dataesr/scientific-paragraphs-categorization huggingface dataset:
| Data In this study, we use monthly data from January 1990 to December 2018. For the threshold variable, we follow  Petkova (2006)  and include six state variables that describe the investment opportunity and macroeconomic environment. Specifically, to describe the investment opportunity environment, we use the 1-month T-Bill, the dividend yield and the term spread calculated as the 10-year government bond yield minus the 10-year treasury yield. For the macroeconomic environment, we use the growth of the consumer price index (CPI) and industrial production index (IPI), and, finally, the level of the three-component economic uncertainty index (EPU3-Comp). Table  1  reports the descriptive statistics for the threshold and factor variables. Over the period examined, the average values of the T-Bill, dividend yield and term spread are 0.
225%, 2. 078% and 1. 519%, respectively. On average, inflation and industrial production growth are 2. 473% and 1. 942% while the EPU index is at 107. 246. Per the specification of the Fama and French (1993) three factor model, we use the U. S. market excess return, SMB and HML variables as factors, for which we observe an average value of 0. 619%, 0. 124% and 0. 170%, respectively. 7  For mutual fund returns, we use the monthly returns available on the CRSP survivorship bias free mutual fund database. 8 In total, 40,500 funds report as least one return observation within the period of interest. To proceed with our analysis and ensure that there are sufficient observations covering the January 1990-December 2018 period, we select the funds that report at least 300 observations. This exclusion of new and short-lived funds leads to a sample of 3052 funds overall.
| Preliminary stability tests Existing ICAPM and conditional CAPM studies have implemented simple stability tests à la  Andrews (1993)  for known or unknown break dates. For example, Ghysels (1998) (in a conditional CAPM framework) or  Vassalou (2003)  and  Li et al. (2006)  (in an ICAPM framework) use the SupLM test of  Andrews (1993)  to test for the stability of the SMB and HML factors in the Fama-French model for mutual funds extracted from the CRSP database for a relatively long pre-crisis period. 9 Nevertheless, these LM tests have several limitations; they may rely on an incorrect specification of the likelihood function, as they limit the change in the constant and slope coefficients to occur on the same date, despite there being no theoretical justification for this, and have important trimming assumptions, leading to loss of power when the date of the break is located close to the start or the end of the sample.
| CONCLUSION This paper demonstrates that mutual fund performance (as described via factor loadings to typical risk factors) is unstable and mainly procyclical, evolving in line with macroeconomic stances. We consider a novel methodology relying on a factor-augmented CAPM with regimes driven by a set of macroeconomic and financial variables. Using a dataset including the returns of 825 U. S. equity mutual funds over a period of 30 years, we find that linearity in the traditional Fama-French model is rejected for the majority of mutual funds. Furthermore, we show that fund sensitivities to the Fama-French factors are regime dependent and mainly driven by a few variables such as the yield curve, the dividend yield, short-term interest rates and economic growth. Moreover, regime shift dates almost perfectly match financial crises and economic downturns.
See Petersen (2009) about the comparison of these different approaches in finance panel data sets. 7 The dividend yield, 10-year government bond yield, 1-year treasury yield, CPI and IPI variables are available at the Federal Reserve Economic Data (Fred) Federal Reserve Bank of St. Louis. The EPU index data are available at the Economic Policy Uncertainty domain (https://www. policyuncertainty. com/). Finally, the 1-month T-Bill, U. S. market excess returns, SMB and HML data are available at the Kenneth R. French Data Library (https://mba. tuck. dartmouth. edu/pages/faculty/ken. french/data_ library. html). 8 Data are the property of Essex business school and therefore are not shared. 9 See also  Guidolin and Timmermann (2008) ,  Chincoli and Guidolin (2017)  and  Bianchi et al. (2017)  about SMB and HML returns instability.
DATA AVAILABILITY STATEMENT The dividend yield, 10-year government bond yield, 1-year treasury yield, CPI and IPI variables are available at the Federal Reserve Economic Data (Fred) Federal Reserve Bank of St. Louis. The EPU index data are available at the Economic Policy Uncertainty domain (https://www. policyuncertainty. com/). Finally, the 1-month T-Bill, U. S. market excess returns, SMB and HML data are available at the Kenneth R. French Data Library (https:// mba. tuck. dartmouth. edu/pages/faculty/ken. french/datalibrary. html). Crsp Data are the property of Essex business school and therefore are not shared.
Larger and more productive firms can more easily take advantage of the change in trade costs than smaller and less productive firms via a productivity channel (Melitz, 2003) and/or a heterogeneous mark-up and pricing-to-market behaviour (Melitz and   Ottaviano, 2008; Atkeson and Burstein, 2008). As deep trade agreements can reduce trade costs among members by eliminating tariffs and by reducing other frictions due to regulatory differences and/or policy uncertainty, a natural extension of this literature is that DTAs should affect firms differently based on their characteristics, promoting the export performance of larger firms and leading to a selection effect. This paper empirically investigates the effect of RTAs' depth on the export performance of heterogeneous firms. The analysis combines firm-level export data for 31 developing countries from the World Bank Exporter Dynamics Database (Fernandes et al.
, 2016) and information on the detailed content of more than 300 RTAs from the World Bank Deep Trade Agreements database (Hofmann et al. , 2017). The richness of the data allows us to precisely take into account the heterogeneous consequences of deep RTAs on the exports of different firms. We use a decomposition à la Berman et al. (2012) to study the extent to which the effects of deep RTAs on trade come from the intensive and extensive margins of trade. Importantly, we carefully investigate the endogeneity of trade and deep RTAs through different methods. We use an Instrumental Variable approach based on the domino effect of RTAs (Baldwin and Jaimovich,   2012), a plausible exogeneity test à la Conley et al. (2012), and we employ an event study approach to check the parallel trend assumption and confirm the validity of the key results.
To assess the impact of deep RTAs on the export performances of firms, we adapt the standard gravity model for trade (Head and Mayer, 2014) to firm-level analysis and include a variable capturing the depth of RTAs. Using information from the Deep Trade Agreements database, we construct different measures of RTA depth based on the policy areas covered by the agreements and their legal enforceability. Our specification allows us to identify the impact on firms' exports of a change in the depth of RTAs (i. e. newly signed RTAs or amendment of preexisting ones) between two countries, controlling for any firm-year and country-specific factor that may affect the export performance of firms. The baseline results show that one additional legally enforceable policy area in RTAs boosts the exports of firms by 0.
Data and descriptive evidence Our empirical analysis is based on two main World Bank data sources: (i) the Exporter Dynamics Database (Fernandes et al. , 2016) providing firm-level exports for 55 developing countries in the period 1996-2020, and (ii) the Content of Deep Trade Agreements (Hofmann et al. , 2017) on the content of RTAs in force and notified to the WTO for the period 2000-2020. Moreover, we complete our dataset by including gravity-related variables (such as distance, common border, language, etc. ) from the CEPII gravity database, and data on the effectively applied tariffs faced by each exporter at the destination from the MacMap (CEPII) database. We build our final dataset in several steps. First, we reorganize the original DTA database into a country pair-year-specific dataset.
2017), (iii) number of WTO-plus provisions (WTO+) -i. e. provisions covered by the current mandate of the WTO, (iv) the number of WTOextra provisions (WTO-X) -i. e. provisions not covered by the current mandate of the WTO, and (iv) number of core provisions -i. e. provisions directly related to trade enhancing factors. 6  While in our empirical exercise, we use all these proxies for the depth of RTAs, our baseline measure of RTA depth is the count of legally enforceable provisions in each RTA. 7  The simple count of provisions in RTAs gives equal weight to each clause included in the agreement, and one may want to give more weight to "rare" provisions (i. e. provisions included less frequently in RTAs and likely to signal stronger market integration). To address this concern, as a robustness check, we also use alternative measures of RTA depth based on the weighted sum of provisions (using one minus the frequency to which each provision appears in the 300 RTAs covered by the DTA database).
8   One drawback of the DTA database is that it does not cover RTAs that are no longer in force. 9  So, when controlling for the presence of an RTA between two countries (see section 4), we complement the DTA database with the CEPII data covering active and inactive RTAs. The RTA adjusted dummy is equal to one if a RTA is observed (from CEPII and/or DTA dataset). The Exporter Dynamics Database (EDD) takes the form of multiple databases: one per country. 10  So, as a last step in the construction of the final data set, we add to each countryspecific EDD dataset: (i) the gravity variables from the CEPII gravity database, and (ii) the tariffs data from MacMap (CEPII). Finally, we pool all the 55 country-specific databases to get a single complete firm-level dataset. This dataset initially contains 29,009,865 observations (firmdestination-product-year specific) spanning from the late 1990s to 2020.
However, the DTA database covers only the period 2000-2020; so we keep only firm export data for this period. It must be noted that the coverage of the EDD varies by country: for some countries we have data for the entire 2000-2020 period, while for some other countries we have a more limited time period (cf. table A1 in appendix). Although original firm-level export data are (also) product HS6-digit specific, our main variable of interest is not, so we aggregate export data at the level of firm-destination-year (sum across HS 6-digit products for each firm-destination-year combination). As described in Table  1 , the majority of exporters included in the EDD have at least one active RTA in force (i. e. 48 out of 55). However, only a sub-sample of countries (and countrypairs) have changed the depth of their RTAs in the period 2000-2020 (i.
18 Using the simple count of provisions to approximate the RTAs' depth implicitly gives the same importance to any type of provision. One may want to assign relatively higher value of depth to those RTAs including rare provisions. So, as a robustness check, in Table  4  we show results by using weighted count to approximate the depth of RTAs (the weight is equal to one minus each provision's frequency in the matrix of the RTAs mapped by the World Bank Deep Trade Agreements database). The resulting index weights relatively more RTAs containing rare provisions. Results, reported in Table  4 , support the robustness of our baseline results. Interestingly, the estimations coefficients in Table  4  point to a stronger impact of deep RTAs on exports when approximated by a weighted sum. This suggests that the inclusion of rare provisions in RTAs is a good signal of the extent of trade cost reductions associated with deep RTAs between member countries.
The effect of RTA depth is robust to the inclusion of dummies for the presence of an RTA between country i and j at time t. See results reported in Table  A6. To take into account the presence of non-mapped active RTAs (i. e. those RTAs not included in the World Bank database), in Table  A6  we take the list of RTAs from CEPII and assign a value of depth respectively equal to one (see columns 1-2), or equal to the closest (in time) country-pair's RTA depth to nonmapped active RTAs (see columns 3-4). As expected, the presence of an empty (active) RTA -15 RTAs at the 75 th percentile of legally enforceable provisions contain 14 provisions. 16 WTO+ provisions contain standard tariff cut provisions on agriculture and manufacturing sectors, so we consider RTAs with two WTO+ provisions as shallow.
(2012). Namely, we regress the aggregate exports by origin-destination-year for respectively incumbent and entry-exit firms on RTAs' depth, and country-year fixed effects. The coefficients on RTA depth are respectively 20 PPML estimator is used in Table  6. 21  A more compelling way of testing the pro-competitive effect of DTAs would be using the participation margin of firm level exports (i. e. dummy equal to one if the firm exports into a given destination-year). However, the EDD is a customs based dataset and does not include real "zeros". Any artificial inclusion of zeros (i. e. squaring the dataset with zeros) would be arbitrary. βincumbent and βentry. We then calculate the share of total exports from respectively incumbent and entry-exit firms (V incumbent /V tot and V entry /V tot ), and multiply such shares for the respective elasticity βincumbent and βentry.
Missing values in environmental time series are common and must be imputed before carrying out an analysis requiring complete data. We propose an imputation method for the time series of a target station using information of neighboring stations measuring the same variable. The method allows these neighboring stations to have missing values themselves. The multivariate dataset comprising the time series of the target station and its neighboring stations is jointly modeled by a vine copula and parametric margins. Multiple imputation takes into account the uncertainty of missing data by generating several plausible values for each missing value in the time series of the target station. This is done in a Bayesian framework by sampling the posterior distribution of a missing value, which is conditional on the observed stations for the date.
Introduction The presence of missing values in time series is a recurring issue in environmental sciences and many other disciplines. Data may be missing for various reasons, such as measurement device failure or measurement error  (Kalteh and Hjorth, 2009). Many common analyses applied to environmental variables, such as spectral analysis or extreme value analysis, require complete time series  (Gao et al. , 2018). The analysis can be performed on a subset of the dataset for which there are no missing values, but this can severely limit the length of the time series and thus negatively impact the results. Values can also be missing in a systematic way and introduce bias in an analysis. As an example, the probability of missingness can be higher during extreme events due to measurement device failure, which would artificially reduce the frequency of extremes in the recorded time series.
Therefore, a prior imputation of the missing data is often necessary. Even for an analysis that can accommodate missing values, imputing rather than ignoring them can improve results by increasing the length of the time series and reducing the potential bias caused by the missingness mechanism. Our interest is in the imputation of the time series at a given station (referred to as the target station thereafter), using the information of other stations (the neighboring stations) measuring the same variable in an homogeneous region. The imputation method must allow neighboring stations to have missing values, as they are also subject to missingness. This homogeneous region is defined according to the objective of the subsequent analysis of the imputed dataset. Hamdi et al. (2019)  and  Andreevsky et al.
(2020)  defined this region based on the ratio of common extreme events between the target station and each neighboring station, which is adapted if the interest is in the extremes. Since the analysis of extreme values is of particular interest in environmental sciences, e. g. , for risk assessment, the imputation must retain good performance for the tails of the distribution in addition to its bulk, and must take into account the uncertainty associated with missing data  (Serinaldi and Kilsby, 2015). The imputation methods available for environmental sciences have been extensively reviewed  (Kalteh and Hjorth, 2009; Ben Aissia et al. , 2017; Gao et al. , 2018; Hamzah et al. , 2020). The time series of the target station and its neighboring stations constitute a multivariate dataset. Joint modeling of this dataset with a parametric distribution is a suitable approach if the extremes are of interest, as the tails of the distribution are explicitly modeled.
For a -dimensional random vector  ∈ [0, 1]  , a copula  is defined by: ( 1 , … ,   ) = Pr( 1 ≤  1 , …   ≤   ). (1) Each dimension of a dataset can be transformed to be uniform over (0, 1) with its probability integral transformation  (Yan, 2007). A dimensional joint distribution  with margins  1 , … ,   has a unique copula , such that:  ( 1 , … ,   ) = { 1 ( 1 ), … ,   (  )}. (2) Thus, copulas allow separate modeling of the margins and the dependence between dimensions. Many parametric families of twodimensional copulas (referred to as pair-copulas thereafter) exist to model a variety of dependence structures, but the choice becomes much more restricted for copulas in higher dimensions  (Aas et al. , 2009). Furthermore, the existing families of copulas usable in three or more dimensions, such as the Gaussian or Student  copulas, can impose a too restrictive dependence structure for a given dataset.
Copulas have already been applied to multiple imputation. Hollenbach et al. (2014)  used Gaussian copulas in two or more dimensions, but this is not appropriate for the extremes because the dimensions are asymptotically independent in the Gaussian copula (i. e. , it cannot model the eventual tail dependence). Di  Lascio et al. (2015)  considered the Gaussian and  copulas in high dimension, or several families of pair-copulas. The high-dimensional  copula has the inverse drawback compared to the Gaussian copula, as it forces a positive tail dependence. The imputation with a single pair-copula (i. e. , not several pair-copulas organized as a vine copula) is restricted to twodimensional datasets. Vine copulas solve both limitations of copulas by constructing high-dimensional dependence structures as assemblages of pair-copulas, taking advantage of the rich diversity of pair-copula families and allowing the dependence structure (including the tail dependence) to vary for each pair of dimensions.
Ahn (2021) applied a D-vine copula to estimate streamflow at a partially gauged site conditionally on observations at neighboring stations, with the latter having complete records in their time series. Ahn (2021)  compared the performance of the D-vine with six other imputation methods, including geostatistical methods with inverse distance weighting and Kriging, and found the D-vine to outperform them. Hasler et al. (2018)  developed the imputation of a multivariate dataset with a D-vine copula for the case where each dimension may have missing values. The approach of  Hasler et al. (2018)  was restricted to monotone missingness patterns (i. e. , only the lower and/or upper dimensions of the ordered dimensions of the D-vine are missing), and assumed the data to be missing completely at random (MCAR, meaning that the probability of missingness is independent on the actual data value).
Hasler et al. (2018)  compared the performance of their method with five alternatives, different from the six alternatives tested by  Ahn (2021) , and also found the D-vine to outperform them. In particular, the D-vine outperforms alternatives when the extremes are considered because it accounts for the eventual tail dependence. Jane et al. (2016)  applied copulas to extend wave height time series beyond their measurement period using regional information. This can be achieved by considering the dates outside of the observation period as missing values, which does not require additional development of the multiple imputation model. Valle and Kaplan (2019)  applied a Gaussian copula for a counterfactual analysis of a dataset where every dimension can have missing values, which shows that the ability of copulas to handle missing values has applications beyond imputation.
Methods The time series at the target station and its neighbors constitute a multivariate dataset. The margins of this dataset are modeled with univariate parametric distributions. The marginal nonexceedance probabilities of the observations at each station are obtained from their respective margins. The dependence structure of the multivariate probabilities are then modeled with a vine copula, each station corresponding to a dimension of this multivariate distribution. For a given date, values are sampled from the vine copula for the missing dimensions, conditionally on the observed one. The multiple imputation accounts for the uncertainty of the values generated  (Little et al. , 2014). Finally the multiple imputed probabilities are transformed back to quantiles.
In the case of  Hasler et al. (2018) , their dataset only had a monotone missingness pattern, so these subsets allowed every observation to contribute to the likelihood. In our case, the missingness patterns of some dates do not correspond to a valid D-vine subset, when the observed dimensions are not ordered continuously in the full D-vine, therefore these dates cannot contribute to the likelihood. Despite not being able to use every observation for the likelihood of the D-vine, this approach still uses much more information compared to only using dates without any missing value. Subsetting the full D-vine is also useful to reduce the computational requirement when sampling from the model for imputation, as will be presented in Section 2. 5. Let  be the set of missingness patterns corresponding to a subsettable D-vine, including the full D-vine.
If the Gaussian copula was not considered, all the families other than the independence copula would have a positive upper tail dependence, which could force the model to have asymptotic dependence between some dimensions. More families could be considered but it is preferable to limit the set of families so that relevant pair-copulas are proposed more often during the Jump steps of the RJMCMC algorithm. The Student  copula is not considered because the evaluation of its likelihood takes much more time than for the six families mentioned previously (with the VineCopula R package). If the method is applied to a smaller dataset, the RJMCMC can be run for more iterations, so a larger set of pair-copula families can be tested. Likewise the  copula could be included for a small dataset, since the difference in computation time would then be negligible.
g. , for the  14|23 paircopula, the marginal probabilities are obtained from the observations without missing values in dimensions {1, … , 4}. Min and Czado (2011)  estimated the location parameter of the proposal by adjusting the entire D-vine with the modified pair-copula family by maximum likelihood. Instead, estimating  *  only by adjusting the proposed pair-copula on its marginal probabilities does not required evaluating the likelihood of the entire D-vine, which is the costliest part of the algorithm. This modification of the algorithm makes a significant difference in computation time for applications with a large dataset. As in the Stay step with (12), the full parameter vector   is assembled with    , whose family and parameters are modified, and the family and parameters of the pair-copulas other than the th that remain unmodified.