric vehicle (EV) home charging. Previous estimates are either based on surveys that reach conflicting conclusions, or are extrapolated from a small, unrepresentative sample of households with dedicated EV meters. We combine billions of hourly electricity meter measurements with address-level EV registration records from California households. The average EV increases overall household load by 2.9 kilowatt-hours per day, less than half the amount assumed by state regulators. Our results imply that EVs travel 5,300 miles per year, under half of the US fleet average. This raises questions about transportation electrification for climate policy. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

In this paper, an extension of the indirect inference methodology to semiparametric estimation is explored in the context of censored regression. Motivated by weak small-sample performance of the censored regression quantile estimator proposed by Powell (J Econom 32:143–155, 1986a), two- and three-step estimation methods were introduced for estimation of the censored regression model under conditional quantile restriction. While those stepwise estimators have been proven to be consistent and asymptotically normal, their finite sample performance greatly depends on the specification of an initial estimator that selects the subsample to be used in subsequent steps. In this paper, an alternative semiparametric estimator is introduced that does not involve a selection procedure in the first step. The proposed estimator is based on the indirect inference principle and is shown to be consistent and asymptotically normal under appropriate regularity conditions. Its performance is demonstrated and compared to existing methods by means of Monte Carlo simulations.

Confronted by increasingly tight budgets and a broad range of alternative options, policy makers need empirical methods to evaluate the effectiveness of policies aimed at supporting the diffusion of renewable energy sources (RES). Rigorous empirical studies of renewable energy policy effectiveness have typically relied on panel data models to identify the most effective mechanisms. A common characteristic of some of these studies, which has important econometric implications, is that they assume that the contribution of RES to total electricity generation will be stationary around a mean. This paper reviews such assumptions and rigorously tests the time series properties of the contribution of RES in the energy mix for the presence of a unit root. To that end, we use both individual and panel unit root tests to determine whether the series exhibit non-stationary behavior at the country level as well as for the panel as a whole. The analysis, applied to a panel of 19 OECD countries over the period 1990-2012, provides strong evidence that the time series of the renewable share of electricity output are not stationary in 17 of the 19 countries examined. This finding has important implications for energy policy assessment and energy policy making, which are discussed in the paper.

This paper provides an analytical overview of the most widely used capital flow datasets. The paper is written as a guide for academics who embark on empirical research projects and for policymakers who need timely information on capital flow developments to inform their decisions. We address common misconceptions about capital flow data and discuss differences between high-frequency proxies for portfolio flows. In a nowcasting “horse race” we show that high-frequency proxies have significant predictive content for portfolio flows from the balance of payments (BoP). We also construct a new dataset for academic use, consisting of monthly portfolio flows broadly consistent with BoP data.

This paper investigates the dynamic connectedness of random shocks to housing prices between the 50 U.S. states and the District of Columbia. The paper implements a standard vector autoregressive (VAR) model as well as three VAR models with shrinkage effects - Elastic Net, Lasso, and Ridge VAR models. The transmission of random shocks on a regional basis flows from Southern states to Western states to Midwestern states to Northeastern states. Since VAR models generally confront parameter values between zero and one, the Elastic Net and Lasso VAR models perform the best since the penalty involves the absolute value rather than he squared value as in the Ridge VAR model. Our results have important implications for investors and policymakers.

We give a thorough analytic characterization of a large class of sticky-price models where the firm’s price setting behavior is described by a generalized hazard function. Such a function provides a tractable description of the firm’s price setting behavior and allows for a vast variety of empirical hazards to be fitted. This setup is microfounded by random menu costs as in Caballero and Engel (1993) or, alternatively, by information frictions as in Woodford (2009). We establish two main results. First, we show how to identify all the primitives of the model, including the distribution of the fundamental adjustment costs and the implied generalized hazard function, using the distribution of price changes or the distribution of spell durations. Second, we derive a sufficient statistic for the aggregate effect of a monetary shock: given an arbitrary generalized hazard function, the cumulative impulse response to a once-and-for-all monetary shock is given by the ratio of the kurtosis of the steady-state distribution of price changes over the frequency of price adjustment times six. We prove that Calvo’s model yields the upper bound and Golosov and Lucas’ model the lower bound on this measure within the class of random menu cost models. 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 novel district-level dataset of the human development index, this paper studies the evolution of regional disparities in Indonesia over the 2010-2018 period. In particular, the paper evaluates the role of spatial dependence on the speed of regional convergence. The main findings are threefold. First, regional disparities have been decreasing in the overall index of human development as well as in most of its components. Second, there are considerable differences in the speed of convergence in the components of the human development index. Specifically, education-related components have tended to accelerate the speed of regional convergence, while life expectancy and expenditure components have tended to decelerate it. Third, there is an increasing degree of spatial dependence that is associated with the decreasing regional disparities. Moreover, results derived from spatial convergence regressions indicate that the performance of neighboring regions has a significant effect on the speed of regional convergence. Keywords

This paper develops a new method informed by data and models to recover information about investor beliefs. Our approach uses information embedded in forward-looking asset prices in conjunction with asset pricing models. We step back from presuming rational expectations and entertain potential belief distortions bounded by a statistical measure of discrepancy. Additionally, our method allows for the direct use of sparse survey evidence to make these bounds more informative. Within our framework, market-implied beliefs may differ from those implied by rational expectations due to behavioral/psychological biases of investors, ambiguity aversion, or omitted permanent components to valuation. Formally, we represent evidence about investor beliefs using a novel nonlinear expectation function deduced using model-implied moment conditions and bounds on statistical divergence. We illustrate our method with a prototypical example from macro-finance using asset market data to infer belief restrictions for macroeconomic growth rates. 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 extent to which like-with like marry is particularly important for inequality as well as for the outcomes of children that result from the union. In this paper we discuss approaches to the measurement of changes in assortative mating. We derive two key conditions that a well-defined measure should satisfy. We argue that changes in assortativeness should be interpreted through a structural model of the marriage market; in particular, a crucial issue is how they relate to variations in the economic surplus generated by marriage. We propose a very general criterion of increase in assortativeness, and show that almost all indices used in the literature are implied by our criterion with one notable exception, that moreover violates one of our conditions. Finally, we use our approach to evaluate the evolution of assortative matching in the US over the last decades, and conclude that assortative matching has increased, particularly at the top of the education distribution. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

We investigate the behaviour of cryptocurrencies' return data. Using return data for bitcoin, ethereum and ripple which account for over 70% of the cyrptocurrency market, we demonstrate that $\alpha$-stable distribution models highly speculative cryptocurrencies more robustly compared to other heavy tailed distributions that are used in financial econometrics. We find that the Maximum Likelihood Method proposed by DuMouchel (1971) produces estimates that fit the cryptocurrency return data much better than the quantile based approach of McCulloch (1986) and sample characteristic method by Koutrouvelis (1980). The empirical results show that the leptokurtic feature presented in cryptocurrencies' return data can be captured by an ${\alpha}$-stable distribution. This papers covers predominant literature in cryptocurrencies and stable distributions., Comment: LaTeX MikTex 2.9 Texworks, 13 pages, 10 figures (in total). Paper presented at The 4th PKU-NUS International Conference in Quantitative Finance and Economics, 2019

This paper models the dynamics of Japanese government bond (JGB) nominal yields using daily data. Models of government bond yields based on daily data, such as those presented in this paper, can be useful not only to investors and market analysts, but also to central bankers and other policymakers for assessing financial conditions and macroeconomic developments in real time. The paper shows that long-term JGB nominal yields can be modeled using the short-term interest rate on Treasury bills, the equity index, the exchange rate, commodity price index, and other key financial variables.

This paper evaluates how adaptive learning agents weight different pieces of information when forming expectations with a recursive least squares algorithm. The analysis is based on a renewed and more general non-recursive representation of the learning algorithm, namely, a penalized weighted least squares estimator, where a penalty term accounts for the effects of the learning initials. The paper then draws behavioral implications of alternative specifications of the learning mechanism, such as the cases with decreasing, constant, regime-switching, adaptive, and age-dependent gains, as well as practical recommendations on their computation. One key new finding is that without a proper account for the uncertainty about the learning initial, a constant-gain can generate a time-varying profile of weights given to past observations, particularly distorting the estimation and behavioral interpretation of this mechanism in small samples of data. In fact, simulations and empirical estimation of a Phillips curve model with learning indicate that this particular misspecification of the initials can lead to estimates where inflation rates are less responsive to expectations and output gaps than in reality, or “flatter” Phillips curves.

The present paper develops a new Instrumental Variables (IV) estimator for spatial, dynamic panel data models with interactive effects under large N and T asymptotics. For this class of models, the only approaches available in the literature are based on quasi-maximum likelihood estimation. The approach put forward in this paper is appealing from both a theoretical and a practical point of view for a number of reasons. Firstly, the proposed IV estimator is linear in the parameters of interest and it is computationally inexpensive. Secondly, the IV estimator is free from asymptotic bias. In contrast, existing QML estimators suffer from incidental parameter bias, depending on the magnitude of unknown parameters. Thirdly, the IV estimator retains the attractive feature of Method of Moments estimation in that it can accommodate endogenous regressors, so long as external exogenous instruments are available. The IV estimator is consistent and asymptotically normal as N, T go to infinity, with N/T^2 going to 0 and T/N^2 tending to 0. The proposed methodology is employed to study the determinants of risk attitude of banking institutions. The results of our analysis provide evidence that the more risk-sensitive capital regulation that was introduced by the Basel III framework in 2011 has succeeded in influencing banks’ behavior in a substantial manner.

We study the effects of parameter uncertainty prompted by structural breaks. In our model, agents respond differently to uncertainty prompted by regime shifts in shock processes than they react to comparable perceived increases in shock volatility. The magnitude of the response to an increase in uncertainty about TFP associated with a structural break is greater than that of a response to a comparable perceived rise in volatility. This is because lifetime utility varies more when shocks shift beliefs and perceived wealth. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Background: An ongoing outbreak of a novel coronavirus (COVID-19) pneumonia has spread to many parts of the world generating concerns about the possibility of an extensive pandemic. However, due to the limited emerging understanding of the new virus and its transmission mechanisms, the results are largely inconsistent across studies. Methods: This paper proposed a quarantined-susceptible-exposed-infectious-resistant (QSEIR) model which considers the unprecedented strict quarantined measures in almost the whole China to resist the epidemic. We estimated model parameters from published information with statistical method and stochastic simulation, we found the parameters that achieved the best simulation test result. The next stage involved quantitative predictions of future epidemic developments based on different containment strategies with QSEIR model, focused on the sensitivity of the outcomes to different parameter choices in mainland China. Findings: The main results are as follows. If the strict quarantined measures are being retained, the peak value of confirmed cases would be in the range of [52438, 64090] and the peak date would be expected in the range February 7 to February 19, 2020. During March 18-30, 2020, the epidemic would be totally controlled. The end date would be in the period from August 20 to September 1, 2020. With 80% probability, our prediction on the peak date was 4 days ahead of the real date, the prediction error of the peak value is 0.43%, both estimates are much closer to the observed values compared with published studies. The sensitive analysis indicated that the quarantine measures (or with vaccination) is the most effective containment strategy to control the epidemic, followed by measures to increase the cured rate (like finding special medicine). The long-term simulation result and sensitive analysis in mainland China showed that the QSEIR model is stable and can be empirically validated. It is suggested that the QSEIR model can be applied to predict the development trend of epidemic in other regions or countries in the world. In mainland China, the quarantine measures can't be relaxed before the end of March, 2020. China can fully resume production with the appropriate anti-measures beginning in early April, 2020. The significance of this study for other countries now facing the epidemic outbreaks is that they should act more decisively and take in time quarantine measures though it may have negative short-term public and economic consequences. Interpretation: Parameter estimation is the most important part in the kind of SEIR model (Cao et al., 2020b). The paper illustrated the method to generate the parameter estimations. Given the data limitations, there were 20% errors in the simulation tests. With the improvement of data quality and more data, variable parameters can be estimated and the forecasting accuracy of the model can be enhanced. Funding Statement: The 2019 Chinese Government Scholarship and National Natural Science Foundation of China under Grants No. 71874184 and No. 71988101. Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: Missing.

This paper develops a normative approach to the measurement of ex-ante inequality of opportunity in a multidimensional setting, i.e., when the individual outcome is represented by a multidimensional variable. We characterize three classes of social welfare functions, all endorsing ex ante compensation but each of them reflecting a specific reward principle: (1) utilitarian, (2) agnostic and (3) averse. The first class is implemented via generalized Lorenz Dominance applied to each attribute separately. The agnostic and inequality averse classes are implemented by a welfarist Lorenz ordering, namely, of type-aggregate utilities. In the case of inequality-averse class, utility functions are submodular, hence capturing the dependence between attributes. We also develop normative inequality indices (Atkinson, 1970; Kolm 1969; Sen, 1973) for the classes of welfare functions and study their properties. Finally, we propose an empirical applications of the methods developed in the paper: by using the National Longitudinal Study of Adolescent to Adult Health (Add Health) we evaluate inequality of opportunity in U.S. for the case of three dimensions of individual outcomes, namely, education, health and income.

Business cycles are costlier and stabilization policies could be more beneficial than widely thought. This paper introduces a new test to show that all business cycles are asymmetric and resemble “mini-disasters.” By this we mean that growth is pervasively fat-tailed and non-Gaussian. Using long-run historical data, we show empirically that this is true for advanced economies since 1870. Focusing on peacetime eras, we develop a tractable local projection framework to estimate consumption growth paths for normal and financial-crisis recessions. Introducing random coefficient local projections (RCLP) we get an easy and transparent mapping from the estimates to a calibrated simulation model with disasters of variable severity. Simulations show that substantial welfare costs arise not just from the large rare disasters, but also from the smaller but more frequent mini-disasters in every cycle. On average, and in post-WW2 data, even with low risk aversion, households would sacrifice about 15 percent of consumption to avoid such cyclical fluctuations. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

This paper emphasizes the need for understanding the interdependencies between the real and financial sides of the economy in macroeconomic models. While the real side of the economy is generally well explained in macroeconomic models, the financial side and its interaction with the real economy remains poorly understood. This paper makes an attempt to model the interdependencies between the real and financial sides of the economy in Denmark while adopting a stock-flow consistent approach. The model is estimated using Danish data for the period 1995–2016. The model is simulated to create a baseline scenario for the period 2017– 30, against which the effects of two standard shocks (fiscal shocks and interest rate shocks) are analyzed. Overall, our model is able to replicate the stylized facts, as will be discussed. While the model structure is fairly simple due to different constraints, the use of the stockflow approach makes it possible to explain several transmission mechanisms through which real economic behavior can affect the balance sheets, and at the same time capture the feedback effects from the balance sheets to the real economy. Finally, we discuss certain limitations of our model.

We develop and implement asymptotic theory to conduct inference on continuous-time asset pricing models using individual equity returns sampled at high frequencies over an increasing time horizon. We study the identification and estimation of risk premia for the continuous and jump components of risks. Our results generalize the Fama-MacBeth two-pass regression approach from the classical discrete-time factor setting to a continuous-time factor model with general dynamics for the factors, idiosyncratic components and factor loadings, while accounting for the fact that the inputs of the second-pass regression are themselves estimated in the first pass. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

We measure the role of firm heterogeneity in counterfactual predictions of monopolistic competition trade models without parametric restrictions on the distribution of firm fundamentals. We show that two bilateral elasticity functions are sufficient to nonparametrically compute the counterfactual aggregate impact of trade shocks, and recover changes in economic fundamentals from observed data. These functions are identified from two semiparametric gravity equations governing the impact of bilateral trade costs on the extensive and intensive margins of firm-level exports. Applying our methodology, we estimate elasticity functions that imply an impact of trade costs on trade flows that falls when more firms serve a market because of smaller extensive margin responses. Compared to a baseline where elasticities are constant, firm heterogeneity amplifies both the gains from trade in countries with more exporter firms and the welfare gains of European market integration in 2003-2012. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Most empirical papers in economics focus on two aspects of their results: whether the estimates are statistically significantly different from zero and the interpretation of the point estimates. This focus obscures important information about the implications of the results for economically interesting hypotheses about values of the parameters other than zero, and in some cases, about the strength of the evidence against values of zero. This limitation can be overcome by reporting confidence intervals for papers' main estimates and discussing their economic interpretation.

Although typically overlooked, many purchase datasets exhibit a high incidence of products with zero sales. We propose a new estimator for the Random-Coefficients Logit demand system for purchase datasets with zero-valued market shares. The identification of the demand parameters is based on a pairwise-differencing approach that constructs moment conditions based on differences in demand between pairs of products. The corresponding estimator corrects non-parametrically for the potential selection of the incidence of zeros on unobserved aspects of demand. The estimator also corrects for the potential endogeneity of marketing variables both in demand and in the selection propensities. Monte Carlo simulations show that our proposed estimator provides reliable small-sample inference both with and without selection-on- unobservables. In an empirical case study, the proposed estimator not only generates different demand estimates than approaches that ignore selection in the incidence of zero shares, it also generates better out-of-sample fit of observed retail contribution margins. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

In standard models, economic activity fluctuates symmetrically around a “natural rate” and stabilization policies can dampen these fluctuations but do not affect the average level of activity. An alternative view—labeled the “plucking model” by Milton Friedman—is that economic fluctuations are drops below the economy’s full potential ceiling. If this view is correct, stabilization policy, by dampening these fluctuations, can raise the average level of activity. We show that the dynamics of the unemployment rate in the US display a striking asymmetry that strongly favors the plucking model: increases in unemployment are followed by decreases of similar amplitude, while the amplitude of the increase is not related to the amplitude of the previous decrease. We develop a microfounded plucking model of the business cycle. The source of asymmetry in our model is downward nominal wage rigidity, which we embed in an explicit search model of the labor market. Our search framework implies that downward nominal wage rigidity is consistent with optimizing behavior and equilibrium. In our plucking model, stabilization policy lowers average unemployment and thereby yields sizable welfare gains. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

This paper builds a novel database on the effects of macro-prudential policy drawing from 58 empirical studies, comprising over 6,000 results on a wide range of instruments and outcome variables. It encompasses information on statistical significance, standardized magnitudes, and other characteristics of the estimates. Using meta-analysis techniques, the paper estimates average effects to find: i) statistically significant effects on credit, but with considerable heterogeneity across instruments; ii) weaker and more imprecise effects on house prices; iii) quantitatively stronger effects in emerging markets and among studies using micro-level data; and iv) statistically significant evidence of leakages and spillovers. Other findings include relatively stronger impacts for tightening than loosening actions and negative effects on economic activity in the near term.