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Last month |
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12 months |
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A Coupled Component GARCH Model for Intraday and Overnight Volatility |
1 |
1 |
1 |
24 |
1 |
2 |
2 |
91 |

A Discrete Choice Model For Large Heterogeneous Panels with Interactive Fixed Effects with an Application to the Determinants of Corporate Bond Issuance |
0 |
0 |
1 |
11 |
0 |
0 |
6 |
59 |

A Dynamic Network of Arbitrage Characteristics |
0 |
0 |
0 |
18 |
0 |
0 |
2 |
59 |

A Dynamic Semiparametric Characteristics-based Model for Optimal Portfolio Selection |
0 |
0 |
2 |
12 |
1 |
2 |
10 |
37 |

A Flexible Semiparametric Model for Time Series |
0 |
0 |
0 |
53 |
0 |
0 |
0 |
77 |

A GARCH Model of the Implied Volatility of the Swiss Market Index From Option Pricesdffrom Options Prices |
0 |
0 |
0 |
310 |
0 |
0 |
0 |
887 |

A GARCH model of the implied volatility of the Swiss Market Index from options prices |
0 |
0 |
0 |
8 |
0 |
0 |
1 |
40 |

A Local Instrumental Variable Estimation Method For Generalized Additive Volatility Models |
0 |
0 |
0 |
50 |
0 |
0 |
1 |
225 |

A Local Instrumental Variable Estimation Method for Generalized Additive Volatility Models |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
23 |

A New Semiparametric Estimation Approach for Large Dynamic Covariance Matrices with Multiple Conditioning Variables |
0 |
0 |
0 |
12 |
0 |
0 |
1 |
54 |

A New Semiparametric Estimation Approach for Large Dynamic Covariance Matrices with Multiple Conditioning Variables |
0 |
0 |
0 |
40 |
1 |
2 |
2 |
71 |

A Nonparametric Regression Estimator that Adapts to Error Distribution of Unknown Form |
0 |
0 |
0 |
4 |
0 |
0 |
3 |
23 |

A Quantilogram Approach to Evaluating Directional Predictability |
0 |
0 |
0 |
117 |
0 |
0 |
0 |
470 |

A Quantilogram Approach to Evaluating Directional Predictability |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
31 |

A ReMeDI for Microstructure Noise |
0 |
2 |
2 |
79 |
1 |
5 |
10 |
171 |

A Semiparametric Panel Model for Unbalanced Data with Application to Climate Change in the United Kingdom |
0 |
0 |
1 |
96 |
0 |
0 |
2 |
239 |

A Semiparametric Panel Model for unbalanced data with Application to Climate Change in the United Kingdom |
0 |
0 |
0 |
60 |
0 |
0 |
2 |
166 |

A Simulation Comparison between Integration and Backfitting Methods of Estimating Separable Nonparametric Regression Models |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
101 |

A Unified Framework for Efficient Estimation of General Treatment Models |
0 |
0 |
1 |
30 |
0 |
1 |
3 |
30 |

A Unified Framework for Efficient Estimation of General Treatment Models |
0 |
0 |
0 |
8 |
0 |
0 |
1 |
22 |

A Unified Framework for Efficient Estimation of General Treatment Models |
0 |
0 |
0 |
6 |
0 |
0 |
0 |
32 |

A Unified Framework for Specification Tests of Continuous Treatment Effect Models |
0 |
0 |
0 |
4 |
0 |
0 |
1 |
27 |

A Unified Framework for Specification Tests of Continuous Treatment Effect Models |
0 |
0 |
0 |
9 |
0 |
0 |
0 |
11 |

A coupled component GARCH model for intraday and overnight volatility |
0 |
0 |
0 |
25 |
0 |
0 |
1 |
24 |

A coupled component GARCH model for intraday and overnight volatility |
0 |
0 |
1 |
67 |
0 |
0 |
1 |
56 |

A discrete choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance |
0 |
1 |
1 |
51 |
0 |
2 |
4 |
86 |

A discrete choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance |
0 |
0 |
1 |
31 |
0 |
0 |
2 |
178 |

A flexible semiparametric model for time series |
0 |
0 |
0 |
50 |
0 |
0 |
0 |
91 |

A local instrumental estimation method for generalized additive volatility models |
0 |
0 |
0 |
10 |
0 |
0 |
0 |
188 |

A local instrumental variable estimation method for generalized additive volatility models |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
19 |

A local instrumental variable estimation method for generalized additive volatility models |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
32 |

A nonparametric regression estimator that adapts to error distribution of unknown form |
0 |
0 |
0 |
32 |
0 |
0 |
0 |
214 |

A nonparametric regression estimator that adapts to error distribution of unknown form |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
39 |

A nonparametric test of a strong leverage hypothesis |
0 |
0 |
0 |
26 |
0 |
0 |
0 |
82 |

A nonparametric test of the leverage hypothesis |
0 |
1 |
1 |
20 |
0 |
2 |
2 |
64 |

A quantilogram approach to evaluating directional predictability |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
25 |

A semiparametric model for heterogeneous panel data with fixed effects |
0 |
0 |
0 |
102 |
0 |
0 |
0 |
264 |

A semiparametric panel model for unbalanced data with application to climate change in the United Kingdom |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
73 |

A simple and efficient estimation method for models with nonignorable missing data |
0 |
0 |
0 |
131 |
0 |
0 |
0 |
266 |

A smoothed least squares estimator for threshold regression models |
0 |
0 |
4 |
23 |
0 |
0 |
4 |
77 |

Adaptive Estimation in ARCH Models |
0 |
0 |
0 |
234 |
0 |
0 |
2 |
611 |

Adaptive Testing in ARCH Models |
0 |
1 |
1 |
173 |
0 |
1 |
1 |
884 |

Additive nonparametric models with time variable and both stationary and nonstationary regressions |
0 |
0 |
0 |
18 |
0 |
0 |
0 |
32 |

An Almost Closed Form Estimator For The EGARCH Model |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
5 |

An Almost Closed Form Estimator for the EGARCH |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
9 |

An Almost Closed Form Estimator for the EGARCH model |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
7 |

An Alternative Way of ComputingEfficient Instrumental VariableEstimators |
0 |
0 |
1 |
2 |
0 |
0 |
4 |
39 |

An Analysis of Transformations for Additive Nonparanetric Regression |
0 |
0 |
0 |
27 |
0 |
0 |
0 |
221 |

An Asymptotic Expansion in the Garch(1,1) Model |
0 |
0 |
0 |
203 |
0 |
0 |
0 |
583 |

An Improved Bootstrap Test of Stochastic Dominance |
0 |
0 |
0 |
74 |
0 |
0 |
3 |
228 |

An Optimization Interpretation of Integration and Backfitting Estimators for Separable Nonparametric Models |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
81 |

An almost closed form estimator for the EGARCH model |
0 |
0 |
0 |
74 |
0 |
0 |
0 |
108 |

An almost closed form estimator for the EGARCH model |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
21 |

An alternative way of computing efficient instrumental variable estimators |
0 |
0 |
0 |
3 |
0 |
0 |
2 |
54 |

An improved bootstrap test of stochastic dominance |
0 |
0 |
0 |
20 |
0 |
0 |
1 |
106 |

An investigation into Multivariate Variance Ratio Statistics and their application to Stock Market Predictability |
0 |
0 |
0 |
23 |
0 |
0 |
1 |
63 |

An investigation into multivariate variance ratio statistics and their application to stock market predictability |
0 |
0 |
0 |
16 |
0 |
1 |
1 |
53 |

Applied Nonparametric Methods |
0 |
0 |
20 |
1,178 |
2 |
7 |
48 |
2,422 |

Applied nonparametric methods |
0 |
0 |
2 |
372 |
0 |
0 |
4 |
945 |

Are there Monday effects in Stock Returns: A Stochastic Dominance Approach |
0 |
0 |
0 |
322 |
1 |
1 |
1 |
976 |

Are there Monday effects in stock returns: a stochastic dominance approach |
0 |
0 |
0 |
12 |
0 |
0 |
2 |
70 |

Asymptotic Expansions for Some Semiparametric Program Evaluation Estimators |
0 |
0 |
0 |
17 |
0 |
0 |
2 |
43 |

Asymptotic expansions for some semiparametric program evaluation estimators |
0 |
0 |
0 |
177 |
0 |
1 |
3 |
414 |

Asymptotic expansions for some semiparametric program evaluation estimators |
0 |
0 |
0 |
7 |
0 |
0 |
1 |
49 |

Asymptotic properties of a Nadaraya-Watson type estimator for regression functions of in?finite order |
0 |
0 |
1 |
13 |
0 |
0 |
3 |
36 |

Averaging of moment condition estimators |
0 |
0 |
0 |
48 |
0 |
0 |
2 |
98 |

Bootstrap Tests of Stochastic Dominance with Asymptotic Similarity on the Boundary |
0 |
0 |
0 |
64 |
0 |
0 |
1 |
201 |

Bootstrap Tests of Stochastic Dominance with AsymptoticSimilarity on the Boundary |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
25 |

Bootstrap tests of stochastic dominance with asymptotic similarity on the boundary |
0 |
0 |
0 |
23 |
0 |
0 |
0 |
106 |

Bootstrap tests of stochastic dominance with asymptotic similarity on the boundary |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
36 |

Classification of nonparametric regression functions in heterogeneous panels |
0 |
0 |
0 |
28 |
0 |
1 |
2 |
52 |

Conditional Independence Restrictions: Testing and Estimation |
0 |
1 |
5 |
613 |
0 |
1 |
8 |
2,148 |

Consistent Estimation of the Risk-Return Tradeoff in the Presence of Measurement Error |
0 |
0 |
0 |
27 |
0 |
0 |
1 |
152 |

Consistent Testing for Stochastic Dominance under General Sampling Schemes |
0 |
0 |
0 |
21 |
0 |
0 |
0 |
88 |

Consistent Testing for Stochastic Dominance: A Subsampling Approach |
0 |
0 |
0 |
189 |
0 |
0 |
3 |
844 |

Consistent Testing for Stochastic Dominance: A Subsampling Approach |
0 |
0 |
0 |
79 |
0 |
0 |
12 |
526 |

Consistent Testing for Stochastic Dominance: A Subsampling Approach |
0 |
0 |
0 |
2 |
0 |
0 |
11 |
82 |

Consistent Testing for Stochastic Dominance: A Subsampling Approach |
1 |
1 |
2 |
44 |
1 |
1 |
6 |
226 |

Consistent Testing for an Implication of Supermodular Dominance |
1 |
2 |
8 |
40 |
1 |
5 |
14 |
81 |

Consistent estimation of the risk-return tradeoff in the presence of measurement error |
0 |
0 |
0 |
1 |
0 |
0 |
3 |
39 |

Consistent estimation of the risk-return tradeoff in the presence of measurement error |
0 |
0 |
0 |
39 |
0 |
2 |
5 |
201 |

Consistent testing for stochastic dominance under general sampling schemes |
0 |
0 |
1 |
14 |
0 |
0 |
2 |
68 |

Consistent testing for stochastic dominance: a subsampling approach |
0 |
0 |
0 |
1 |
0 |
0 |
7 |
85 |

Consistent testing for stochastic dominance: a subsampling approach |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
68 |

Consistent testing for stochastic dominance: a subsampling approach |
1 |
1 |
1 |
4 |
1 |
1 |
9 |
83 |

Consistent testing for stochastic dominance: a subsampling approach |
0 |
0 |
0 |
144 |
0 |
0 |
1 |
634 |

Dynamic Autoregressive Liquidity (DArLiQ) |
0 |
0 |
1 |
11 |
0 |
3 |
6 |
20 |

Dynamic Autoregressive Liquidity (DArLiQ) |
0 |
0 |
0 |
32 |
0 |
0 |
2 |
14 |

ESTIMATION OF A SEMIPARAMETRICIGARCH(1,1) MODEL |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
18 |

Edgeworth Approximation for MINPIN Estimators in Semiparametric Regression Models |
0 |
0 |
0 |
54 |
0 |
1 |
1 |
493 |

Edgeworth Approximations for Semiparametric Instrumental Variable Estimators and Test Statistics |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
14 |

Edgeworth approximations for semiparametric instrumental variable estimators and test statistics |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
27 |

Efficient Estimation of Nonparametric Regression in The Presence of Dynamic Heteroskedasticity |
0 |
0 |
0 |
50 |
0 |
0 |
1 |
96 |

Efficient Estimation of a Multivariate Multiplicative Volatility Model |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
38 |

Efficient Estimation of a Semiparametric Characteristic- Based Factor Model of Security Returns |
0 |
0 |
0 |
92 |
0 |
0 |
1 |
289 |

Efficient Estimation of a Semiparametric Characteristic-Based Factor Model of Security Returns |
0 |
0 |
0 |
60 |
0 |
0 |
0 |
229 |

Efficient Estimation of a SemiparametricCharacteristic-Based Factor Model of Security Returns |
1 |
1 |
2 |
4 |
1 |
1 |
2 |
32 |

Efficient estimation of a multivariate multiplicative volatility model |
0 |
0 |
0 |
5 |
0 |
2 |
2 |
43 |

Efficient estimation of a semiparametric characteristic-based factor model of security returns |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
40 |

Efficient estimation of a semiparametric characteristic-based factor model of security returns |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
42 |

Efficient estimation of conditional risk measures in a semiparametric GARCH model |
0 |
0 |
0 |
58 |
0 |
0 |
0 |
97 |

Efficient estimation of generalized additive nonparametric regression models |
0 |
0 |
0 |
5 |
0 |
0 |
1 |
57 |

Estimating Features of a Distribution from Binomial Data |
0 |
0 |
0 |
211 |
0 |
1 |
2 |
1,293 |

Estimating Multiplicative and Additive Hazard Functions by Kernel Methods |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
36 |

Estimating Multiplicative and Additive Hazard Functions by Kernel Methods |
0 |
0 |
0 |
111 |
0 |
0 |
1 |
503 |

Estimating Quadratic VariationConsistently in thePresence of Correlated MeasurementError |
1 |
1 |
1 |
1 |
1 |
1 |
3 |
48 |

Estimating Semiparametric ARCH (8) Models by Kernel Smoothing Methods |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
22 |

Estimating Semiparametric ARCH Models by Kernel Smoothing Methods |
0 |
0 |
0 |
111 |
0 |
0 |
0 |
344 |

Estimating Yield Curves by Kernel Smoothing Methods |
0 |
0 |
0 |
691 |
0 |
0 |
2 |
1,889 |

Estimating additive nonparametric models by partial Lq norm: the curse of fractionality |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
23 |

Estimating features of a distribution from binomial data |
0 |
0 |
0 |
105 |
0 |
0 |
0 |
712 |

Estimating features of a distribution from binomial data |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
44 |

Estimating multiplicative and additive hazard functions by kernel methods |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
27 |

Estimating quadratic variation consistently in the presence of correlated measurement error |
0 |
0 |
0 |
1 |
0 |
0 |
2 |
26 |

Estimating semiparametric ARCH (8) models by kernel smoothing methods |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
63 |

Estimating semiparametric ARCH (∞) models by kernel smoothing methods |
0 |
0 |
0 |
3 |
0 |
0 |
2 |
33 |

Estimating the Quadratic Covariation Matrix for an Asynchronously Observed Continuous Time Signal Masked by Additive Noise |
0 |
1 |
1 |
50 |
0 |
1 |
3 |
124 |

Estimating yield curves by Kernel smoothing methods |
0 |
0 |
0 |
212 |
0 |
0 |
0 |
751 |

Estimation and Inference in Semiparametric Quantile Factor Models |
0 |
0 |
0 |
10 |
0 |
0 |
3 |
46 |

Estimation and inference in semiparametric quantile factor models |
0 |
0 |
0 |
76 |
0 |
0 |
1 |
137 |

Estimation in semiparametric quantile factor models |
0 |
0 |
0 |
26 |
0 |
0 |
0 |
50 |

Estimation of Additive Regression Models with Links |
0 |
0 |
0 |
3 |
0 |
1 |
3 |
95 |

Estimation of Linear Regression Models by a Spread-Tolerant Estimator |
0 |
0 |
0 |
17 |
0 |
0 |
1 |
128 |

Estimation of Semiparametric Models when the Criterion Function is not Smooth |
0 |
1 |
2 |
8 |
0 |
1 |
5 |
64 |

Estimation of a Multiplicative Correlation Structure in the Large Dimensional Case |
0 |
0 |
0 |
25 |
0 |
0 |
2 |
64 |

Estimation of a Multiplicative Covariance Structure |
0 |
0 |
0 |
26 |
0 |
0 |
3 |
32 |

Estimation of a Multiplicative Covariance Structure in the Large Dimensional Case |
0 |
0 |
0 |
11 |
0 |
0 |
3 |
86 |

Estimation of a Multiplicative Covariance Structure in the Large Dimensional Case |
0 |
0 |
0 |
22 |
0 |
0 |
0 |
35 |

Estimation of a Nonparametric Model for Bond Prices from Cross-Section and Time Series Information |
0 |
1 |
1 |
51 |
1 |
2 |
2 |
62 |

Estimation of a multiplicative correlation structure in the large dimensional case |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
3 |

Estimation of a multiplicative covariance structure in the large dimensional case |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
19 |

Estimation of linear regression models by a spread-tolerant estimator |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
24 |

Estimation of semiparametric models when the criterion function is not smooth |
0 |
0 |
0 |
3 |
0 |
1 |
2 |
69 |

Estimation of semiparametric models when the criterion function is not smooth |
0 |
1 |
1 |
153 |
0 |
1 |
6 |
623 |

Estimation of tail thickness parameters from GJR-GARCH models |
0 |
0 |
0 |
258 |
0 |
0 |
6 |
846 |

Estimation of the Kronecker Covariance Model by Quadratic Form |
0 |
0 |
1 |
7 |
0 |
0 |
5 |
42 |

Estimation with Mixed Data Frequencies: A Bias-Correction Approach |
0 |
0 |
0 |
32 |
0 |
0 |
0 |
17 |

Evaluating Value-at-Risk Models via Quantile Regression |
1 |
1 |
1 |
146 |
1 |
1 |
3 |
377 |

Evaluating Value-at-Risk Models via Quantile Regressions |
2 |
3 |
5 |
224 |
4 |
7 |
19 |
567 |

Evaluating Value-at-Risk models via Quantile Regression |
0 |
0 |
1 |
200 |
0 |
0 |
4 |
542 |

Evaluating Value-at-Risk models via Quantile regressions |
0 |
0 |
2 |
187 |
0 |
1 |
4 |
417 |

Evaluating hedge fund performance: a stochastic dominance approach |
0 |
0 |
0 |
158 |
0 |
1 |
2 |
369 |

Evaluating hedge fund performance: a stochastic dominance approach |
0 |
0 |
1 |
8 |
0 |
0 |
1 |
56 |

Flexible Term Structure Estimation: Which Method Is Preferred? |
1 |
1 |
2 |
4 |
1 |
2 |
6 |
12 |

Flexible Term Structure Estimation: Which Method Is Preferred? |
0 |
0 |
1 |
187 |
0 |
0 |
2 |
491 |

Flexible Term Structure Estimation: Which Method is Preferable? |
0 |
0 |
0 |
42 |
0 |
0 |
0 |
164 |

Flexible Term Structure Estimation: Which Method is Preferred? |
0 |
0 |
0 |
216 |
0 |
1 |
1 |
485 |

Flexible term structure estimation: which method is preferable? |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
30 |

Global Bahadur representation for nonparametric censored regression quantiles and its applications |
0 |
0 |
0 |
49 |
0 |
0 |
0 |
109 |

High Dimensional Semiparametric Moment Restriction Models |
0 |
0 |
1 |
16 |
0 |
0 |
7 |
64 |

High dimensional semiparametric moment restriction models |
0 |
0 |
1 |
54 |
0 |
1 |
2 |
119 |

High dimensional semiparametric moment restriction models |
0 |
0 |
0 |
27 |
0 |
0 |
1 |
58 |

High dimensional semiparametric moment restriction models |
0 |
0 |
0 |
2 |
0 |
0 |
3 |
34 |

High dimensional semiparametric moment restriction models |
0 |
0 |
0 |
4 |
0 |
0 |
1 |
38 |

Identification and Nonparametric Estimation of a Transformed Additively Separable Model |
0 |
0 |
0 |
58 |
0 |
0 |
1 |
271 |

Identification and nonparametric estimation of a transformed additively separable model |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
56 |

Implications of High-Frequency Trading for Security Markets |
0 |
0 |
0 |
68 |
0 |
0 |
4 |
112 |

Implications of high-frequency trading for security markets |
0 |
0 |
0 |
17 |
0 |
1 |
1 |
47 |

Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance |
0 |
0 |
2 |
16 |
1 |
2 |
7 |
15 |

Inference about Realized Volatility using Infill Subsampling |
0 |
0 |
0 |
1 |
0 |
1 |
3 |
20 |

Inference about realized volatility using infill subsampling |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
22 |

Inference on a Semiparametric Model with Global Power Law and Local Nonparametric Trends |
0 |
0 |
0 |
52 |
0 |
0 |
0 |
71 |

Inference on a semiparametric model with global power law and local nonparametric trends |
0 |
0 |
0 |
4 |
0 |
0 |
1 |
32 |

Integration and Backfitting methods in additive models: finite sample properties and comparison |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
20 |

Is There Chaos in the World Economy? A Nonparametric Test Using Consistent Standard Errors |
0 |
0 |
0 |
208 |
0 |
1 |
2 |
608 |

Is There Chaos in the World Economy? A Nonparametric Test Using Consistent Standard Errors |
0 |
0 |
0 |
278 |
1 |
1 |
2 |
878 |

Kernel estimation in a nonparametric marker dependent Hazard Model |
0 |
0 |
0 |
134 |
0 |
0 |
0 |
403 |

Let's get LADE: robust estimation of semiparametric multiplicative volatility models |
0 |
0 |
0 |
34 |
0 |
0 |
0 |
99 |

Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems |
0 |
0 |
0 |
104 |
0 |
0 |
2 |
420 |

Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
39 |

Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems |
0 |
0 |
0 |
206 |
1 |
1 |
1 |
913 |

Limit theorems for estimating the parameters of differentiated product demand systems |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
49 |

Local Linear Fitting Under Near Epoch Dependence: Uniform consistency with Convergence Rates |
0 |
0 |
0 |
39 |
0 |
0 |
0 |
90 |

Local Nonlinear Least Squares Estimation: Using Parametric Information Nonparametrically |
0 |
0 |
1 |
290 |
0 |
1 |
2 |
1,151 |

Loch Linear Fitting under Near Epoch Dependence: Uniform Consistency with Convergence Rate |
0 |
0 |
2 |
3 |
0 |
0 |
2 |
25 |

Loch linear fitting under near epoch dependence: uniform consistency with convergence rate |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
28 |

Making Inferences About Rich Country - Poor Country Convergence: The Polarization Trapezoid and Overlap measures |
0 |
0 |
0 |
72 |
0 |
1 |
4 |
395 |

Mean Ratio Statistic for measuring predictability |
0 |
0 |
0 |
19 |
0 |
0 |
0 |
38 |

More Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
43 |

More Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors |
0 |
0 |
0 |
241 |
0 |
0 |
1 |
806 |

More efficient kernel estimation in nonparametric regression with autocorrelated errors |
0 |
0 |
0 |
5 |
0 |
0 |
1 |
39 |

Multi-step non- and semi-parametric predictive regressions for short and long horizon stock return prediction |
0 |
0 |
1 |
31 |
0 |
0 |
1 |
64 |

Multi-step non- and semi-parametric predictive regressions for short and long horizon stock return prediction |
0 |
0 |
2 |
74 |
0 |
0 |
4 |
102 |

Multiscale clustering of nonparametric regression curves |
0 |
0 |
0 |
17 |
0 |
0 |
2 |
29 |

Multivariate Variance Ratio Statistics |
0 |
1 |
1 |
8 |
0 |
1 |
3 |
33 |

Multivariate variance ratio statistics |
0 |
0 |
0 |
32 |
0 |
1 |
2 |
81 |

Non Parametric Estimation of a Polarization Measure |
0 |
0 |
1 |
34 |
0 |
0 |
1 |
100 |

Non-Standard Errors |
0 |
1 |
8 |
40 |
3 |
12 |
71 |
317 |

Non-Standard Errors |
0 |
0 |
1 |
41 |
5 |
14 |
79 |
386 |

Non-parametric transformation regression with non-stationary data |
0 |
0 |
0 |
46 |
0 |
0 |
2 |
72 |

Nonparametric Censored Regression |
0 |
0 |
0 |
410 |
0 |
0 |
0 |
1,372 |

Nonparametric Censored and Truncated Regression |
0 |
0 |
1 |
5 |
0 |
1 |
4 |
71 |

Nonparametric Censored and Truncated Regression |
0 |
0 |
0 |
479 |
0 |
0 |
2 |
1,885 |

Nonparametric Censored and Truncated Regression |
0 |
0 |
0 |
197 |
0 |
0 |
1 |
528 |

Nonparametric Estimation of Additive Seperable Regression Models |
0 |
0 |
0 |
18 |
0 |
0 |
0 |
155 |

Nonparametric Estimation of Homothetic and Homothetically Separable Functions |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
22 |

Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data |
0 |
0 |
2 |
55 |
1 |
2 |
7 |
22 |

Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data |
0 |
0 |
2 |
20 |
1 |
1 |
8 |
28 |

Nonparametric Estimation of a Multifactor Heath-Jarrow-Morton Model: An Integrated Approach |
0 |
0 |
1 |
210 |
0 |
0 |
3 |
686 |

Nonparametric Estimation of a Polarization Measure |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
38 |

Nonparametric Estimation of a Polarization Measure |
0 |
0 |
0 |
59 |
0 |
0 |
0 |
186 |

Nonparametric Estimation with Aggregated Data |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
21 |

Nonparametric Euler Equation Identi?cation and Estimation |
0 |
0 |
0 |
27 |
0 |
0 |
0 |
28 |

Nonparametric Euler Equation Identification and Estimation |
0 |
0 |
0 |
51 |
0 |
0 |
1 |
175 |

Nonparametric Euler Equation Identification andEstimation |
0 |
0 |
0 |
46 |
0 |
1 |
4 |
101 |

Nonparametric Euler equation identification and estimation |
0 |
0 |
0 |
41 |
0 |
0 |
5 |
115 |

Nonparametric Inference for Unbalanced Time Series Data |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
21 |

Nonparametric Matching and Efficient Estimators of Homothetically Separable Functions |
0 |
0 |
0 |
135 |
0 |
0 |
1 |
726 |

Nonparametric Neural Network Estimation of Lyapunov Exponents and a Direct Test for Chaos |
0 |
0 |
0 |
1 |
0 |
0 |
2 |
27 |

Nonparametric Neural Network Estimation of Lyapunov Exponents and a Direct Test for Chaos |
0 |
0 |
0 |
368 |
0 |
0 |
1 |
1,169 |

Nonparametric Neutral Network Estimation of Lyapunov Exponents and a Direct Test for Chaos |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
21 |

Nonparametric Predictive Regressions for Stock Return Prediction |
0 |
0 |
1 |
51 |
0 |
0 |
1 |
47 |

Nonparametric Predictive Regressions for Stock Return Prediction |
0 |
0 |
3 |
125 |
0 |
2 |
7 |
124 |

Nonparametric Recovery of the Yield Curve Evolution from Cross-Section and Time Series Information |
0 |
0 |
0 |
56 |
0 |
0 |
0 |
94 |

Nonparametric Regression |
0 |
0 |
0 |
74 |
0 |
0 |
1 |
219 |

Nonparametric Regression with a Latent Time Series |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
22 |

Nonparametric Transformation to White Noise |
0 |
0 |
0 |
3 |
0 |
1 |
1 |
37 |

Nonparametric censored and truncated regression |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
75 |

Nonparametric estimation of a periodic sequence in the presence of a smooth trend |
0 |
0 |
0 |
32 |
0 |
0 |
1 |
66 |

Nonparametric estimation of a polarization measure |
0 |
0 |
0 |
39 |
0 |
0 |
0 |
103 |

Nonparametric estimation of a polarization measure |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
36 |

Nonparametric estimation of a polarization measure |
0 |
0 |
0 |
25 |
0 |
0 |
0 |
66 |

Nonparametric estimation of homothetic and homothetically separable functions |
0 |
0 |
0 |
5 |
0 |
0 |
0 |
30 |

Nonparametric estimation of homothetic and homothetically separable functions |
0 |
0 |
0 |
40 |
0 |
0 |
0 |
310 |

Nonparametric estimation of infinite order regression and its application to the risk-return tradeoff |
0 |
0 |
0 |
12 |
0 |
0 |
1 |
51 |

Nonparametric estimation of multivariate elliptic densities via finite mixture sieves |
0 |
0 |
0 |
4 |
0 |
0 |
1 |
32 |

Nonparametric estimation of multivariate elliptic densities via finite mixture sieves |
0 |
0 |
0 |
5 |
0 |
0 |
0 |
28 |

Nonparametric estimation with aggregated data |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
23 |

Nonparametric estimation with aggregated data |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
28 |

Nonparametric factor analysis of time series |
0 |
0 |
0 |
7 |
0 |
0 |
0 |
252 |

Nonparametric inference for unbalance time series data |
0 |
0 |
0 |
58 |
0 |
0 |
0 |
262 |

Nonparametric inference for unbalanced time series data |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
31 |

Nonparametric inference for unbalanced time series data |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
24 |

Nonparametric neural network estimation of Lyapunov exponents and a direct test for chaos |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
20 |

Nonparametric neural network estimation of Lyapunov exponents and a direct test for chaos |
0 |
0 |
0 |
2 |
0 |
0 |
2 |
33 |

Nonparametric neutral network estimation of lyapunov exponents and a direct test for chaos |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
25 |

Nonparametric regression with filtered data |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
3 |

Nonparametric transformation to white noise |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
32 |

On Time Trend of COVID-19: A Panel Data Study |
0 |
0 |
1 |
87 |
0 |
0 |
3 |
330 |

On Time Trend of COVID-19: A Panel Data Study |
0 |
0 |
0 |
47 |
0 |
0 |
1 |
111 |

On Unit Free Assessment of The Extent of Multilateral Distributional Variation |
0 |
0 |
2 |
6 |
0 |
0 |
4 |
30 |

On Unit Free Assessment of The Extent of Multilateral Distributional Variation |
0 |
0 |
0 |
30 |
0 |
0 |
0 |
38 |

On a semiparametric survival model with flexible covariate effect |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
29 |

On the Time Trend of COVID-19: A Panel Data Study |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
19 |

Optimal Smoothing for a Computationally and Statistically Efficient Single Index Estimator |
1 |
1 |
1 |
74 |
2 |
2 |
2 |
150 |

Optimal Smoothing for a Computationallyand StatisticallyEfficient Single Index Estimator |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
16 |

Optimal smoothing for a computationally and statistically efficient single index estimator |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
23 |

Pricing American Options under Stochastic Volatility and Stochastic Interest Rates |
0 |
0 |
1 |
23 |
0 |
0 |
4 |
128 |

Quantilograms under Strong Dependence |
0 |
0 |
0 |
50 |
0 |
0 |
0 |
29 |

Quantilograms under Strong Dependence |
0 |
0 |
0 |
4 |
0 |
0 |
5 |
15 |

Robust Estimation of Integrated and Spot Volatility |
0 |
1 |
2 |
39 |
1 |
2 |
7 |
32 |

Second Order Approximation in a Linear Regression with Heteroskedasticity for Unknown Form |
0 |
0 |
0 |
59 |
0 |
1 |
3 |
469 |

Second Order Approximation in the Partially Linear Regression Model |
0 |
0 |
0 |
165 |
0 |
0 |
2 |
1,268 |

Second-order approximation for adaptive regression estimators |
0 |
0 |
0 |
5 |
0 |
0 |
0 |
25 |

Semiparametric Dynamic Portfolio Choice with Multiple Conditioning Variables |
0 |
0 |
0 |
32 |
0 |
0 |
1 |
119 |

Semiparametric Estimation of Locally Stationary Diffusion Models |
0 |
0 |
0 |
6 |
0 |
0 |
1 |
30 |

Semiparametric Estimation of Markov Decision Processeswith Continuous State Space |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
29 |

Semiparametric Estimation of a Characteristic-Based Factor Model of Stock Returns |
0 |
1 |
1 |
156 |
0 |
1 |
2 |
416 |

Semiparametric Estimation of aCharacteristic-based Factor Model ofCommon Stock Returns |
1 |
1 |
1 |
3 |
1 |
2 |
2 |
35 |

Semiparametric Model Averaging of Ultra-High Dimensional Time Series |
0 |
0 |
0 |
68 |
0 |
0 |
1 |
105 |

Semiparametric Nonlinear Panel Data Models with Measurement Error |
0 |
0 |
0 |
6 |
0 |
0 |
0 |
48 |

Semiparametric Regression Analysis under Imputation for Missing Response Data |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
21 |

Semiparametric dynamic portfolio choice with multiple conditioning variables |
0 |
0 |
0 |
8 |
0 |
0 |
2 |
35 |

Semiparametric estimation of Markov decision processeswith continuous state space |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
29 |

Semiparametric estimation of a characteristic-based factor model of common stock returns |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
43 |

Semiparametric estimation of locally stationary diffusion models |
0 |
0 |
0 |
6 |
0 |
0 |
0 |
24 |

Semiparametric model averaging of ultra-high dimensional time series |
0 |
0 |
0 |
32 |
1 |
1 |
2 |
72 |

Semiparametric nonlinear panel data models with measurement error |
0 |
0 |
0 |
21 |
0 |
0 |
0 |
24 |

Semiparametric regression analysis under imputation for missing response data |
0 |
0 |
0 |
31 |
0 |
0 |
1 |
242 |

Semiparametric regression analysis under imputation for missing response data |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
39 |

Semiparametric regression analysis with missing response at random |
0 |
0 |
0 |
244 |
0 |
1 |
2 |
745 |

Simple Nonparametric Estimators for the Bid-Ask Spread in the Roll Model |
0 |
0 |
0 |
36 |
0 |
0 |
1 |
84 |

Simple Nonparametric Estimators for the Bid-Ask Spread in the Roll Model |
0 |
0 |
0 |
20 |
0 |
1 |
4 |
48 |

Simple Nonparametric Estimators for the Bid-Ask Spread in the Roll Model |
0 |
0 |
0 |
16 |
0 |
0 |
1 |
30 |

Single stock circuit breakers on the London Stock Exchange: do they improve subsequent market quality? |
0 |
0 |
0 |
39 |
0 |
0 |
4 |
264 |

Some Higher Order Theory for a Consistent Nonparametric Model Specification Test |
0 |
0 |
0 |
97 |
0 |
0 |
0 |
385 |

TESTING FOR STOCHASTICMONOTONICITY |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
51 |

Testing Additivity in Generalized Nonparametric Regression Models |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
61 |

Testing Additivity in Generalized Nonparametric Regression Models |
0 |
0 |
1 |
135 |
0 |
0 |
1 |
904 |

Testing Stochastic Dominance with Many Conditioning Variables |
0 |
0 |
1 |
18 |
0 |
1 |
3 |
36 |

Testing for Stochastic Dominance Efficiency |
0 |
0 |
0 |
58 |
0 |
0 |
0 |
144 |

Testing for Time Stochastic Dominance |
0 |
0 |
1 |
60 |
0 |
0 |
6 |
162 |

Testing for stochastic monotonicity |
0 |
0 |
0 |
53 |
0 |
0 |
0 |
157 |

Testing for stochastic monotonicity |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
53 |

Testing for the stochastic dominance efficiency of a given portfolio |
0 |
0 |
0 |
48 |
0 |
0 |
0 |
148 |

Testing the Capital Asset Pricing Model Efficiently Under Elliptical Symmetry: A Semiparametric Approach |
0 |
0 |
0 |
244 |
0 |
1 |
1 |
1,162 |

Testing the Capital Asset Pricing Model Efficiently Under Elliptical Symmetry: A Semiparametric Approach |
0 |
0 |
0 |
645 |
0 |
1 |
1 |
3,490 |

Testing the Capital Asset Pricing Model Efficiently under Elliptical Symmetry: A Semiparametric Approach |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
27 |

Testing the capital asset pricing model efficiently under elliptical symmetry: a semiparametric approach |
0 |
0 |
0 |
2 |
0 |
1 |
2 |
56 |

The Asymptotic Distribution of Nonparametric Estimates of the Lyapunov Exponent for Stochastic Time Series |
0 |
0 |
0 |
244 |
0 |
0 |
0 |
959 |

The Behaviour of Betting and Currency Markets on the Night of the EU Referendum |
0 |
0 |
0 |
16 |
0 |
0 |
1 |
44 |

The Cross-Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability between Time Series |
0 |
0 |
2 |
12 |
0 |
1 |
6 |
92 |

The Effect of Fragmentation in Trading on Market Quality in the UK Equity Market |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
34 |

The Estimation of Conditional Densities |
0 |
0 |
0 |
6 |
0 |
0 |
3 |
29 |

The Existence and Asymptotic Properties of a Backfitting Projection Algorithm Under Weak Conditions |
0 |
0 |
0 |
62 |
0 |
0 |
1 |
314 |

The Existence and Asymptotic Properties of a Backfitting Projection Algorithm under Weak Conditions |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
16 |

The Froot and Stein Model Revisited |
0 |
0 |
2 |
457 |
0 |
0 |
7 |
1,434 |

The Impact of Corporate QE on Liquidity: Evidence from the UK |
0 |
1 |
1 |
37 |
0 |
1 |
2 |
72 |

The Lower Regression Function and Testing Expectation Dependence Dominance Hypotheses |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
8 |

The Lower Regression Function and Testing Expectation Dependence Dominance Hypotheses |
0 |
0 |
0 |
11 |
0 |
0 |
2 |
31 |

The October 2016 sterling flash episode: when liquidity disappeared from one of the world’s most liquid markets |
0 |
1 |
1 |
11 |
0 |
1 |
1 |
47 |

The Shape of the Risk Premium: Evidence from a Semiparametric GARCH Model |
0 |
0 |
0 |
48 |
0 |
0 |
1 |
216 |

The Shape of the Risk Premium: Evidence from a Semiparametric Garch Model |
0 |
0 |
0 |
95 |
0 |
0 |
1 |
364 |

The behaviour of betting and currency markets on the night of the EU referendum |
0 |
0 |
0 |
33 |
0 |
0 |
0 |
52 |

The behaviour of betting and currency markets on the night of the EU referendum |
0 |
0 |
0 |
18 |
0 |
0 |
0 |
29 |

The cross-quantilogram: measuring quantile dependence and testing directional predictability between time series |
0 |
0 |
3 |
45 |
1 |
2 |
10 |
109 |

The cross-sectional spillovers of single stock circuit breakers |
0 |
0 |
0 |
23 |
1 |
1 |
4 |
67 |

The effect of fragmentation in trading on market quality in the UK equity market |
0 |
0 |
0 |
27 |
0 |
0 |
1 |
79 |

The estimation of conditional densities |
0 |
1 |
1 |
7 |
2 |
3 |
4 |
33 |

The existence and asymptotic properties of a backfitting projection algorithm under weak conditions |
0 |
0 |
0 |
2 |
1 |
2 |
2 |
42 |

The existence and asymptotic properties of a backfitting projection algorithm under weak conditions |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
26 |

The impact of corporate QE on liquidity: evidence from the UK |
0 |
1 |
4 |
43 |
1 |
3 |
10 |
120 |

The live method for generalized additive volatility models |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
35 |

The shape of the risk premium: evidence from a semiparametric GARCH model |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
29 |

Uniform Bahadur Representation for LocalPolynomial Estimates of M-Regressionand Its Application to The Additive Model |
0 |
0 |
1 |
3 |
2 |
2 |
6 |
36 |

When will the Covid-19 pandemic peak? |
0 |
0 |
1 |
51 |
0 |
1 |
3 |
144 |

When will the Covid-19 pandemic peak? |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
7 |

Yield Curve Estimation by Kernel Smoothing |
0 |
0 |
0 |
48 |
0 |
0 |
1 |
179 |

Yield Curve Estimation by Kernel Smoothing Methods |
0 |
0 |
0 |
357 |
0 |
0 |
0 |
758 |

Yield Curve Estimation by Kernel Smoothing Methods |
0 |
0 |
1 |
9 |
0 |
0 |
1 |
67 |

Yield curve estimation by kernel smoothing methods |
0 |
0 |
0 |
5 |
0 |
0 |
1 |
34 |

Total Working Papers |
12 |
32 |
141 |
19,259 |
46 |
146 |
781 |
67,368 |