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REVIEW 3 major objections 5 minor 1 cited by

Nonlinear Boosting with Multiple Testing in High-Dimensional Generalised Linear Models with Binary Responses

T0 review · 3 major / 5 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read A stagewise boosting procedure can recover the exact sparse model in high-dimensional binary-response GLMs, and its post-selection estimator matches an oracle that knows the true signals in advance.

desk verdict Careful, honest extension of BMT to binary-response GLMs — the exact-recovery theorem lives or dies with the population-dominance Assumption A4, but the paper says so and deserves a serious referee. read the letter →

arxiv 2607.22440 v1 pith:WB5AXGE6 submitted 2026-07-24 econ.EM

Charisios Grivas , George Kapetanios , Zacharias Psaradakis , Vasilis Sarafidis , Marian Vavra , Alexia Ventouri This is my paper · ORCID
classification econ.EM MSC 62F0762F1262J12
keywords variableselectionbinaryresponsegeneralisedlinearmodelsboostingmultipletestingoraclepropertyhighdimensionalityWaldstatistic
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper extends boosting-with-multiple-testing from linear to binary-response generalized linear models. It claims that by adding one covariate at a time based on the largest conditional Wald statistic and filtering through a family-wise threshold, the procedure asymptotically selects all true signals and no others. If this is right, post-selection maximum likelihood inference is as efficient as if the correct sparse model were known. The argument rests on a population measure of conditional strength, the Wald noncentrality, and on a dominance gap requiring true signals to stand out from pseudo-signals at every stage.

What carries the argument

The population Wald noncentrality NC*_j(S) = sqrt(T)|theta*_j(S)| / sqrt(V*_theta,theta,j(S)), the asymptotic mean of the stagewise Wald statistic for adding covariate j to the current model. The procedure's identification and selection are driven by this quantity: a uniform approximation result bounds the maximal deviation of sample Wald statistics from it, and a stagewise dominance condition (Assumption A4) requires every remaining true signal to exceed every non-signal in this measure by a margin that also dominates the approximation error. The multiple-testing threshold c_T, chosen with slack beyond the approximation error, acts as both screening device and stopping rule.

What would settle it

Construct a design like that in Appendix C.3 where an inactive covariate has a larger first-stage Wald statistic than any true signal (for example, a covariate with correlation 3/5 with two signals and -1/4 with a third). For a small true coefficient, BMT will select this covariate first with probability tending to one, so the selected set cannot be exactly the true support; simulating this design and recording the first selection would refute the exact-recovery claim.

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Extended reading notes

Core claim

The central claim is that the BMT algorithm—forward stagewise inclusion of the single most significant covariate, subject to a multiple-testing threshold that also serves as the stopping rule—achieves exact recovery: with probability tending to one, its selected set is exactly the union of the always-in controls and the true signal set. Conditional on that event, the post-BMT maximum likelihood estimator has the same first-order limiting distribution as an oracle estimator that knows the support in advance. The proof works by showing that the stagewise Wald statistics concentrate uniformly around their population expectations, and that the population ordering of these expectations is preserv

Load-bearing premise

At every stage before all true signals are selected, every remaining true signal must have a population conditional strength that exceeds every remaining non-signal's strength by a margin large enough to dominate sampling noise—essentially, the population ordering the algorithm is supposed to discover is already guaranteed by the assumptions.

Editorial extensions

If this is right

  • In sparse binary GLMs with a fixed number of signals, BMT selects all true covariates and no false ones with probability tending to one, for both logit and probit links.
  • The post-BMT (quasi-)MLE is asymptotically equivalent to the infeasible oracle estimator, so confidence intervals from the selected model are valid to first order.
  • The procedure does not rely on sparsity-inducing penalties or marginal screening; it is designed to resist pseudo-signals correlated with true signals.
  • The framework extends to general one-parameter exponential family GLMs, preserving exact recovery and oracle inference under suitable conditions.
  • Monte Carlo evidence indicates BMT yields smaller models and lower estimation error than OCMT and LASSO in the studied designs, and an inflation-forecasting application uses five predictors with competitive out-of-sample accuracy.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the dominance margin fails only mildly, a natural extension is to derive finite-sample or high-probability bounds on the number of false selections under weaker separation, rather than only exact recovery as the paper states.
  • The one-at-a-time conditional updating resembles orthogonal matching pursuit and forward stepwise regression; the paper's population noncentrality is a nonlinear analogue of partial correlation, suggesting the machinery might transfer to other single-index models.
  • The theory requires the threshold to have slack beyond the uniform approximation error; a practical, data-driven calibration of this slack would be a testable extension.
  • In the local small-index regime, dominance reduces to a partial-correlation gap, implying practitioners could pre-screen designs by computing these gaps before deciding whether BMT is appropriate.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper extends the boosting-with-multiple-testing (BMT) framework to high-dimensional binary-response GLMs. Selection is forward-stagewise: at each stage, the BMT procedure adds the covariate with the largest conditional Wald statistic among those passing a family-wise multiple-testing threshold. The main theoretical results state that, under stated assumptions, BMT selects exactly the true sparse set with probability tending to one (Theorem 4), that the post-selection MLE is asymptotically equivalent to the oracle estimator (Theorem 5), and that uniform stochastic control of stagewise statistics holds under high-level approximation conditions (Theorems 1-2). The paper also contains a local- and global-regime analysis of dominance conditions, a comparison with binary LASSO, an extension to exponential-family GLMs, Monte Carlo evidence, and an empirical inflation application using FRED-MD data.

Significance. The theoretical apparatus is substantial: the proofs are detailed and transparently conditional on explicitly stated assumptions, and the paper is honest about the strength of its conditions. The extension of BMT to nonlinear GLMs with oracle post-selection inference is a useful contribution if the assumptions are credible. The comparison with binary LASSO irrepresentability (Appendix C), including a design where BMT fails and LASSO succeeds, is particularly valuable because it delineates the scope of the method. However, the central exact-recovery claim is heavily conditional on a stagewise population dominance condition that is close to the desired selection ordering; the paper does not verify that condition in the simulation or empirical settings, and the empirical application uses HAC standard errors for which the primitive verification of the key approximation assumption is explicitly not provided. These gaps limit the practical force of the theoretical claims.

major comments (3)
  1. [§5.2, Assumption A4 (Eqs. 13-14); §6] The exact-recovery theorem is logically valid, but its content is almost entirely delegated to Assumption A4: at every intermediate set, every remaining signal must have population Wald noncentrality NC*_j(S) exceeding every remaining non-signal's NC*_m(S) by d_T that dominates the maximal approximation error E_T. This is essentially a population-level version of the ordering the algorithm is supposed to discover. The primitive conditions in Section 6 do not remove the concern: Assumption L3 (Eq. 38) is a partial-correlation gap of the same shape as A4, and Assumption GM1 (Eq. 40) is a variance-normalized coefficient dominance condition. The paper does not verify A4 in the Monte Carlo DGP or the empirical data and gives no diagnostic check. The severity of A4 is underscored by the paper's own Theorem C.2, which presents a simple Gaussian design with one inactive covariate in which BMT se
  2. [Appendix D and §8.1] Appendix D (Theorem D.1 and the final paragraph) gives primitive sufficient conditions for Assumption A3.4 only for observed-information standard errors under the information equality and for one-period sandwich standard errors with serially uncorrelated scores. It explicitly states that kernel HAC estimators require a separate bandwidth-dependent argument and remain at the high-level A3.4 level. The empirical illustration in Section 8.1 (footnote 6) uses a kernel HAC covariance estimator with the Bartlett kernel and Newey-West bandwidth. Thus the verified theory does not cover the empirical implementation's standard errors. Please either extend the uniform HAC approximation with explicit bandwidth conditions, or change the application to a covered standard-error estimator; at minimum, state this coverage gap in the main text.
  3. [§7.1, Assumption A5] The simulation threshold c_T = Phi^{-1}(1-0.05/(2aT)) with a=1,2 is of order sqrt(log T). Under the exponential-tail regime of Theorem D.1, E_T = O_p(sqrt(log(p∨T))), and Assumption A5 requires E_T/c_T -> 0 in probability. In the experiments p <= 400 and T <= 300, so log p and log T are comparable; no verification is provided that A5 holds for the implemented threshold. The favorable simulation outcomes suggest it may hold in these designs, but the theoretical link between the simulations and the exact-recovery theorem is not established. Please add a discussion or check of A5 for the chosen threshold, or use a threshold with provable slack.
minor comments (5)
  1. [§2.1] The estimate bse_j(S) is described verbally but not explicitly defined by an equation. A displayed definition of the standard-error estimator would improve clarity, especially because the theory distinguishes between information, sandwich, and HAC versions.
  2. [§4, Theorem 1] Theorem 1 is essentially a restatement of Assumption A3.4 via the definition of E_T. This is fine, but the main text should make clear that the uniform approximation rate is an assumption except under the primitive conditions verified in Appendix D.
  3. [§7, Tables 1-5] The summary statistics are computed over 20 design points but no Monte Carlo standard errors are reported for the medians or RMSE. Given the small numbers of replications for some extreme cases, a few standard errors or confidence intervals would be useful.
  4. [§8.2] The 85/15 sample split is described, but the total number of observations in the evaluation sample is not stated. Reporting T for training and evaluation periods would help interpret the out-of-sample metrics.
  5. [§6.2, Assumption G2] Remark 1 correctly notes that Assumption G2 alone does not imply dominance. This is an important caveat and should be echoed in the concluding section, where 'verifiable via multiple primitive routes' could be read too optimistically.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity found; core results are conditional theorems from explicit population-order and approximation assumptions, with primitive verification in the appendix.

full rationale

The derivation chain is conditional rather than circular. The central exact-recovery result (Theorem 4) is proved by induction from Assumption A4 (Eqs. 13-14), which is a population condition on pseudo-true Wald noncentralities; it is not fitted from the BMT output and is not defined in terms of the selected set, so the theorem does not reduce to its own conclusion. The same holds for the oracle result (Theorem 5), which is conditional on consistent selection and standard oracle regularity. Section 6's primitive conditions (L1-L3, GM1, G1/G2) are dominance-type assumptions about the design; the lemmas give explicit algebraic derivations of A4 from them rather than renaming the conclusion. Assumption A3.4 is admittedly high-level, and Theorem 1's proof invokes it directly; however, A3.4 is not smuggled in as a result: it is labeled as an assumption and Appendix D derives it from primitive mixing and moment conditions (A1-A3.3), so the uniform approximation, while initially assumed, receives independent verification. The self-citation to Kapetanios et al. (2026) for the linear BMT analogue is not load-bearing: Theorem 4's proof is self-contained in Appendix A.5. The unverified status of A4 for the empirical/simulated designs and the HAC-standard-error gap in Appendix D are valid robustness limitations, not circularity.

Assumptions & free parameters 2 free parameters · 9 assumptions · 0 invented entities

The paper contributes a theorem that transfers population dominance of conditional Wald noncentralities to sample selection; it does not derive that dominance from first principles. The assumptions are explicit, but A4/GM1 and the uniform Wald approximation carry most of the weight. The local-regime conditions are more primitive but still assume a partial-correlation gap. No new physical or structural entities are introduced.

free parameters (2)
  • Multiple-testing threshold c_T = Φ^{-1}(1 - 0.05/(2T)) at stage 1; Φ^{-1}(1 - 0.05/(4T)) for later stages
    The theory only requires c_T → ∞, c_T = o(√T), and domination of E_T. The specific 5% level and stage-dependent a=1/2 are user choices borrowed from OCMT practice, not derived from the theory.
  • k_max (maximum number of stages)
    User-specified; theory requires k_max ≥ k and fixed. Not fitted to data, but it is an algorithmic tuning choice that affects stopping behavior and is not reported in the simulation tables.
assumptions (9)
  • domain assumption Single-index GLM correct specification: E(y_t|x_t) = G(x_t' β0)
    Maintained throughout (Eq. 1). Ensures that once S0 is included, remaining candidates have zero population conditional strength.
  • domain assumption A1.1: strict stationarity and strong mixing of the triangular array
    Supports maximal inequalities and uniform laws used in the proofs.
  • domain assumption A2-ET or A2-PM envelope/moment conditions
    Requires sub-Gaussian or sufficiently high polynomial moments for score, Hessian, and third-derivative envelopes.
  • domain assumption A3.2-A3.3: uniform identifiability, nonsingular information, and long-run sandwich matrices
    Ensures pseudo-true parameters are well separated and standard errors are well behaved uniformly.
  • ad hoc to paper A3.4: high-level uniform Wald approximation with rate E_T
    The central approximation assumption over the admissible class. Appendix D verifies it for information and one-period sandwich standard errors, but explicitly excludes kernel HAC, which the empirical application relies on.
  • ad hoc to paper A4/A4-1/GM1: population stagewise dominance of signals over non-signals in NC*
    Eqs. (13)-(14) and Section 6. This is the load-bearing condition that makes exact recovery possible; it is structurally close to the theorem's conclusion.
  • domain assumption A5: threshold c_T dominates the maximal approximation error with margin
    Pr(E_T ≤ (1-η0)c_T) → 1. Needed to kill population-null covariates and to stop after the true model is found.
  • domain assumption A6: oracle CLT and nonsingular oracle Hessian
    Standard regularity for the oracle estimator; needed for the oracle inference result.
  • domain assumption Local regime L1-L3 (optional): small index and partial-correlation gap
    Alternative primitive route to dominance; L1 requires |x_t' β0| ≤ δ_T with probability tending to one, which is strong for binary GLMs with moderate coefficients.

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Pith. "Pith review of Nonlinear Boosting with Multiple Testing in High-Dimensional Generalised Linear Models with Binary Responses." pith.science (2026). https://pith.science/paper/WB5AXGE6

@misc{pith2026260722440,
  author       = {Pith},
  title        = {Pith review of: Nonlinear Boosting with Multiple Testing in High-Dimensional Generalised Linear Models with Binary Responses},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WB5AXGE6}},
  note         = {Machine review of arXiv:2607.22440}
}
read the original abstract

This paper proposes a nonlinear boosting with multiple testing (BMT) approach to variable selection in high-dimensional generalised linear models with binary responses. At each stage of the BMT procedure, the model is updated by adding only the most significant covariate, conditional on those already selected in previous stages, while taking into account the multiple testing nature of the problem. It is shown that, under the stated conditions, the BMT procedure selects all covariates whose true coefficients are nonzero, and no other covariates, with probability tending to one. Furthermore, the procedure enjoys an oracle property, in the sense that the post-BMT maximum likelihood estimator of the parameters of the model is asymptotically equivalent to an oracle estimator that knows the correct sparse model in advance. Monte Carlo experiments demonstrate that BMT outperforms competing methods, delivering high covariate-selection accuracy and low parameter estimation error. An empirical example illustrates that BMT delivers a predictive model for the probability that U.S. inflation exceeds a given threshold over a 12-month horizon which has very good out-of-sample performance.

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Forward citations

Cited by 1 Pith paper

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Reference graph

Works this paper leans on

300 extracted references · cited by 1 Pith paper

  1. [1]

    Econometric Theory , year =

    Dendramis, Yiannis and Giraitis, Liudas and Kapetanios, George , title =. Econometric Theory , year =

  2. [2]

    Merlev. A. Probability Theory and Related Fields , year =

  3. [3]

    Zeitschrift f

    Yokoyama, Ryozo , title =. Zeitschrift f. 1980 , volume =

    1980

  4. [4]

    The Annals of Probability , year =

    Merlev. The Annals of Probability , year =

  5. [5]

    American Economic Journal: Macroeconomics , volume=

    Evaluating the classification of economic activity into recessions and expansions , author=. American Economic Journal: Macroeconomics , volume=

  6. [6]

    Journal of Money, Credit and Banking , volume=

    Forecasting national recessions using state-level data , author=. Journal of Money, Credit and Banking , volume=

  7. [7]

    Journal of Finance , volume=

    The term structure as a predictor of real economic activity , author=. Journal of Finance , volume=

  8. [8]

    Handbook of Research Methods and Applications in Macroeconomic Forecasting , pages=

    Nowcasting recession risk , author=. Handbook of Research Methods and Applications in Macroeconomic Forecasting , pages=. 2024 , publisher=

    2024

Show all 300 references
  1. [9]

    2020 , publisher=

    Macroeconomic Forecasting in the Era of Big Data , author=. 2020 , publisher=

    2020

  2. [10]

    Federal Reserve Bank of St

    A measure of price pressures , author=. Federal Reserve Bank of St. Louis Review , volume=

  3. [11]

    and Galakis, John and Vrontos, Ioannis D

    Vrontos, Spyridon D. and Galakis, John and Vrontos, Ioannis D. , journal=. Modeling and predicting

  4. [12]

    , journal=

    Fenga, Livio and Politis, Dimitris N. , journal=

  5. [13]

    Journal of Econometrics , volume=

    Forecasting with factor-augmented regression: A frequentist model averaging approach , author=. Journal of Econometrics , volume=

  6. [14]

    International journal of forecasting , volume=

    Macroeconomic forecasting using penalized regression methods , author=. International journal of forecasting , volume=

  7. [15]

    and Mendes, Eduardo F

    Medeiros, Marcelo C. and Mendes, Eduardo F. , journal=. Adaptive

  8. [16]

    Tuning parameter selection for the adaptive

    Kwon, Sunghoon and Lee, Sangin and Na, Okyoung , journal=. Tuning parameter selection for the adaptive

  9. [17]

    Journal of the Royal Statistical Society B , volume=

    Regression Shrinkage and Selection Via the Lasso , author=. Journal of the Royal Statistical Society B , volume=

  10. [18]

    Journal of the American statistical Association , volume=

    Variable selection via nonconcave penalized likelihood and its oracle properties , author=. Journal of the American statistical Association , volume=

  11. [19]

    Discussions

    Least angle regression. Discussions. Authors reply , author=. Annals of Statistics , volume=

  12. [20]

    Journal of the American Statistical Association , volume=

    Exact post-selection inference for sequential regression procedures , author=. Journal of the American Statistical Association , volume=

  13. [21]

    Journal of the Royal Statistical Society B , year =

    Park, Mee Young and Hastie, Trevor , title =. Journal of the Royal Statistical Society B , year =

  14. [22]

    Journal of the Royal Statistical Society B , volume=

    Regularization and variable selection via the elastic net , author=. Journal of the Royal Statistical Society B , volume=

  15. [23]

    Econometrica , volume=

    A one covariate at a time, multiple testing approach to variable selection in high-dimensional linear regression models , author=. Econometrica , volume=

  16. [24]

    Annals of statistics , volume=

    Can one estimate the conditional distribution of post-model-selection estimators? , author=. Annals of statistics , volume=

  17. [25]

    Econometric Theory , volume=

    Can one estimate the unconditional distribution of post-model-selection estimators? , author=. Econometric Theory , volume=

  18. [26]

    Journal of econometrics , volume=

    Factor-adjusted regularized model selection , author=. Journal of econometrics , volume=

  19. [27]

    Biometrika , volume=

    Bias of some commonly-used time series estimates , author=. Biometrika , volume=

  20. [28]

    2008 , publisher=

    Asymptotic Theory of Statistics and Probability , author=. 2008 , publisher=

    2008

  21. [29]

    1986 , publisher=

    Density Estimation for Statistics and Data Analysis , author=. 1986 , publisher=

    1986

  22. [30]

    International Journal of Forecasting , volume=

    Forecasting market returns: bagging or combining? , author=. International Journal of Forecasting , volume=

  23. [31]

    Econometric Reviews , volume=

    Bagging or combining (or both)? An analysis based on forecasting US employment growth , author=. Econometric Reviews , volume=

  24. [32]

    International Journal of Forecasting , volume=

    Selecting exchange rate fundamentals by bootstrap , author=. International Journal of Forecasting , volume=

  25. [33]

    Journal of the American Statistical Association , volume=

    How useful is bagging in forecasting economic time series? A case study of US consumer price inflation , author=. Journal of the American Statistical Association , volume=

  26. [34]

    International journal of forecasting , volume=

    Bagging exponential smoothing methods using STL decomposition and Box--Cox transformation , author=. International journal of forecasting , volume=

  27. [35]

    European Journal of Operational Research , volume=

    Exploring the sources of uncertainty: Why does bagging for time series forecasting work? , author=. European Journal of Operational Research , volume=

  28. [36]

    Handbooks , year=

    State of the art of inflation targeting , author=. Handbooks , year=

  29. [37]

    Journal of Business and Economic Statistics , volume=

    M. Journal of Business and Economic Statistics , volume=

  30. [38]

    Econometrica , volume=

    Heteroskedasticity and autocorrelation consistent covariance matrix estimation , author=. Econometrica , volume=

  31. [39]

    The Federal Reserve Board, Washington, DC , year=

    Monetary policy under uncertainty , author=. The Federal Reserve Board, Washington, DC , year=

  32. [40]

    Econometric theory , volume=

    A warp-speed method for conducting Monte Carlo experiments involving bootstrap estimators , author=. Econometric theory , volume=

  33. [41]

    Econometric Theory , volume=

    The approximate moments of the least squares estimator for the stationary autoregressive model under a general error distribution , author=. Econometric Theory , volume=

  34. [42]

    Journal of Business and Economic Statistics , volume=

    Correcting estimation bias in dynamic term structure models , author=. Journal of Business and Economic Statistics , volume=

  35. [43]

    Casella, G and Berger, R L , year=

  36. [44]

    Upton, G and Cook, I , year=

  37. [45]

    Brockwell, Peter J and Davis, Richard A , year=

  38. [46]

    Yule, George Udny , journal=

  39. [47]

    Journal of the Royal Statistical Society

    Note on a generalization of the large sample goodness of fit test for linear autoregressive schemes , author=. Journal of the Royal Statistical Society. Series B (Methodological) , volume=

  40. [48]

    Econometrica , volume=

    The summation of random causes as the source of cyclic processes , author=. Econometrica , volume=

  41. [49]

    Journal of Business & Economic Statistics , volume=

    Generalized shrinkage methods for forecasting using many predictors , author=. Journal of Business & Economic Statistics , volume=

  42. [50]

    Econometric Reviews , volume=

    The benefits of bagging for forecast models of realized volatility , author=. Econometric Reviews , volume=

  43. [51]

    Pattern Recognition , volume=

    Bagging for linear classifiers , author=. Pattern Recognition , volume=

  44. [52]

    Computational statistics & data analysis , volume=

    Trimmed bagging , author=. Computational statistics & data analysis , volume=

  45. [53]

    Econometric Reviews , volume=

    Robustify financial time series forecasting with bagging , author=. Econometric Reviews , volume=

  46. [54]

    McCracken, Michael W and Ng, Serena , journal=

  47. [55]

    International Journal of Forecasting , volume=

    Macroeconomic forecasting for Australia using a large number of predictors , author=. International Journal of Forecasting , volume=

  48. [56]

    AStA Advances in Statistical Analysis , volume=

    Nowcasting GDP using machine learning methods , author=. AStA Advances in Statistical Analysis , volume=

  49. [57]

    Journal of Econometrics , volume=

    Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence , author=. Journal of Econometrics , volume=

  50. [58]

    Journal of the American Statistical Association , volume=

    Cross-validation: what does it estimate and how well does it do it? , author=. Journal of the American Statistical Association , volume=

  51. [59]

    Degrees of Freedom

    On the “Degrees of Freedom” of the LASSO , author=. The Annals of Statistics , volume=

  52. [60]

    Journal of Statistical Computation and Simulation , volume=

    LASSO order selection for sparse autoregression: a bootstrap approach , author=. Journal of Statistical Computation and Simulation , volume=

  53. [61]

    Econometric Reviews , volume=

    Adaptive LASSO estimation for ARDL models with GARCH innovations , author=. Econometric Reviews , volume=

  54. [62]

    Journal of the Korean Statistical Society , volume=

    Tuning parameter selection for the adaptive Lasso in the autoregressive model , author=. Journal of the Korean Statistical Society , volume=

  55. [63]

    Economics Letters , volume=

    Time series cross-validation: A theoretical result and finite sample performance , author=. Economics Letters , volume=

  56. [64]

    Journal of Econometrics , volume=

    Nonlinear models for strongly dependent processes with financial applications , author=. Journal of Econometrics , volume=

  57. [65]

    Oxford Bulletin of Economics and Statistics , volume=

    Quantile autoregressive distributed lag model with an application to house price returns , author=. Oxford Bulletin of Economics and Statistics , volume=

  58. [66]

    Giacomini, Raffaella and Politis, Dimitris N and White, Halbert , journal=

  59. [67]

    Hollo, Daniel and Kremer, Manfred and Lo Duca, Marco , volume=

  60. [68]

    Econometric Reviews , volume=

    Inference for impulse response coefficients from multivariate fractionally integrated processes , author=. Econometric Reviews , volume=

  61. [69]

    Journal of Forecasting , volume=

    Forecast combination for euro area inflation: a cure in times of crisis? , author=. Journal of Forecasting , volume=

  62. [70]

    International Journal of Forecasting , volume=

    Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy? , author=. International Journal of Forecasting , volume=

  63. [71]

    Journal of Econometrics , volume=

    On the selection of forecasting models , author=. Journal of Econometrics , volume=

  64. [72]

    Economic Modelling , volume=

    Forecasting inflation through a bottom-up approach: How bottom is bottom? , author=. Economic Modelling , volume=

  65. [73]

    Dolmas, Jim , journal=

  66. [74]

    University of Alberta Working Paper , year=

    A Cautionary Tale of Fat Tails , author=. University of Alberta Working Paper , year=

  67. [75]

    Macroeconomic Dynamics , volume=

    Fat-tail distributions and business-cycle models , author=. Macroeconomic Dynamics , volume=

  68. [76]

    Econometrica , volume=

    Quantile and probability curves without crossing , author=. Econometrica , volume=

  69. [77]

    Annals of Probability , year =

    Chernozhukov, Victor and Chetverikov, Denis and Kato, Kengo , title =. Annals of Probability , year =

  70. [78]

    and Watson, Mark W

    Stock, James H. and Watson, Mark W. , title =. Journal of the American Statistical Association , year =

  71. [79]

    Handbook of Statistics , volume=

    Time series quantile regressions , author=. Handbook of Statistics , volume=

  72. [80]

    Journal of applied Econometrics , volume=

    Maximum likelihood estimation of STAR and STAR-GARCH models: theory and Monte Carlo evidence , author=. Journal of applied Econometrics , volume=

  73. [81]

    Deb, Partha , journal=

  74. [82]

    International Economic Review , volume=

    The transmission mechanism in good and bad times , author=. International Economic Review , volume=

  75. [83]

    Journal of Time Series Analysis , volume=

    Multivariate quantile impulse response functions , author=. Journal of Time Series Analysis , volume=

  76. [84]

    Journal of the American Statistical Association , volume=

    The bias of autoregressive coefficient estimators , author=. Journal of the American Statistical Association , volume=

  77. [85]

    Schweikert, Karsten , journal=

  78. [86]

    Journal of Econometrics , volume=

    Specification tests of parametric dynamic conditional quantiles , author=. Journal of Econometrics , volume=

  79. [87]

    Journal of Econometrics , volume=

    Jackknife estimation of stationary autoregressive models , author=. Journal of Econometrics , volume=

  80. [88]

    Subsampling inference on quantile regression processes , author=. Sankhy

  81. [89]

    Journal of Econometrics , volume=

    Estimating structural changes in regression quantiles , author=. Journal of Econometrics , volume=

  82. [90]

    Tsong, Ching Chuan and Lee, Cheng Feng , journal=

  83. [91]

    Journal of the American Statistical Association , volume=

    Unit root quantile autoregression inference , author=. Journal of the American Statistical Association , volume=

  84. [92]

    Oxford Bulletin of Economics and Statistics , volume=

    Quantile aggregation of density forecasts , author=. Oxford Bulletin of Economics and Statistics , volume=

  85. [93]

    Journal of Empirical Finance , volume=

    Quantile forecasts of daily exchange rate returns from forecasts of realized volatility , author=. Journal of Empirical Finance , volume=

  86. [94]

    Journal of Business and Economic Statistics , volume=

    Forecasting the distribution of economic variables in a data-rich environment , author=. Journal of Business and Economic Statistics , volume=

  87. [95]

    NBB Working Paper , year=

    Quantile-based inflation risk models , author=. NBB Working Paper , year=

  88. [96]

    International Journal of Forecasting , volume=

    Quantile regression forecasts of inflation under model uncertainty , author=. International Journal of Forecasting , volume=

  89. [97]

    Song, Xiaojun and Taamouti, Abderrahim , journal=

  90. [98]

    Troster, Victor , journal=

  91. [99]

    Lahiri, S.N. , year=

  92. [100]

    Yamada, Hiroshi , journal=

  93. [101]

    White, Halbert and Kim, Tae-Hwan and Manganelli, Simone , journal=

  94. [102]

    Econometric Theory , volume=

    Conditional quantile estimation and inference for ARCH models , author=. Econometric Theory , volume=

  95. [103]

    1999 , publisher=

    Subsampling , author=. 1999 , publisher=

    1999

  96. [104]

    Journal of Money, Credit and Banking , volume=

    Unemployment risk , author=. Journal of Money, Credit and Banking , volume=

  97. [105]

    Bank of Canada Staff Working Paper , volume=

    Managing GDP tail risk , author=. Bank of Canada Staff Working Paper , volume=

  98. [106]

    FRB of Richmond Working Paper Series , volume=

    Assessing macroeconomic tail risk , author=. FRB of Richmond Working Paper Series , volume=

  99. [107]

    ECB Working Paper Series , volume=

    An area-wide model (AWM) for the euro area , author=. ECB Working Paper Series , volume=

  100. [108]

    Brookings Papers on Economic Activity , volume=

    When is growth at risk? , author=. Brookings Papers on Economic Activity , volume=

  101. [109]

    Brownlees, Christian and Souza, Andre BM , journal=

  102. [110]

    International Economic Review , volume=

    Multimodality in macrofinancial dynamics , author=. International Economic Review , volume=

  103. [111]

    IMF Global Financial Stability Report , volume=

    Is growth at risk? , author=. IMF Global Financial Stability Report , volume=

  104. [112]

    ECB Working Paper , volume=

    Forecasting and stress testing with quantile vector autoregression , author=. ECB Working Paper , volume=

  105. [113]

    and Smith, A.A

    MacKinnon, J.G. and Smith, A.A. , journal=

  106. [114]

    Psaradakis, Zacharias and Sola, Martin , journal=

  107. [115]

    Economics Letters , volume=

    Small sample properties of the conditional least squares estimator in SETAR models , author=. Economics Letters , volume=

  108. [116]

    2005 , publisher=

    Quantile regression , author=. 2005 , publisher=

    2005

  109. [117]

    Journal of the American Statistical Association , volume=

    Quantile autoregression , author=. Journal of the American Statistical Association , volume=

  110. [118]

    American Economic Review , volume=

    Vulnerable growth , author=. American Economic Review , volume=

  111. [119]

    International Journal of Forecasting , volume=

    Forecasting macroeconomic risks , author=. International Journal of Forecasting , volume=

  112. [120]

    Economics Letters , volume=

    Vulnerable growth in the euro area: Measuring the financial conditions , author=. Economics Letters , volume=

  113. [121]

    The Annals of Statistics , volume=

    A smooth block bootstrap for quantile regression with time series , author=. The Annals of Statistics , volume=

  114. [122]

    Computational issues for quantile regression , author=. Sankhy

  115. [123]

    Annals of the Institute of Statistical Mathematics , volume=

    Asymptotic normality of Powell's kernel estimator , author=. Annals of the Institute of Statistical Mathematics , volume=

  116. [124]

    International Journal of Forecasting , volume=

    High-frequency monitoring of growth at risk , author=. International Journal of Forecasting , volume=

  117. [125]

    Journal of the American Statistical Association , volume=

    A consistent nonparametric test of symmetry in linear regression models , author=. Journal of the American Statistical Association , volume=

  118. [126]

    Journal of Nonparametric Statistics , volume=

    On goodness-of-fit tests for weakly dependent processes using kernel method , author=. Journal of Nonparametric Statistics , volume=

  119. [127]

    Journal of Nonparametric Statistics , volume=

    Testing symmetry of an unknown density function by kernel method , author=. Journal of Nonparametric Statistics , volume=

  120. [128]

    Econometric Theory , volume=

    Block bootstrap consistency under weak assumptions , author=. Econometric Theory , volume=

  121. [129]

    Economics Letters , volume=

    A consistent bootstrap procedure for nonparametric symmetry tests , author=. Economics Letters , volume=

  122. [130]

    Computational Statistics and Data Analysis , volume=

    A bootstrap approach to test the conditional symmetry in time series models , author=. Computational Statistics and Data Analysis , volume=

  123. [131]

    Annals of Economics and Finance , volume=

    A bootstrap test for conditional symmetry , author=. Annals of Economics and Finance , volume=

  124. [132]

    Economics Letters , volume=

    A simple test for multivariate conditional symmetry , author=. Economics Letters , volume=

  125. [133]

    Bhattacharya, P. K. and Gastwirth, J. L. and Wright, A. L. , journal=

  126. [134]

    Canadian Journal of Statistics , volume=

    A simple test of symmetry about an unknown median , author=. Canadian Journal of Statistics , volume=

  127. [135]

    and Wang, X

    Lyubchich, V. and Wang, X. and Heyes, A. and Gel, Y. R. , journal=. A distribution-free

  128. [136]

    Journal of Econometrics , volume=

    Testing time reversibility without moment restrictions , author=. Journal of Econometrics , volume=

  129. [137]

    and Kuan, C.-M

    Chen, Y.-T. and Kuan, C.-M. , journal=

  130. [138]

    Davis, C. E. and Quade, D. , journal=. 1978 , volume=

    1978

  131. [139]

    and Burton, R

    Aaronson, J. and Burton, R. and Dehling, H. and Gilat, D. and Hill, T. and Weiss, B. , journal=. 1996 , volume=

    1996

  132. [140]

    Arcones, M. A. , journal=. 1995 , volume=

    1995

  133. [141]

    and Keller, G

    Denker, M. and Keller, G. , journal=. 1983 , volume=

    1983

  134. [142]

    and Wendler, M

    Dehling, H. and Wendler, M. , journal=. 2010 , volume=

    2010

  135. [143]

    Journal of Statistical Planning and Inference , volume=

    An asymptotically distribution-free test of symmetry , author=. Journal of Statistical Planning and Inference , volume=

  136. [144]

    Statistics and Probability Letters , volume=

    A general measure of skewness , author=. Statistics and Probability Letters , volume=

  137. [145]

    Journal of Econometrics , volume=

    A versatile and robust metric entropy test of time-reversibility, and other hypotheses , author=. Journal of Econometrics , volume=

  138. [146]

    Journal of Data Science , volume=

    On bootstrap tests of symmetry about an unknown median , author=. Journal of Data Science , volume=

  139. [147]

    Journal of Statistics , volume=

    A comparative study of ten asymmetry tests , author=. Journal of Statistics , volume=. 2011 , pages=

    2011

  140. [148]

    Communications in Statistics -- Theory and Methods , volume=

    Robust testing for skewness , author=. Communications in Statistics -- Theory and Methods , volume=. 2007 , pages=

    2007

  141. [149]

    Pourahmadi, M. , year=

  142. [150]

    Paparoditis and Streitberg, B

    E. Paparoditis and Streitberg, B. , year = 1992, title =. Journal of Time Series Analysis , volume =

    1992

  143. [151]

    D. J. Nordman , year = 2009, title =. Annals of Statistics , volume =

    2009

  144. [152]

    Carlstein , year = 1986, title =

    E. Carlstein , year = 1986, title =. Annals of Statistics , volume =

    1986

  145. [153]

    D. N. Politis and J. P. Romano , year = 1994, title =. Journal of the American Statistical Association , volume =

    1994

  146. [154]

    S. Gon. The Bootstrap of the mean for dependent heterogeneous arrays , journal =

  147. [155]

    S. Gon. Consistency of the stationary bootstrap under weak moment conditions , journal =

  148. [156]

    Hwang and D

    E. Hwang and D. W. Shin , year = 2012, title =. Statistics and Probability Letters , volume =

    2012

  149. [157]

    Journal of the Royal Statistical Society, Ser

    On the estimation of residual variance and order in autoregressive time series , author=. Journal of the Royal Statistical Society, Ser. B , volume=

  150. [158]

    Sankhy\=

    An asymptotically optimal selection of the order of a linear process , author=. Sankhy\=

  151. [159]

    Choi and P

    E. Choi and P. Hall , year = 2000, title =. Journal of the Royal Statstical Society, Ser. B , volume = 62, pages =

    2000

  152. [160]

    Paparoditis , year = 1996, title =

    E. Paparoditis , year = 1996, title =. Journal of Multivariate Analysis , volume =

    1996

  153. [161]

    Journal of Multivariate Analysis , volume=

    Invariant tests for symmetry about an unspecified point based on the empirical characteristic function , author=. Journal of Multivariate Analysis , volume=

  154. [162]

    Stability Selection , journal =

    Meinshausen, Nicolai and B. Stability Selection , journal =. 2010 , volume =

    2010

  155. [163]

    Econometrica , year =

    White, Halbert , title =. Econometrica , year =

  156. [164]

    , title =

    McCullagh, Peter and Nelder, John A. , title =. 1989 , edition =

    1989

  157. [165]

    Canadian Journal of Statistics , volume=

    Nonparametric weighted symmetry tests , author=. Canadian Journal of Statistics , volume=

  158. [166]

    Journal of the American Statistical Association , volume=

    Robust univariate test of symmetry , author=. Journal of the American Statistical Association , volume=

  159. [167]

    M. K. Gupta , year = 1967, title =. Annals of Mathematical Statistics , volume = 38, pages =

    1967

  160. [168]

    Zeitschrift f\"

    Tests for symmetry , author=. Zeitschrift f\"

  161. [169]

    Journal of the American Statistical Association , volume=

    Testing symmetry , author=. Journal of the American Statistical Association , volume=

  162. [170]

    Communications in Statistics -- Simulation and Computation , volume=

    Using the bootstrap in testing symmetry versus asymmetry , author=. Communications in Statistics -- Simulation and Computation , volume=

  163. [171]

    Journal of Macroeconomics , volume=

    Further evidence on the asymmetric behavior of unemployment rates over the business cycle , author=. Journal of Macroeconomics , volume=

  164. [172]

    Studies in Nonlinear Dynamics and Econometrics , volume=

    Reconsideration of the Markov chain evidence on unemployment rate asymmetry , author=. Studies in Nonlinear Dynamics and Econometrics , volume=

  165. [173]

    Econometrica , volume=

    Pseudo Maximum Likelihood Methods: Theory , author=. Econometrica , volume=

  166. [174]

    Maximum likelihood estimation of misspecified dynamic models , booktitle =

    White, Halbert , editor =. Maximum likelihood estimation of misspecified dynamic models , booktitle =

  167. [175]

    , journal=

    Aki, S. , journal=. Asymptotic distribution of a Cram

  168. [176]

    Rothman, E. D. and Woodroofe, M. , journal=. A Cram

  169. [177]

    Communications in Statistics -- Theory and Methods , volume=

    Tests for symmetry about an unknown value based on the empirical distribution function , author=. Communications in Statistics -- Theory and Methods , volume=

  170. [178]

    Koziol, J. A. , journal=. On a Cram

  171. [179]

    Hill, D. L. and Rao, P. V. , journal=. Tests of symmetry based on Cram

  172. [180]

    Econometric Reviews , volume=

    A robust entropy-based test of asymmetry for discrete and continuous processes , author=. Econometric Reviews , volume=

  173. [181]

    Review of Economic Studies , volume=

    Asymmetric cycles , author=. Review of Economic Studies , volume=

  174. [182]

    Studies in Nonlinear Dynamics and Econometrics , volume=

    Conditional and unconditional asymmetry in US macroeconomic time series , author=. Studies in Nonlinear Dynamics and Econometrics , volume=

  175. [183]

    Applied Economics , volume=

    Steepness and deepness in the Australian macroeconomy , author=. Applied Economics , volume=

  176. [184]

    Olekalns, Nilss , journal=

  177. [185]

    Applied Economics Letters , volume=

    Understanding asymmetries in macroeconomic aggregates: the case of Singapore , author=. Applied Economics Letters , volume=

  178. [186]

    Carnegie-Rochester conference series on public policy , volume=

    Discretion versus policy rules in practice , author=. Carnegie-Rochester conference series on public policy , volume=

  179. [187]

    Economic Modelling , volume=

    Forecast combination and the Bank of England's suite of statistical forecasting models , author=. Economic Modelling , volume=

  180. [188]

    Journal of Time Series Analysis , volume=

    A Quantile-based test for symmetry of weakly dependent processes , author=. Journal of Time Series Analysis , volume=

  181. [189]

    Boos, D. D. , journal=

  182. [190]

    Statistics and Probability Letters , volume=

    A modified runs test for symmetry , author=. Statistics and Probability Letters , volume=

  183. [191]

    UNSW Business School Research Paper , number=

    Testing for symmetry in weakly dependent time series , author=. UNSW Business School Research Paper , number=

  184. [192]

    FRB of New Zealand Discussion Paper , volume=

    Business cycle asymmetries and the nominal exchange rate regimes , author=. FRB of New Zealand Discussion Paper , volume=

  185. [193]

    Journal of the American Statistical Association , volume=

    On the sign test for symmetry , author=. Journal of the American Statistical Association , volume=

  186. [194]

    Applied Economics Letters , volume=

    Testing for business cycle asymmetry: some international evidence , author=. Applied Economics Letters , volume=

  187. [195]

    Journal of Monetary Economics , volume=

    Trends and random walks in macroeconmic time series: some evidence and implications , author=. Journal of Monetary Economics , volume=

  188. [196]

    DeLong, J. B. and L. H. Summers , editor =. Are Business Cycles Symmetrical? , booktitle =

  189. [197]

    International Review of Economics and Finance , volume=

    Investigating business cycle asymmetry for the G7 countries: Evidence from over a century of data , author=. International Review of Economics and Finance , volume=

  190. [198]

    Journal of Applied Sciences , volume=

    An International Perspective on Business Cycle Asymmetry: Evidence from Non-parametric Analysis , author=. Journal of Applied Sciences , volume=

  191. [199]

    Economic Record , volume=

    Testing for asymmetry in the measured and underground business cycles in New Zealand , author=. Economic Record , volume=

  192. [200]

    The Manchester School , volume=

    International evidence on the asymmetry of business cycle turning points , author=. The Manchester School , volume=

  193. [201]

    Econometric Theory , volume=

    A sieve bootstrap test for cointegration in a conditional error correction model , author=. Econometric Theory , volume=

  194. [202]

    and Gel, Y

    Miao, W. and Gel, Y. R. and Gastwirth, J. L. , editor =. A new test of symmetry about an unknown median , booktitle =

  195. [203]

    W. J. A Practitioner's Guide to Robust Covariance Matrix Estimation , booktitle =

  196. [204]

    Journal of Statistical Computation and Simulation , volume=

    An empirical power comparison of univariate goodness-of-fit tests for normality , author=. Journal of Statistical Computation and Simulation , volume=

  197. [205]

    Journal of Statistical Computation and Simulation , volume=

    Comparisons of various types of normality tests , author=. Journal of Statistical Computation and Simulation , volume=

  198. [206]

    Metrika , volume=

    A consistent test for multivariate normality based on the empirical characteristic function , author=. Metrika , volume=

  199. [207]

    Sankhy\=

    Goodness-of-fit tests when parameters are estimated , author=. Sankhy\=

  200. [208]

    and Choukri, K

    Moulines, E. and Choukri, K. , journal=

  201. [209]

    Communications in Statistics -- Simulation and Computation , volume=

    Recent and classical tests for normality - a comparative study , author=. Communications in Statistics -- Simulation and Computation , volume=

  202. [210]

    Communications in Statistics -- Theory and Methods , volume=

    A class of invariant consistent tests for multivariate normality , author=. Communications in Statistics -- Theory and Methods , volume=

  203. [211]

    Metrika , volume=

    Bootstrap based goodness-of-fit-tests , author=. Metrika , volume=

  204. [212]

    Statistics and Probability Letters , volume=

    Jim\'. Statistics and Probability Letters , volume=

  205. [213]

    Kojadinovic, I. and J. Yan , journal=

  206. [214]

    and Neumann, M

    Leucht, A. and Neumann, M. H. , journal=

  207. [215]

    Journal of Multivariate Analysis , volume=

    Characteristic function-based hypothesis tests under weak dependence , author=. Journal of Multivariate Analysis , volume=

  208. [216]

    , journal=

    Leucht, A. , journal=

  209. [217]

    Annals of Statistics , volume=

    J\". Annals of Statistics , volume=

  210. [218]

    Annals of Statistics , volume=

    On the number of bootstrap simulations required to construct a confidence interval , author=. Annals of Statistics , volume=

  211. [219]

    Biometrika , volume=

    The bootstrap applied to power spectral density function estimation , author=. Biometrika , volume=

  212. [220]

    Journal of Forecasting , volume=

    An evaluation of tests of distributional forecasts , author=. Journal of Forecasting , volume=

  213. [221]

    Journal of Business and Economic Statistics , volume=

    Tests for forecast encompassing , author=. Journal of Business and Economic Statistics , volume=

  214. [222]

    Journal of Time Series Analysis , volume=

    A diagnostic test for nonlinear serial dependence in time series fitting errors , author=. Journal of Time Series Analysis , volume=

  215. [223]

    Econometrics and Statistics , volume=

    A distance test of normality for a wide class of stationary processes , author=. Econometrics and Statistics , volume=

  216. [224]

    National Institute Economic Review , volume=

    Asymmetric density forecasts of inflation and the Bank of England's fan chart , author=. National Institute Economic Review , volume=

  217. [225]

    Biometrika , volume=

    Goodness-of-fit tests for correlated data , author=. Biometrika , volume=

  218. [226]

    Biometrika , volume=

    Monte Carlo approximation and the iterated bootstrap , author=. Biometrika , volume=

  219. [227]

    1994 , publisher=

    An Introduction to the Bootstrap , author=. 1994 , publisher=

    1994

  220. [228]

    Journal of Applied Econometrics , volume=

    Estimating time variation in measurement error from data revisions: an application to backcasting and forecasting in dynamic models , author=. Journal of Applied Econometrics , volume=

  221. [229]

    1997 , address=

    Bootstrap Methods and their Application , author=. 1997 , address=

    1997

  222. [230]

    Kybernetika , volume =

    And. Kybernetika , volume =. 2005 , title =

    2005

  223. [231]

    Kybernetika , volume =

    And. Kybernetika , volume =. 1984 , title =

    1984

  224. [232]

    K. N. Berk , year =. Consistent autoregressive spectral estimates , journal =

  225. [233]

    R. J. Bhansali , year =. Linear prediction by autoregressive model fitting in the time domain , journal =

  226. [234]

    and Kokoszka, P

    Hassler, U. and Kokoszka, P. , year =. Impulse responses of fractionally integrated processes with long memory , journal =

  227. [235]

    Chen and Y

    X. Chen and Y. Fan , year =. Estimation of copula-based semiparametric time series models , journal =

  228. [236]

    Cotter , year =

    J. Cotter , year =. Journal of International Money and Finance , volume =

  229. [237]

    2000 , publisher=

    Gaussian and Non-Gaussian Linear Time Series and Random Fields , author=. 2000 , publisher=

    2000

  230. [238]

    Cotter , year =

    J. Cotter , year =. Varying the VaR for unconditional and conditional environments , journal =

  231. [239]

    Stein , year = 1972, title =

    C. Stein , year = 1972, title =. Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, Volume 2: Probability Theory , pages =

    1972

  232. [240]

    Lewis and G

    R. Lewis and G. C. Reinsel , title =. Journal of the Multivariate Analysis , year =

  233. [241]

    Biometrika , volume=

    An improved Bonferroni procedure for multiple tests of significance , author=. Biometrika , volume=

  234. [242]

    Review of Economics and Statistics , year =

    Forni, Mario and Hallin, Marc and Lippi, Marco and Reichlin, Lucrezia , title =. Review of Economics and Statistics , year =

  235. [243]

    Journal of the Royal Statistical Society, Ser

    Controlling the false discovery rate: a practical and powerful approach to multiple testing , author=. Journal of the Royal Statistical Society, Ser. B , volume=

  236. [244]

    Annals of Statistics , volume=

    The control of the false discovery rate in multiple testing under dependency , author=. Annals of Statistics , volume=

  237. [245]

    Journal of Econometrics , volume=

    The generalised autocovariance function , author=. Journal of Econometrics , volume=

  238. [246]

    Journal of Forecasting , volume=

    The impact of measurement errors on ARMA prediction , author=. Journal of Forecasting , volume=

  239. [247]

    Econometric Theory , volume=

    Diagnostic checking for the adequacy of nonlinear time series models , author=. Econometric Theory , volume=

  240. [248]

    Journal of the Royal Statistical Society B , year =

    Fan, Jianqing and Lv, Jinchi , title =. Journal of the Royal Statistical Society B , year =

  241. [249]

    Annals of Statistics , year =

    Fan, Jianqing and Song, Rui , title =. Annals of Statistics , year =

  242. [250]

    1993 , publisher=

    Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment , author=. 1993 , publisher=

    1993

  243. [251]

    1996 , publisher=

    Multiple Comparisons: Theory and Methods , author=. 1996 , publisher=

    1996

  244. [252]

    2010 , publisher=

    Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction , author=. 2010 , publisher=

    2010

  245. [253]

    , journal=

    Psaradakis, Z. , journal=

  246. [254]

    Biometrika , volume=

    A sharper Bonferroni procedure for multiple tests of significance , author=. Biometrika , volume=

  247. [255]

    Econometrica , volume=

    Consistent testing for serial correlation of unknown form , author=. Econometrica , volume=

  248. [256]

    Statistica Sinica , volume=

    Detecting and modeling nonlinearity in univariate time series analysis , author=. Statistica Sinica , volume=

  249. [257]

    Biometrika , volume=

    Entropy testing for nonlinear serial dependence in time series , author=. Biometrika , volume=

  250. [258]

    and Wouters, R

    Smets, F. and Wouters, R. , journal=

  251. [259]

    Annals of Statistics , volume=

    Asymptotically optimal estimation in misspecified time series models , author=. Annals of Statistics , volume=

  252. [260]

    Journal of the Royal Statistical Society, Ser

    Minimum contrast estimation for spectral densities of stationary processes , author=. Journal of the Royal Statistical Society, Ser. B , volume=

  253. [261]

    Journal of Business and Economic Statistics , volume=

    On the fit of new Keynesian models , author=. Journal of Business and Economic Statistics , volume=

  254. [262]

    Theory and Applications of Long-Range Dependence , publisher =

  255. [263]

    Epps, T. W. , journal=

  256. [264]

    Biometrika , volume=

    A test for normality based on the empirical characteristic function , author=. Biometrika , volume=

  257. [265]

    Nusrat, J. and J. L. Harvill , journal=

  258. [266]

    Henry and P

    M. Henry and P. Zaffaroni , editor =. The long-range dependence paradigm for macroeconomics and finance , booktitle =

  259. [267]

    Hinich, M. J. , journal=

  260. [268]

    , journal=

    Boldin, M.D. , journal=

  261. [269]

    H. R. K. Statistical aspects of self-similar processes , booktitle =

  262. [270]

    National Bureau of Economic Research Working Paper , volume=

    Sovereign debt risk premia and fiscal policy in Sweden , author=. National Bureau of Economic Research Working Paper , volume=

  263. [271]

    Journal of Monetary Economics , volume=

    Asymmetric business cycles: Theory and time-series evidence , author=. Journal of Monetary Economics , volume=

  264. [272]

    Psaradakis, Z. and M. Sola , journal=

  265. [273]

    Burns, A.F. and W.C. Mitchell , year=

  266. [274]

    Henry and P

    M. Henry and P. M. Robinson , editor =. Bandwidth choice in Gaussian semiparametric estimation of long range dependence , booktitle =

  267. [275]

    Annals of statistics , volume=

    Gaussian semiparametric estimation of long range dependence , author=. Annals of statistics , volume=

  268. [276]

    and Hinkley, D.V

    Davison, A.C. and Hinkley, D.V. , year=

  269. [277]

    Econometric Reviews , volume=

    Confidence intervals for impulse responses under departures from normality , author=. Econometric Reviews , volume=

  270. [278]

    Journal of Banking and Finance , volume=

    Portfolio selection and skewness: Evidence from international stock markets , author=. Journal of Banking and Finance , volume=

  271. [279]

    Journal of Finance , pages=

    On diversification given asymmetry in returns , author=. Journal of Finance , pages=

  272. [280]

    International Journal of forecasting , volume=

    Testing the equality of prediction mean squared errors , author=. International Journal of forecasting , volume=

  273. [281]

    Journal of Econometrics , volume=

    Long memory relationships and the aggregation of dynamic models , author=. Journal of Econometrics , volume=

  274. [282]

    D. W. K. Andrews and Y. Sun , year =. Adaptive local polynomial Whittle estimation of long-range dependence , journal =

  275. [283]

    Journal of International Money and Finance , volume=

    The long memory of the forward premium , author=. Journal of International Money and Finance , volume=

  276. [284]

    Communications of the ACM , volume=

    An approximate method for generating asymmetric random variables , author=. Communications of the ACM , volume=

  277. [285]

    Journal of Money, Credit, and Banking , volume=

    Long-Memory Inflation Uncertainty: Evidence from the Term Structure of Interest Rates , author=. Journal of Money, Credit, and Banking , volume=

  278. [286]

    Journal of econometrics , volume=

    Modeling and pricing long memory in stock market volatility , author=. Journal of econometrics , volume=

  279. [287]

    Journal of Econometrics , volume=

    Testing of unit root and other nonstationary hypotheses in macroeconomic time series , author=. Journal of Econometrics , volume=

  280. [288]

    International Economic Review , volume=

    Long memory and aggregation in macroeconomic time series , author=. International Economic Review , volume=

  281. [289]

    Journal of Econometrics , volume=

    Testing for neglected nonlinearity in regression models based on the theory of random fields , author=. Journal of Econometrics , volume=

  282. [290]

    Studies in Nonlinear Dynamics and Econometrics , volume=

    p-Value adjustments for multiple tests for nonlinearity , author=. Studies in Nonlinear Dynamics and Econometrics , volume=

  283. [291]

    Journal of Business and Economic Statistics , volume=

    Using the bootstrap to test for symmetry under unknown dependence , author=. Journal of Business and Economic Statistics , volume=

  284. [292]

    D. S. Poskitt , year =. Properties of the sieve bootstrap for fractionally integrated and non-invertible processes , journal =

  285. [293]

    D. S. Poskitt and S. D. Grose and G. M. Martin , year =. Higher-order improvements of the sieve bootstrap for fractionally integrated processes , journal =

  286. [294]

    D. S. Poskitt , year =. Autoregressive approximation in nonstandard situations: the fractionally integrated and non-invertible cases , journal =

  287. [295]

    Kapetanios and Z

    G. Kapetanios and Z. Psaradakis , year =. Sieve bootstrap for strongly dependent stationary processes , note =

  288. [296]

    Kreiss , editor =

    J.-P. Kreiss , editor =. Bootstrapping and Related Techniques , pages =. 1992 , title =

    1992

  289. [297]

    Annals of Statistics , volume=

    On the range of validity of the autoregressive sieve bootstrap , author=. Annals of Statistics , volume=

  290. [298]

    Journal of Statistical Planning and Inference , volume=

    A bootstrap test for time series linearity , author=. Journal of Statistical Planning and Inference , volume=

  291. [299]

    Econometrica , volume=

    Asymptotic distribution theory for nonparametric entropy measures of serial dependence , author=. Econometrica , volume=

  292. [300]

    Journal of Econometrics , volume=

    A single-blind controlled competition among tests for nonlinearity and chaos , author=. Journal of Econometrics , volume=

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