Testing for Granger Causality in Heterogeneous Mixed Panels
Testing for Granger Causality in Heterogeneous Mixed Panels
By Furkan Emirmahmutoglu, and Nezir Kose
Economic Modelling (2011)
Abstract Paper

Furkan  Emirmahmutoglu

Gazi University

Turkey

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This code is written to test panel causality which is valid the following cases: 1) The test is valid for four different DGPs in mixed panels involving I(0), I(1), cointegrated and non-cointegrated series. 2) The lag lengths on autoregressive coefficients and exogenous variables can be different for cross-section units. 3) Time periods of each unit should not be same. 4) To prevent cross-section dependency problem, we obtain emprical distribution of the Fisher Test statistic using Bootstrap procedure.
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March 17, 2013
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Abstract
In this paper, we propose a simple Granger causality procedure based on Meta analysis in heterogeneous mixed panels. Firstly, we examine the finite sample properties of the causality test through Monte Carlo experiments for panels characterized by both cross-section independency and cross-section dependency. Then, we apply the procedure for investigating the export led growth hypothesis in a panel data of twenty OECD countries.
Emirmahmutoglu, F., and N. Kose, "Testing for Granger Causality in Heterogeneous Mixed Panels ", Economic Modelling , 28, 870-876.
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Backtesting Value-at-Risk: A Duration-Based Approach
Abstract
Financial risk model evaluation or backtesting is a key part of the internal model’s approach to market risk management as laid out by the Basle Committee on Banking Supervision. However, existing backtesting methods have relatively low power in realistic small sample settings. Our contribution is the exploration of new tools for backtesting based on the duration of days between the violations of the Value-at-Risk. Our Monte Carlo results show that in realistic situations, the new duration-based tests have considerably better power properties than the previously suggested tests.
Hurlin, C., and C. Perignon, "Backtesting Value-at-Risk: A Duration-Based Approach", Journal of Financial Econometrics, 2, 84-108.
Authors: Pelletier
Christoffersen
Coders: Hurlin
Perignon
Last update
07/23/2012
Ranking
26
Runs
17
Visits
207
Backtesting Value-at-Risk Accuracy: A Simple New Test
Abstract
This paper proposes a new test of value-at-risk (VAR) validation. Our test exploits the idea that the sequence of VAR violations (hit function) – taking value 1 - α if there is a violation, and -α otherwise – for a nominal coverage rate α verifies the properties of a martingale difference if the model used to quantify risk is adequate (Berkowitz et al., 2005). More precisely, we use the multivariate portmanteau statistic of Li and McLeod (1981), an extension to the multivariate framework of the test of Box and Pierce (1970), to jointly test the absence of autocorrelation in the vector of hit sequences for various coverage rates considered relevant for the management of extreme risks. We show that this shift to a multivariate dimension appreciably improves the power properties of the VAR validation test for reasonable sample sizes.
Hurlin, C., and S. Tokpavi, "Backtesting Value-at-Risk Accuracy: A Simple New Test", Journal of Risk, 9, 19-37.
Authors: Hurlin
Tokpavi
Coders: Hurlin
Tokpavi
Last update
03/13/2012
Ranking
49
Runs
2
Visits
240
How To Evaluate an Early Warning System? Towards a unified Statistical Framework for Assessing Financial Crises Forecasting Methods
Abstract
This paper proposes an original and unified toolbox to evaluate financial crisis Early Warning Systems (EWS). It presents four main advantages. First, it is a model-free method which can be used to asses the forecasts issued from different EWS (probit, logit, markov switching models, or combinations of models). Second, this toolbox can be applied to any type of crisis EWS (currency, banking, sovereign debt, etc.). Third, it does not only provide various criteria to evaluate the (absolute) validity of EWS forecasts but also proposes some tests to compare the relative performance of alternative EWS. Fourth, our toolbox can be used to evaluate both in-sample and out-of-sample forecasts. Applied to a logit model for twelve emerging countries we show that the yield spread is a key variable for predicting currency crises exclusively for South-Asian countries. Besides, the optimal cut-off correctly allows us to identify now on average more than 2/3 of the crisis and calm periods.
Candelon, B., E. Dumitrescu, and C. Hurlin, "How To Evaluate an Early Warning System? Towards a unified Statistical Framework for Assessing Financial Crises Forecasting Methods", IMF Economic Review, 60.
Authors: Candelon
Dumitrescu
Hurlin
Coders: Candelon
Dumitrescu
Hurlin
Last update
07/23/2012
Ranking
34
Runs
24
Visits
269
Forecasting Realized Volatility Using a Nonnegative Semiparametric Model
Abstract
This paper introduces a parsimonious and yet exible nonnegative semi-parametric model to forecast financial volatility. The new model extends the linear nonnegative autoregressive model of Barndor-Nielsen & Shephard (2001) and Nielsen & Shephard (2003) by way of a power transformation. It is semiparametric in the sense that the distributional form of its error component is left unspecified. The statistical properties of the model are discussed and a novel estimation method is proposed. Asymptotic properties are established for the new estimation method. Simulation studies validate the new estimation method. The out-of-sample performance of the proposed model is evaluated against a number of standard methods, using data on S&P 500 monthly realized volatilities. The competing models include the exponential smoothing method, a linear AR(1) model, a log-linear AR(1) model, and two long-memory ARFIMA models. Various loss functions are utilized to evaluate the predictive accuracy of the alternative methods. It is found that the new model generally produces highly competitive forecasts.
Preve, D., J. Yu, "Forecasting Realized Volatility Using a Nonnegative Semiparametric Model", Uppsala University.
Authors: Eriksson
Preve
Yu
Coders: Preve
Yu
Last update
06/06/2012
Ranking
64
Runs
19
Visits
114
Mixed Logit with Bounded Distributions of Correlated Partworths
Abstract
The use of a joint normal distribution for partworths is computationally attractive, particularly with Bayesian MCMC procedures, and yet is unrealistic for any attribute whose partworth is logically bounded (e.g., is necessarily positive or cannot be unboundedly large). A mixed logit is specified with partworths that are transformations of normally distributed terms, where the transformation induces bounds; examples include censored normals, log-normals, and SB distributions which are bounded on both sides. The model retains the computational advantages of joint normals while providing greater flexibility for the distributions of correlated partworths. The method is applied to data on customers’ choice among vehicles in stated choice experiments. The flexibility that the transformations allow is found to greatly improve the model, both in terms of fit and plausibility, without appreciably increasing the computational burden.
Train, K., "Mixed Logit with Bounded Distributions of Correlated Partworths ", Applications of Simulation Methods in Environmental Resource Economics , Chapter 7.
Authors: Sonnier
Train
Coders: Train
Last update
07/10/2012
Ranking
4
Runs
9
Visits
268
Value-at-Risk (Chapter 5: Computing VaR)
Abstract
Book description: To accommodate sweeping global economic changes, the risk management field has evolved substantially since the first edition of Value at Risk, making this revised edition a must. Updates include a new chapter on liquidity risk, information on the latest risk instruments and the expanded derivatives market, recent developments in Monte Carlo methods, and more. Value at Risk will help professional risk managers understand, and operate within, today’s dynamic new risk environment.
Hurlin, C., C. Perignon, "Value-at-Risk (Chapter 5: Computing VaR)", MacGraw-Hill, Third Edition.
Authors: Jorion
Coders: Hurlin
Perignon
Last update
03/19/2012
Ranking
44
Runs
63
Visits
328
The pernicious effects of contaminated data in risk management
Abstract
Banks hold capital to guard against unexpected surges in losses and long freezes in financial markets. The minimum level of capital is set by banking regulators as a function of the banks’ own estimates of their risk exposures. As a result, a great challenge for both banks and regulators is to validate internal risk models. We show that a large fraction of US and international banks uses contaminated data when testing their models. In particular, most banks validate their market risk model using profit-and-loss (P/L) data that include fees and commissions and intraday trading revenues. This practice is inconsistent with the definition of the employed market risk measure. Using both bank data and simulations, we find that data contamination has dramatic implications for model validation and can lead to the acceptance of misspecified risk models. Moreover, our estimates suggest that the use of contaminated data can significantly reduce (market-risk induced) regulatory capital.
Fresard, L., C. Perignon, and A. Wilhelmsson, "The pernicious effects of contaminated data in risk management", Journal of Banking and Finance, 35.
Authors: Fresard
Perignon
Wilhelmsson
Coders: Fresard
Perignon
Wilhelmsson
Last update
11/23/2012
Ranking
9999
Runs
N.A.
Visits
42
Techniques for Verifying the Accuracy of Risk Management Models
Abstract
Risk exposures are typically quantified in terms of a "Value at Risk" (VaR) estimate. A VaR estimate corresponds to a specific critical value of a portfolio's potential one-day profit and loss probability distribution. Given their function both as internal risk management tools and as potential regulatory measures of risk exposure, it is important to quantify the accuracy of an institution's VaR estimates. This study shows that the formal statistical procedures that would typically be used in performance-based VaR verification tests require large samples to produce a reliable assessment of a model's accuracy in predicting the size and likelihood of very low probability events. Verification test statistics based on historical trading profits and losses have very poor power in small samples, so it does not appear possible for a bank or its supervisor to verify the accuracy of a VaR estimate unless many years of performance data are available. Historical simulation-based verification test statistics also require long samples to generate accurate results: Estimates of 0.01 critical values exhibit substantial errors even in samples as large as ten years of daily data.
Hurlin, C., C. Perignon, "Techniques for Verifying the Accuracy of Risk Management Models", Journal of Derivatives, 3, 73-84.
Authors: Kupiec
Coders: Hurlin
Perignon
Last update
04/17/2012
Ranking
57
Runs
26
Visits
339
Outliers and GARCH Models in Financial Data
Abstract
We propose to extend the additive outlier (AO) identification procedure developed by Franses and Ghijsels(Franses, P.H., Ghijsels, H., 1999. Additive outliers, GARCH and forecasting volatility. International Journal of Forecasting, 15, 1–9) to take into account the innovative outliers (IOs) in a GARCH model. We apply it to three daily stock market indexes and examine the effects of outliers on the diagnostics of normality.
Charles, A., and O. Darné, D. Banulescu, E. Dumitrescu, "Outliers and GARCH Models in Financial Data", Economics Letters, 86, 347-352.
Authors: Charles
Darné
Coders: Charles
Darné
Banulescu
Dumitrescu
Last update
06/22/2012
Ranking
15
Runs
61
Visits
238
Extracting Factors from Heteroskedastic Asset Returns
Abstract
This paper proposes an alternative to the asymptotic principal components procedure of Connor and Korajczyk (Journal of Financial Economics, 1986) that is robust to time series heteroskedasticity in the factor model residuals. The new method is simple to use and requires no assumptions stronger than those made by Connor and Korajczyk. It is demonstrated through simulations and analysis of actual stock market data that allowing heteroskedasticity sometimes improves the quality of the extracted factors quite dramatically. Over the period from 1989 to 1993, for example, a single factor extracted using the Connor and Korajczyk method explains only 8.2% of the variation of the CRSP value-weighted index, while the factor extracted allowing heteroskedasticity explains 57.3%. Accounting for heteroskedasticity is also important for tests of the APT, with p-values sometimes depending strongly on the factor extraction method used.
Jones, S. C., "Extracting Factors from Heteroskedastic Asset Returns", Journal of Financial Economics, 62, 293-325.
Authors: Jones
Coders: Jones
Last update
11/17/2012
Ranking
30
Runs
17
Visits
81
Currency Crises Early Warning Systems: why they should be Dynamic
Abstract
This paper introduces a new generation of Early Warning Systems (EWS) which takes into account the dynamics, i.e. the persistence in the binary crisis indicator. We elaborate on Kauppi and Saikonnen (2008), which allows to consider several dynamic specifications by re- lying on an exact maximum likelihood estimation method. Applied so as to predict currency crises for fifteen countries, this new EWS turns out to exhibit significantly better predic- tive abilities than the existing models both within and out of the sample, thus vindicating dynamic models in the quest for optimal EWS.
Candelon, B., E. Dumitrescu, and C. Hurlin, "Currency Crises Early Warning Systems: why they should be Dynamic", Maastricht University.
Authors: Candelon
Dumitrescu
Hurlin
Coders: Candelon
Dumitrescu
Hurlin
Last update
06/04/2012
Ranking
45
Runs
64
Visits
112
Modeling State Credit Risks in Illinois and Indiana
Abstract
I use an open-source budget-simulation model to evaluate Illinois’s credit risk and to compare it to that of Indiana, a neighboring state generally believed to have better fiscal management. Based on a review of the history and theory of state credit performance, I assume that a state will default if the aggregate of its interest and pension costs reaches 30 percent of total revenues. In Illinois, this ratio is currently 10 percent, compared to 4 percent in Indiana. My analysis finds that neither state will reach the critical threshold in the next few years under any reasonable economic scenario, suggesting no material default risk. Over the longer term, Illinois has some chance of reaching the default threshold, but it would likely be able to take policy actions to lower the ratio before then. If market participants accept my finding that Illinois does not have material default risk, Illinois’s bond yields willfall, yielding cost savings for taxpayers as the state rolls over its debt.
Joffe, D. M., "Modeling State Credit Risks in Illinois and Indiana", Mercatus Center.
Authors: Joffe
Coders: Joffe
Last update
08/01/2013
Ranking
9999
Runs
N.A.
Visits
N.A.
Pitfalls in backtesting Historical Simulation VaR models
Abstract
Abstract Historical Simulation (HS) and its variant, the Filtered Historical Simulation (FHS), are the most popular Value-at-Risk forecast methods at commercial banks. These forecast methods are traditionally evaluated by means of the unconditional backtest. This paper formally shows that the unconditional backtest is always inconsistent for backtesting HS and FHS models, with a power function that can be even smaller than the nominal level in large samples. Our findings have fundamental implications in the determination of market risk capital requirements, and also explain Monte Carlo and empirical findings in previous studies. We also propose a data-driven weighted backtest with good power properties to evaluate HS and FHS forecasts. A Monte Carlo study and an empirical application with three US stocks confirm our theoretical findings. The empirical application shows that multiplication factors computed under the current regulatory framework are downward biased, as they inherit the inconsistency of the unconditional backtest.
Escanciano, J., and P. Pei, "Pitfalls in backtesting Historical Simulation VaR models", Journal of Banking and Finance, 36, 2233-2244.
Authors: Escanciano
Pei
Coders: Escanciano
Pei
Last update
02/22/2013
Ranking
9999
Runs
N.A.
Visits
32
A Generalized Asymmetric Student-t Distribution with Application to Financial Econometrics
Abstract
This paper proposes a new class of asymmetric Student-t (AST) distributions, and investigates its properties, gives procedures for estimation, and indicates applications in financial econometrics. We derive analytical expressions for the cdf, quantile function, moments, and quantities useful in financial econometric applications such as the Expected Shortfall. A stochastic representation of the distribution is also given. Although the AST density does not satisfy the usual regularity conditions for maximum likelihood estimation, we establish consistency, asymptotic normality and efficiency of ML estimators and derive an explicit analytical expression for the asymptotic covariance matrix. A Monte Carlo study indicates generally good finite-sample conformity with these asymptotic properties.
Colletaz, G., "A Generalized Asymmetric Student-t Distribution with Application to Financial Econometrics", Journal of Econometrics, 157, 297-305.
Authors: Zhu
Galbraith
Coders: Colletaz
Last update
05/05/2012
Ranking
38
Runs
6
Visits
95
Determining the Number of Factors in Approximate Factors Models
Abstract
In this paper we develop some econometric theory for factor models of large dimensions. The focus is the determination of the number of factors (r), which is an unresolved issue in the rapidly growing literature on multifactor models. We first establish the convergence rate for the factor estimates that will allow for consistent estimation of r. We then propose some panel criteria and show that the number of factors can be consistently estimated using the criteria. The theory is developed under the framework of large cross-sections (N) and large time dimensions (T). No restriction is imposed on the relation between N and T. Simulations show that the proposed criteria have good finite sample properties in many configurations of the panel data encountered in practice.
Hurlin, C., "Determining the Number of Factors in Approximate Factors Models", Econometrica, 70, 191-221.
Authors: Bai
Ng
Coders: Hurlin
Last update
01/29/2013
Ranking
39
Runs
66
Visits
230
Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties
Abstract
We consider pooling cross-section time series data for testing the unit root hypothesis. The degree of persistence in individual regression error, the intercept and trend coefficient are allowed to vary freely across individuals. As both the cross-section and time series dimensions of the panel grow large, the pooled t-statistic has a limiting normal distribution that depends on the regression specification but is free from nuisance parameters. Monte Carlo simulations indicate that the asymptotic results provide a good approximation to the test statistics in panels of moderate size, and that the power of the panel-based unit root test is dramatically higher, compared to performing a separate unitroottest for each individual time series.
Hurlin, C., "Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties", Journal of Econometrics, 108, 1-24.
Authors: Levin
Lin
Chu
Coders: Hurlin
Last update
06/28/2012
Ranking
37
Runs
220
Visits
322
Maximum Likelihood Methods for Models of Markets in Disequilibrium
Abstract
For the abstract, please click on: http://www.jstor.org/discover/10.2307/1914215?uid=3738016&uid=2&uid=4&sid=56146953873
Hurlin, C., "Maximum Likelihood Methods for Models of Markets in Disequilibrium", Econometrica, 42, 1013-1030.
Authors: Maddala
Nelson
Coders: Hurlin
Last update
02/15/2013
Ranking
50
Runs
85
Visits
391
A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test
Abstract
The panel data unit root test suggested by Levin and Lin (LL) has been widely used in several applications, notably in papers on tests of the purchasing power parity hypothesis. This test is based on a very restrictive hypothesis which is rarely ever of interest in practice. The Im–Pesaran–Shin (IPS) test relaxes the restrictive assumption of the LL test. This paper argues that although the IPS test has been offered as a generalization of the LL test, it is best viewed as a test for summarizing the evidence from a number of independent tests of the sample hypothesis. This problem has a long statistical history going back to R. A. Fisher. This paper suggests the Fisher test as a panel data unit root test, compares it with the LL and IPS tests, and the Bonferroni bounds test which is valid for correlated tests. Overall, the evidence points to the Fisher test with bootstrap-based critical values as the preferred choice. We also suggest the use of the Fisher test for testing stationarity as the null and also in testing for cointegration in panel data.
Hurlin, C., "A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test", Oxford Bulletin of Economics and Statistics, 61, 631-652.
Authors: Maddala
Wu
Coders: Hurlin
Last update
10/08/2012
Ranking
59
Runs
217
Visits
129
Unit Root Tests for Panel Data
Abstract
This paper develops unit root tests for panel data. These tests are devised under more general assumptions than the tests previously proposed. First, the number of groups in the panel data is assumed to be either finite or infinite. Second, each group is assumed to have different types of nonstochastic and stochastic components. Third, the time series spans for the groups are assumed to be all different. Fourth, the alternative where some groups have a unit root and others do not can be dealt with by the tests. The tests can also be used for the null of stationarity and for cointegration, once relevant changes are made in the model, hypotheses, assumptions and underlying tests. The main idea for our unit root tests is to combine p-values from a unit root test applied to each group in the panel data. Combining p-values to formulate tests is a common practice in meta-analysis. This paper also reports the finite sample performance of our combination unit root tests and Im et al.'s [Mimeo (1995)] t-bar test. The results show that most of the combination tests are more powerful than the t-bar test in finite samples. Application of the combination unit root tests to the post-Bretton Woods US real exchange rate data provides some evidence in favor of the PPP hypothesis.
Hurlin, C., "Unit Root Tests for Panel Data", Journal of International Money and Finance, 20, 249-272.
Authors: Choi
Coders: Hurlin
Last update
10/08/2012
Ranking
60
Runs
62
Visits
261
Testing for Unit Roots in Heterogeneous Panels
Abstract
This paper proposes unit root tests for dynamic heterogeneous panels based on the mean of individual unit root statistics. In particular it proposes a standardized t-bar test statistic based on the (augmented) Dickey–Fuller statistics averaged across the groups. Under a general setting this statistic is shown to converge in probability to a standard normal variate sequentially with T (the time series dimension) →∞, followed by N (the cross sectional dimension) →∞. A diagonal convergence result with T and N→∞ while N/T→k,k being a finite non-negative constant, is also conjectured. In the special case where errors in individual Dickey–Fuller (DF) regressions are serially uncorrelated a modified version of the standardized t-bar statistic is shown to be distributed as standard normal as N→∞ for a fixed T, so long as T>5 in the case of DF regressions with intercepts and T>6 in the case of DF regressions with intercepts and linear time trends. An exact fixed N and T test is also developed using the simple average of the DF statistics. Monte Carlo results show that if a large enough lag order is selected for the underlying ADF regressions, then the small sample performances of the t-bar test is reasonably satisfactory and generally better than the test proposed by Levin and Lin (Unpublished manuscript, University of California, San Diego, 1993).
Hurlin, C., "Testing for Unit Roots in Heterogeneous Panels ", Journal of Econometrics, 115, 53-74.
Authors: Im
Pesaran
Shin
Coders: Hurlin
Last update
10/08/2012
Ranking
61
Runs
57
Visits
106
Testing for a Unit Root in Panels with Dynamic Factors
Abstract
This paper studies testing for a unit root for large n and T panels in which the cross-sectional units are correlated. To model this cross-sectional correlation, we assume that the data are generated by an unknown number of unobservable common factors. We propose unit root tests in this environment and derive their (Gaussian) asymptotic distribution under the null hypothesis of a unit root and local alternatives. We show that these tests have significant asymptotic power when the model has no incidental trends. However, when there are incidental trends in the model and it is necessary to remove heterogeneous deterministic components, we show that these tests have no power against the same local alternatives. Through Monte Carlo simulations, we provide evidence on the finite sample properties of these new tests.
Hurlin, C., "Testing for a Unit Root in Panels with Dynamic Factors", Journal of Econometrics, 122, 81-126.
Authors: Moon
Perron
Coders: Hurlin
Last update
10/08/2012
Ranking
62
Runs
399
Visits
124
Panel Smooth Transition Regression Models
Abstract
We develop a non-dynamic panel smooth transition regression model with fixed individual effects. The model is useful for describing heterogenous panels, with re- gression coefficients that vary across individuals and over time. Heterogeneity is allowed for by assuming that these coefficients are continuous functions of an ob- servable variable through a bounded function of this variable and fluctuate between a limited number (often two) of “extreme regimes”. The model can be viewed as a generalization of the threshold panel model of Hansen (1999). We extend the modelling strategy for univariate smooth transition regression models to the panel context. This comprises of model specification based on homogeneity tests, parame- ter estimation, and diagnostic checking, including tests for parameter constancy and no remaining nonlinearity. The new model is applied to describe firms’ investment decisions in the presence of capital market imperfections.
Colletaz, G., "Panel Smooth Transition Regression Models", SSE/EFI working paper series in economics and finance, n° 604..
Authors: Gonzalez
van Dijk
Terasvirta
Coders: Colletaz
Last update
07/16/2015
Ranking
9999
Runs
205
Visits
N.A.
Tests of Conditional Predictive Ability
Abstract
We propose a framework for out-of-sample predictive ability testing and forecast selection designed for use in the realistic situation in which the forecasting model is possibly misspecified, due to unmodeled dynamics, unmodeled heterogeneity, incorrect functional form, or any combination of these. Relative to the existing literature (Diebold and Mariano (1995) and West (1996)), we introduce two main innovations: (i) We derive our tests in an environment where the finite sample properties of the estimators on which the forecasts may depend are preserved asymptotically. (ii) We accommodate conditional evaluation objectives (can we predict which forecast will be more accurate at a future date?), which nest unconditional objectives (which forecast was more accurate on average?), that have been the sole focus of previous literature. As a result of (i), our tests have several advantages: they capture the effect of estimation uncertainty on relative forecast performance, they can handle forecasts based on both nested and nonnested models, they allow the forecasts to be produced by general estimation methods, and they are easy to compute. Although both unconditional and conditional approaches are informative, conditioning can help fine-tune the forecast selection to current economic conditions. To this end, we propose a two-step decision rule that uses current information to select the best forecast for the future date of interest. We illustrate the usefulness of our approach by comparing forecasts from leading parameter-reduction methods for macroeconomic forecasting using a large number of predictors.
Giacomini, R., "Tests of Conditional Predictive Ability ", Econometrica, 74, 1545-1578.
Authors: White
Giacomini
Coders: Giacomini
Last update
07/04/2012
Ranking
25
Runs
17
Visits
67
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