
on Econometrics 
By:  Nehla, Debbabi (SUP'COM  Ecole Supérieure des Communications de Tunis); Marie, Kratz (Essec Business School); Mamadou , Mboup (CRESTIC  Centre de Recherche en Sciences et Technologies de l'Information et de la Communication) 
Abstract:  One of the main issues in the statistical literature of extremes concerns the tail index estimation, closely linked to the determination of a threshold above which a Generalized Pareto Distribution (GPD) can be fi tted. Approaches to this estimation may be classfii ed into two classes, one using standard Peak Over Threshold (POT) methods, in which the threshold to estimate the tail is chosen graphically according to the problem, the other suggesting selfcalibrating methods, where the threshold is algorithmically determined. Our approach belongs to this second class proposing a hybrid distribution for heavy tailed data modeling, which links a normal (or lognormal) distribution to a GPD via an exponential distribution that bridges the gap between mean and asymptotic behaviors. A new unsupervised algorithm is then developed for estimating the parameters of this model. The effectiveness of our selfcalibrating method is studied in terms of goodnessoffi t on simulated data. Then, it is applied to real data from neuroscience and fi nance, respectively. A comparison with other more standard extreme approaches follows. 
Keywords:  Algorithm; Extreme Value Theory; Gaussian distribution; Generalized Pareto Distribution; Heavy tailed data; Hybrid model; Least squares optimization; Levenberg Marquardt algorithm; Neural data; S&P 500 index 
JEL:  C02 
Date:  2016–12–12 
URL:  http://d.repec.org/n?u=RePEc:ebg:essewp:dr16019&r=ecm 
By:  Javier Hidalgo; Jungyoon Lee; Myung Hwan Seo 
Abstract:  This paper is concerned with inference in regression models with either a kink or a jump at an unknown threshold, particularly when we do not know whether the kink or jump is the true specification. One of our main results shows that the statistical properties of the estimator of the threshold parameter are substantially different under the two settings, with a slower rate of convergence under the kink design, and more surprisingly slower than if the correct kink specification were employed in the estimation. We thus propose two testing procedures to distinguish between them. Next, we develop a robust inferential procedure that does not require prior knowledge on whether the regression model is kinky or jumpy. Furthermore, we propose to construct confidence intervals for the unknown threshold by the bootstrap test inversion, also known as grid bootstrap. Finite sample performances of the bootstrap tests and the grid bootstrap confidence intervals are examined and compared against tests and confidence intervals based on the asymptotic distribution through Monte Carlo simulations. Finally, we implement our procedure to an economic empirical application 
JEL:  C12 C13 C24 
Date:  2017–02 
URL:  http://d.repec.org/n?u=RePEc:cep:stiecm:590&r=ecm 
By:  J. M. Chen; A. G. Hawkes; E. Scalas; M. Trinh 
Abstract:  We test three common information criteria (IC) for selecting the order of a Hawkes process with an intensity kernel that can be expressed as a mixture of exponential terms. These processes find application in highfrequency financial data modelling. The information criteria are Akaike's information criterion (AIC), the Bayesian information criterion (BIC) and the HannanQuinn criterion (HQ). Since we work with simulated data, we are able to measure the performance of model selection by the success rate of the IC in selecting the model that was used to generate the data. In particular, we are interested in the relation between correct model selection and underlying sample size. The analysis includes realistic sample sizes and parameter sets from recent literature where parameters were estimated using empirical financial intraday data. We compare our results to theoretical predictions and similar empirical findings on the asymptotic distribution of model selection for consistent and inconsistent IC. 
Date:  2017–02 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1702.06055&r=ecm 
By:  Dahl, Christian M. (Department of Business and Economics); Huber, Martin (University of Fribourg); Mellace, Giovanni (Department of Business and Economics) 
Abstract:  In heterogeneous treatment effect models with endogeneity, identification of the LATE typically relies on the availability of an exogenous instrument monotonically related to treatment participation. We demonstrate that a strictly weaker local monotonicity condition identifies the LATEs on compliers and on defiers. We propose simple estimators that are potentially more efficient than 2SLS, even under circumstances where 2SLS is consistent. Additionally, when easing local monotonicity to local stochastic monotonicity, our identification results still apply to subsets of compliers and defiers. Finally, we provide an empirical application, rejoining the endeavor of estimating returns to education using the quarter of birth instrument. 
Keywords:  Instrumental variable; treatment effects; LATE; local monotonicity 
JEL:  C14 C21 C26 
Date:  2017–02–14 
URL:  http://d.repec.org/n?u=RePEc:hhs:sdueko:2017_002&r=ecm 
By:  Kim Huynh; Philipp SchmidtDengler; Gregor W. Smith; Angelika Welte 
Abstract:  The discrete choice to adopt a financial innovation affects a household’s exposure to inflation and transactions costs. We model this adoption decision as being subject to an unobserved cost. Estimating the cost requires a dynamic structural model, to which we apply a conditional choice simulation estimator. A novel feature of our method is that preference parameters are estimated separately, from the Euler equations of a shoppingtime model, to aid statistical efficiency. We apply this method to study ATM card adoption in the Bank of Italy’s Survey of Household Income and Wealth. There, the implicit adoption cost is too large to be consistent with standard models of rational choice, even when sorted by age, cohort, education or region. 
Keywords:  Bank notes, Econometric and statistical methods, Financial services 
JEL:  E41 D14 C35 
Date:  2017 
URL:  http://d.repec.org/n?u=RePEc:bca:bocawp:178&r=ecm 
By:  Ulrich K. Müller; Mark W. Watson 
Abstract:  We develop inference methods about longrun comovement of two time series. The parameters of interest are defined in terms of population secondmoments of lowfrequency trends computed from the data. These trends are similar to lowpass filtered data and are designed to extract variability corresponding to periods longer than the span of the sample divided by q/2, where q is a small number, such as 12. We numerically determine confidence sets that control coverage over a wide range of potential bivariate persistence patterns, which include arbitrary linear combinations of I(0), I(1), near unit roots and fractionally integrated processes. In an application to U.S. economic data, we quantify the longrun covariability of a variety of series, such as those giving rise to the “great ratios”, nominal exchange rates and relative nominal prices, unemployment rate and inflation, money growth and inflation, earnings and stock prices, etc. 
JEL:  C22 C53 E17 
Date:  2017–02 
URL:  http://d.repec.org/n?u=RePEc:nbr:nberwo:23186&r=ecm 
By:  Satya Paul; Sriram Shanker 
Abstract:  This paper proposes an alternative specification for technical efficiency effects in a stochastic production frontier model. The proposed specification is distribution free and thus eschews onesided error term present in almost all the existing inefficiency effects models. The efficiency effects are represented by the standard normal cumulative distribution function of exogenous variables which ensures the efficiency scores to lie in a unit interval. An empirical exercise based on widely used Philippines rice farming data set illustrates the simplicity and usefulness of the proposed model. 
Keywords:  Nonlinear least squares; Standard normal cumulative distribution function; Technical efficiency 
JEL:  C51 D24 Q12 
Date:  2017–02 
URL:  http://d.repec.org/n?u=RePEc:een:crwfrp:1703&r=ecm 
By:  Pablo GuerronQuintana; Molin Zhong 
Abstract:  We propose a parsimonious semiparametric method for macroeconomic forecasting during episodes of sudden changes. Based on the notion of clustering and similarity, we partition the time series into blocks, search for the closest blocks to the most recent block of observations, and with the matched blocks we proceed to forecast. One possibility is to compare local means across blocks, which captures the idea of matching directional movements of a series. We show that our approach does particularly well during the Great Recession and for variables such as inflation, unemployment, and real personal income. When supplemented with information from housing prices, our method consistently outperforms parametric linear, nonlinear, univariate, and multivariate alternatives for the period 1990  2015. 
Keywords:  Forecasting ; Great Recession ; Nearest neighbor ; Semiparametric methods 
JEL:  C14 C53 
Date:  2017–01–31 
URL:  http://d.repec.org/n?u=RePEc:fip:fedgfe:201718&r=ecm 
By:  Andrew Harvey (Faculty of Economics, Cambridge University); Ryoko Ito (Dept of Economics and Nuffield College, Oxford University) 
Abstract:  We consider situations in which a signi?cant proportion of observations in a time series are zero, but the remaining observations are positive and measured on a continuous scale. We propose a new dynamic model in which the conditional distribution of the observations is constructed by shifting a distribution for nonzero observations to the left and censoring negative values. The key to generalizing the censoring approach to the dynamic case is to have (the logarithm of) the location/scale parameter driven by a ?lter that depends on the score of the conditional distribution. An exponential link function means that seasonal effects can be incorporated into the model and this is done by means of a cubic spline (which can potentially be timevarying). The model is ?tted to daily rainfall in northern Australia and compared with a dynamic zeroaugmented model. 
Keywords:  Censored distributions; dynamic conditional score model; generalized beta distribution; rainfall; seasonality, zero aug mented model. 
JEL:  C22 
Date:  2017–02–21 
URL:  http://d.repec.org/n?u=RePEc:nuf:econwp:1701&r=ecm 
By:  Cobb, Marcus P A 
Abstract:  When it comes to point forecasting there is a considerable amount of literature that deals with ways of using disaggregate information to improve aggregate accuracy. This includes examining whether producing aggregate forecasts as the sum of the component’s forecasts is better than alternative direct methods. On the contrary, the scope for producing density forecasts based on disaggregate components remains relatively unexplored. This research extends the bottomup approach to density forecasting by using the methodology of large Bayesian VARs to estimate the multivariate process and produce the aggregate forecasts. Different specifications including both fixed and timevarying parameter VARs and allowing for stochastic volatility are considered. The empirical application with GDP and CPI data for Germany, France and UK shows that VARs can produce well calibrated aggregate forecasts that are similar or more accurate than the aggregate univariate benchmarks. 
Keywords:  Density Forecasting; Bottomup forecasting; Hierarchical forecasting; Bayesian VAR; Forecast calibration 
JEL:  C32 C53 E37 
Date:  2017–02 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:76849&r=ecm 
By:  Alessio Sancetta 
Abstract:  Estimation of the intensity of a point process is considered within a nonparametric framework. The intensity measure is unknown and depends on covariates, possibly many more than the observed number of jumps. Only a single trajectory of the counting process is observed. Interest lies in estimating the intensity conditional on the covariates. The impact of the covariates is modelled by an additive model where each component can be written as a linear combination of possibly unknown functions. The focus is on prediction as opposed to variable screening. Conditions are imposed on the coefficients of this linear combination in order to control the estimation error. The rates of convergence are optimal when the number of active covariates is large. As an application, the intensity of the buy and sell trades of the New Zealand dollar futures is estimated and a test for forecast evaluation is presented. A simulation is included to provide some finite sample intuition on the model and asymptotic properties. 
Date:  2017–02 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1702.05315&r=ecm 
By:  Matteo Barigozzi; Alessio Moneta 
Abstract:  By representing a system of budget shares as an approximate factor model we determine its rank, i.e. the number of common functional forms, or factors and we estimate a base of the factor space by means of approximate principal components. We assume that the extracted factors span the same space of basic Engel curves representing the fundamental forces driving consumers’ behaviour. We identify these curves by imposing statistical independence and by studying their dependence on total expenditure using local linear regressions. We prove consistency of the estimates. Using data from the U.K. Family Expenditure Survey from 1977 to 2006, we find strong evidence of two common factors and mixed evidence of a third factor. These are identified as decreasing, increasing, and almost constant Engel curves. The household consumption behaviour is therefore driven by two factors respectively related to necessities (e.g. food), luxuries (e.g. vehicles), and in some cases by a third factor related to goods to which is allocated the same percentage of total budget both by rich and poor households (e.g. housing). 
Keywords:  Budget Shares; Engel Curves; Approximate Factor Models; Independent Component Analysis; Local Linear Regression 
JEL:  C52 D12 
Date:  2016 
URL:  http://d.repec.org/n?u=RePEc:ehl:lserod:60979&r=ecm 
By:  Egger, Peter Hannes; Egger, Peter 
Abstract:  Most applied work in international economics treats trade policy (a) as a linear component of trade costs and (b) as an exogenous variable. This paper proposes a structural modelling approach that allows for the estimation of (possibly) nonparametric effects of trade policy using a propensity score method to account for the endogeneity bias of trade policy. The findings point to important nonlinear effects of tariff and nontariff policy. Specifically, they suggest that interdependencies between nontariff policy barriers and tariffs are an important determinant of the partial impact of a given policy change. Overall, trade policy changes seem to be effective only for low and mediumtariff trade flows. In general equilibrium, loosening the linearity assumption translates to an increased heterogeneity of predicted trade effects with the mean and median effect being up to three times as large in a flexible, nonparametric specification. 
JEL:  F14 F13 C14 
Date:  2016 
URL:  http://d.repec.org/n?u=RePEc:zbw:vfsc16:145675&r=ecm 
By:  Michael Greenacre 
Abstract:  Compositional data are nonnegative data with the property of closure: that is, each set of values on their components, or socalled parts, has a fixed sum, usually 1 or 100%. Compositional data cannot be analyzed by conventional statistical methods, since the value of any part depends on the choice of the other parts of the composition of interest. For example, reporting the mean and standard deviation of a specific part makes no sense, neither does the correlation between two parts. I propose that a small set of ratios of parts can be determined, either by expert choice or by automatic selection, which effectively replaces the compositional data set. This set can be determined to explain 100% of the variance in the compositional data, or as close to 100% as required. These part ratios can then be validly summarized and analyzed by conventional univariate methods, as well as multivariate methods, where the ratios are preferably logtransformed. 
Keywords:  compositional data, logarithmic transformation, logratio analysis, multivariate analysis, ratios, univariate statistics. 
Date:  2016–08 
URL:  http://d.repec.org/n?u=RePEc:upf:upfgen:1551&r=ecm 
By:  Cobb, Marcus P A 
Abstract:  This paper presents a framework that extends forecast combination to include an aggregate and its components in the same process. This is done with the objective of increasing aggregate forecasting accuracy by using relevant disaggregate information and increasing disaggregate forecasting accuracy by providing a binding context for the component’s forecasts. The method relies on acknowledging that underlying a composite index is a well defined structure and its outcome is a fully consistent forecasting scenario. This is particularly relevant for people that are interested in certain components or that have to provide support for a particular aggregate assessment. In an empirical application with GDP data from France, Germany and the United Kingdom we find that the outcome of the combination method shows equal aggregate accuracy to that of equivalent traditional combination methods and a disaggregate accuracy similar or better to that of the best single models. 
Keywords:  Bottomup forecasting; Forecast combination; Hierarchical forecasting; Reconciling forecasts 
JEL:  C53 E27 E37 
Date:  2017–02 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:76556&r=ecm 
By:  Pillai N., Vijayamohanan 
Abstract:  This note is in response to David C. Hoaglin’s provocative statement in The Stata Journal (2016) that “Regressions are commonly misinterpreted”. “Citing the preliminary edition of Tukey’s classic Exploratory Data Analysis (1970, chap. 23), Hoaglin argues that the correct interpretation of a regression coefficient is that it “tells us how Y responds to change in X2 after adjusting for simultaneous linear change in the other predictors in the data at hand”. He contrasts this with what he views as the common misinterpretation of the coefficient as “the average change in Y for a 1unit increase in X2 when the other Xs are held constant”. He asserts that this interpretation is incorrect because “[i]t does not accurately reflect how multiple regression works”. We find that Hoaglin’s characterization of common practice is often inaccurate and that his narrow view of proper interpretation is too limiting to fully exploit the potential of regression models. His article rehashes debates that were settled long ago, confuses the estimator of an effect with what is estimated, ignores modern approaches, and rejects a basic goal of applied research.” (Long and Drukker, 2016:25). This note broadly agrees with the comments that followed his article in the same issue of The Stata Journal (2016) and seeks to present an argument in favour of the commonly held interpretation that Hoaglin unfortunately marks as misinterpretation. 
Keywords:  Regression, Partial regression coefficients, interpretation, partial correlation 
JEL:  C1 C13 
Date:  2016 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:76867&r=ecm 