nep-ecm New Economics Papers
on Econometrics
Issue of 2017‒02‒26
sixteen papers chosen by
Sune Karlsson
Örebro universitet

  1. A self-calibrating method for heavy tailed data modeling : Application in neuroscience and finance By Nehla, Debbabi; Marie, Kratz; Mamadou , Mboup
  2. Robust Inference and Testing of Continuity in Threshold Regression Models By Javier Hidalgo; Jungyoon Lee; Myung Hwan Seo
  3. Performance of information criteria used for model selection of Hawkes process models of financial data By J. M. Chen; A. G. Hawkes; E. Scalas; M. Trinh
  4. It's never too LATE: A new look at local average treatment effects with or without defiers By Dahl, Christian M.; Huber, Martin; Mellace, Giovanni
  5. Adoption Costs of Financial Innovation: Evidence from Italian ATM Cards By Kim Huynh; Philipp Schmidt-Dengler; Gregor W. Smith; Angelika Welte
  6. Long-Run Covariability By Ulrich K. Müller; Mark W. Watson
  7. An Alternative Specification for Technical Efficiency Effects in a Stochastic Frontier Production Function By Satya Paul; Sriram Shanker
  8. Macroeconomic Forecasting in Times of Crises By Pablo Guerron-Quintana; Molin Zhong
  9. Modeling time series with zero observations By Andrew Harvey; Ryoko Ito
  10. Aggregate Density Forecasting from Disaggregate Components Using Large VARs By Cobb, Marcus P A
  11. Estimation for the Prediction of Point Processes with Many Covariates By Alessio Sancetta
  12. Identifying the independent sources of consumption variation By Matteo Barigozzi; Alessio Moneta
  13. Heterogeneous Effects of Tariff and Nontariff Policy Barriers in General Equilibrium By Egger, Peter Hannes; Egger, Peter
  14. Selection and statistical analysis of compositional ratios By Michael Greenacre
  15. Joint Forecast Combination of Macroeconomic Aggregates and Their Components By Cobb, Marcus P A
  16. How Do You Interpret Your Regression Coefficients? By Pillai N., Vijayamohanan

  1. 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 self-calibrating 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 self-calibrating method is studied in terms of goodness-of-fi 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
  2. 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
  3. 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 high-frequency financial data modelling. The information criteria are Akaike's information criterion (AIC), the Bayesian information criterion (BIC) and the Hannan-Quinn 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 intra-day 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
  4. 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
  5. By: Kim Huynh; Philipp Schmidt-Dengler; 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 shopping-time 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
  6. By: Ulrich K. Müller; Mark W. Watson
    Abstract: We develop inference methods about long-run comovement of two time series. The parameters of interest are defined in terms of population second-moments of lowfrequency trends computed from the data. These trends are similar to low-pass 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 long-run 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
  7. 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 one-sided 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: Non-linear least squares; Standard normal cumulative distribution function; Technical efficiency
    JEL: C51 D24 Q12
    Date: 2017–02
  8. By: Pablo Guerron-Quintana; 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
  9. 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 non-zero 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 time-varying). The model is ?tted to daily rainfall in northern Australia and compared with a dynamic zero-augmented model.
    Keywords: Censored distributions; dynamic conditional score model; generalized beta distribution; rainfall; seasonality, zero aug- mented model.
    JEL: C22
    Date: 2017–02–21
  10. 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 bottom-up 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 time-varying 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; Bottom-up forecasting; Hierarchical forecasting; Bayesian VAR; Forecast calibration
    JEL: C32 C53 E37
    Date: 2017–02
  11. 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
  12. 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
  13. 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) non-parametric 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 medium-tariff 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
  14. By: Michael Greenacre
    Abstract: Compositional data are nonnegative data with the property of closure: that is, each set of values on their components, or so-called 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 log-transformed.
    Keywords: compositional data, logarithmic transformation, log-ratio analysis, multivariate analysis, ratios, univariate statistics.
    Date: 2016–08
  15. 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: Bottom-up forecasting; Forecast combination; Hierarchical forecasting; Reconciling forecasts
    JEL: C53 E27 E37
    Date: 2017–02
  16. 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 1-unit 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

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