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Last month |
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A Coupled Component GARCH Model for Intraday and Overnight Volatility |
0 |
0 |
1 |
20 |
0 |
1 |
10 |
85 |

A Discrete Choice Model For Large Heterogeneous Panels with Interactive Fixed Effects with an Application to the Determinants of Corporate Bond Issuance |
0 |
0 |
0 |
10 |
0 |
0 |
2 |
46 |

A Dynamic Network of Arbitrage Characteristics |
0 |
0 |
2 |
18 |
0 |
0 |
11 |
54 |

A Dynamic Semiparametric Characteristics-based Model for Optimal Portfolio Selection |
0 |
0 |
0 |
9 |
0 |
1 |
5 |
25 |

A Flexible Semiparametric Model for Time Series |
0 |
0 |
0 |
53 |
0 |
0 |
1 |
77 |

A GARCH Model of the Implied Volatility of the Swiss Market Index From Option Pricesdffrom Options Prices |
0 |
0 |
1 |
310 |
0 |
1 |
8 |
885 |

A GARCH model of the implied volatility of the Swiss Market Index from options prices |
0 |
0 |
1 |
8 |
0 |
0 |
1 |
38 |

A Local Instrumental Variable Estimation Method For Generalized Additive Volatility Models |
0 |
0 |
0 |
50 |
0 |
1 |
2 |
224 |

A Local Instrumental Variable Estimation Method for Generalized Additive Volatility Models |
0 |
0 |
0 |
2 |
0 |
0 |
3 |
23 |

A New Semiparametric Estimation Approach for Large Dynamic Covariance Matrices with Multiple Conditioning Variables |
0 |
0 |
0 |
40 |
1 |
2 |
2 |
69 |

A New Semiparametric Estimation Approach for Large Dynamic Covariance Matrices with Multiple Conditioning Variables |
0 |
0 |
0 |
12 |
0 |
1 |
4 |
51 |

A Nonparametric Regression Estimator that Adapts to Error Distribution of Unknown Form |
0 |
0 |
0 |
4 |
0 |
0 |
1 |
20 |

A Quantilogram Approach to Evaluating Directional Predictability |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
30 |

A Quantilogram Approach to Evaluating Directional Predictability |
0 |
0 |
0 |
117 |
0 |
0 |
0 |
469 |

A ReMeDI for Microstructure Noise |
1 |
2 |
8 |
75 |
1 |
5 |
20 |
154 |

A Semiparametric Panel Model for Unbalanced Data with Application to Climate Change in the United Kingdom |
0 |
0 |
0 |
95 |
1 |
1 |
3 |
237 |

A Semiparametric Panel Model for unbalanced data with Application to Climate Change in the United Kingdom |
0 |
0 |
0 |
60 |
0 |
0 |
1 |
164 |

A Simulation Comparison between Integration and Backfitting Methods of Estimating Separable Nonparametric Regression Models |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
100 |

A Unified Framework for Efficient Estimation of General Treatment Models |
0 |
0 |
1 |
8 |
0 |
0 |
2 |
21 |

A Unified Framework for Efficient Estimation of General Treatment Models |
0 |
0 |
1 |
6 |
0 |
3 |
6 |
31 |

A Unified Framework for Efficient Estimation of General Treatment Models |
0 |
0 |
1 |
29 |
0 |
0 |
2 |
27 |

A Unified Framework for Specification Tests of Continuous Treatment Effect Models |
0 |
0 |
1 |
4 |
0 |
0 |
6 |
26 |

A Unified Framework for Specification Tests of Continuous Treatment Effect Models |
0 |
0 |
0 |
9 |
0 |
0 |
1 |
11 |

A coupled component GARCH model for intraday and overnight volatility |
0 |
0 |
0 |
25 |
0 |
0 |
3 |
21 |

A coupled component GARCH model for intraday and overnight volatility |
0 |
0 |
2 |
66 |
0 |
0 |
5 |
53 |

A discrete choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance |
0 |
0 |
0 |
50 |
1 |
2 |
6 |
82 |

A discrete choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance |
0 |
0 |
2 |
30 |
0 |
0 |
5 |
176 |

A flexible semiparametric model for time series |
0 |
0 |
0 |
50 |
0 |
0 |
3 |
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 |
2 |
31 |

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

A nonparametric regression estimator that adapts to error distribution of unknown form |
0 |
0 |
0 |
32 |
1 |
1 |
3 |
214 |

A nonparametric regression estimator that adapts to error distribution of unknown form |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
38 |

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

A nonparametric test of the leverage hypothesis |
0 |
0 |
0 |
19 |
0 |
0 |
1 |
62 |

A quantilogram approach to evaluating directional predictability |
0 |
0 |
1 |
2 |
0 |
0 |
1 |
25 |

A semiparametric model for heterogeneous panel data with fixed effects |
0 |
1 |
1 |
101 |
1 |
2 |
4 |
262 |

A semiparametric panel model for unbalanced data with application to climate change in the United Kingdom |
0 |
0 |
0 |
4 |
0 |
0 |
4 |
72 |

A simple and efficient estimation method for models with nonignorable missing data |
0 |
0 |
1 |
130 |
0 |
1 |
3 |
262 |

A smoothed least squares estimator for threshold regression models |
0 |
0 |
1 |
18 |
0 |
1 |
5 |
72 |

Adaptive Estimation in ARCH Models |
0 |
0 |
0 |
234 |
0 |
0 |
1 |
609 |

Adaptive Testing in ARCH Models |
0 |
0 |
0 |
172 |
0 |
0 |
0 |
883 |

Additive nonparametric models with time variable and both stationary and nonstationary regressions |
0 |
0 |
1 |
18 |
0 |
1 |
5 |
30 |

An Almost Closed Form Estimator For The EGARCH Model |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
3 |

An Almost Closed Form Estimator for the EGARCH |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
9 |

An Almost Closed Form Estimator for the EGARCH model |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
6 |

An Alternative Way of ComputingEfficient Instrumental VariableEstimators |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
35 |

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 |
1 |
1 |
4 |
583 |

An Improved Bootstrap Test of Stochastic Dominance |
0 |
0 |
1 |
73 |
0 |
0 |
1 |
223 |

An Optimization Interpretation of Integration and Backfitting Estimators for Separable Nonparametric Models |
0 |
0 |
0 |
4 |
0 |
0 |
2 |
81 |

An almost closed form estimator for the EGARCH model |
0 |
0 |
0 |
0 |
1 |
1 |
4 |
21 |

An almost closed form estimator for the EGARCH model |
0 |
0 |
0 |
74 |
0 |
0 |
2 |
108 |

An alternative way of computing efficient instrumental variable estimators |
0 |
0 |
0 |
3 |
0 |
0 |
2 |
51 |

An improved bootstrap test of stochastic dominance |
0 |
0 |
0 |
20 |
0 |
1 |
4 |
100 |

An investigation into Multivariate Variance Ratio Statistics and their application to Stock Market Predictability |
1 |
1 |
2 |
22 |
1 |
2 |
5 |
60 |

An investigation into multivariate variance ratio statistics and their application to stock market predictability |
0 |
0 |
0 |
16 |
0 |
0 |
1 |
51 |

Applied Nonparametric Methods |
2 |
4 |
18 |
1,151 |
8 |
17 |
51 |
2,348 |

Applied nonparametric methods |
1 |
1 |
1 |
370 |
2 |
2 |
6 |
940 |

Are there Monday effects in Stock Returns: A Stochastic Dominance Approach |
0 |
1 |
2 |
322 |
0 |
1 |
6 |
973 |

Are there Monday effects in stock returns: a stochastic dominance approach |
0 |
0 |
0 |
11 |
0 |
0 |
5 |
66 |

Asymptotic Expansions for Some Semiparametric Program Evaluation Estimators |
0 |
0 |
2 |
17 |
0 |
0 |
3 |
41 |

Asymptotic expansions for some semiparametric program evaluation estimators |
0 |
0 |
2 |
177 |
0 |
0 |
4 |
411 |

Asymptotic expansions for some semiparametric program evaluation estimators |
0 |
0 |
0 |
7 |
0 |
0 |
2 |
48 |

Asymptotic properties of a Nadaraya-Watson type estimator for regression functions of in?finite order |
0 |
0 |
0 |
11 |
0 |
1 |
4 |
31 |

Averaging of moment condition estimators |
0 |
0 |
0 |
48 |
0 |
0 |
4 |
96 |

Bootstrap Tests of Stochastic Dominance with Asymptotic Similarity on the Boundary |
0 |
0 |
0 |
64 |
0 |
0 |
0 |
200 |

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 |
0 |
0 |
0 |
2 |
36 |

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

Classification of nonparametric regression functions in heterogeneous panels |
0 |
0 |
0 |
28 |
2 |
2 |
3 |
50 |

Conditional Independence Restrictions: Testing and Estimation |
1 |
1 |
9 |
604 |
2 |
3 |
17 |
2,134 |

Consistent Estimation of the Risk-Return Tradeoff in the Presence of Measurement Error |
0 |
0 |
2 |
26 |
0 |
0 |
4 |
150 |

Consistent Testing for Stochastic Dominance under General Sampling Schemes |
0 |
0 |
1 |
21 |
1 |
2 |
3 |
88 |

Consistent Testing for Stochastic Dominance: A Subsampling Approach |
0 |
0 |
1 |
41 |
0 |
2 |
10 |
216 |

Consistent Testing for Stochastic Dominance: A Subsampling Approach |
0 |
0 |
0 |
189 |
0 |
1 |
3 |
837 |

Consistent Testing for Stochastic Dominance: A Subsampling Approach |
0 |
0 |
0 |
1 |
0 |
5 |
11 |
61 |

Consistent Testing for Stochastic Dominance: A Subsampling Approach |
0 |
0 |
0 |
78 |
0 |
1 |
15 |
503 |

Consistent Testing for an Implication of Supermodular Dominance |
0 |
2 |
8 |
30 |
0 |
7 |
27 |
63 |

Consistent estimation of the risk-return tradeoff in the presence of measurement error |
0 |
0 |
0 |
39 |
0 |
0 |
5 |
193 |

Consistent estimation of the risk-return tradeoff in the presence of measurement error |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
33 |

Consistent testing for stochastic dominance under general sampling schemes |
0 |
1 |
1 |
13 |
0 |
1 |
1 |
65 |

Consistent testing for stochastic dominance: a subsampling approach |
0 |
0 |
0 |
1 |
0 |
6 |
18 |
73 |

Consistent testing for stochastic dominance: a subsampling approach |
0 |
0 |
0 |
144 |
0 |
0 |
4 |
629 |

Consistent testing for stochastic dominance: a subsampling approach |
0 |
0 |
0 |
3 |
0 |
1 |
9 |
65 |

Consistent testing for stochastic dominance: a subsampling approach |
0 |
0 |
0 |
2 |
0 |
0 |
7 |
68 |

Dynamic Autoregressive Liquidity (DArLiQ) |
0 |
0 |
9 |
9 |
0 |
0 |
9 |
9 |

Dynamic Autoregressive Liquidity (DArLiQ) |
0 |
0 |
32 |
32 |
1 |
1 |
12 |
12 |

ESTIMATION OF A SEMIPARAMETRICIGARCH(1,1) MODEL |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
17 |

Edgeworth Approximation for MINPIN Estimators in Semiparametric Regression Models |
0 |
0 |
0 |
54 |
0 |
0 |
2 |
491 |

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 |
0 |
26 |

Efficient Estimation of Nonparametric Regression in The Presence of Dynamic Heteroskedasticity |
0 |
0 |
0 |
50 |
0 |
2 |
8 |
94 |

Efficient Estimation of a Multivariate Multiplicative Volatility Model |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
34 |

Efficient Estimation of a Semiparametric Characteristic- Based Factor Model of Security Returns |
0 |
0 |
0 |
92 |
0 |
0 |
0 |
288 |

Efficient Estimation of a Semiparametric Characteristic-Based Factor Model of Security Returns |
0 |
0 |
0 |
60 |
0 |
1 |
2 |
229 |

Efficient Estimation of a SemiparametricCharacteristic-Based Factor Model of Security Returns |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
30 |

Efficient estimation of a multivariate multiplicative volatility model |
0 |
0 |
0 |
5 |
0 |
0 |
0 |
39 |

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

Efficient estimation of a semiparametric characteristic-based factor model of security returns |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
40 |

Efficient estimation of conditional risk measures in a semiparametric GARCH model |
0 |
0 |
0 |
58 |
0 |
0 |
3 |
96 |

Efficient estimation of generalized additive nonparametric regression models |
0 |
0 |
0 |
5 |
0 |
0 |
2 |
56 |

Estimating Features of a Distribution from Binomial Data |
0 |
0 |
0 |
210 |
0 |
0 |
0 |
1,290 |

Estimating Multiplicative and Additive Hazard Functions by Kernel Methods |
0 |
0 |
0 |
111 |
0 |
0 |
1 |
501 |

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

Estimating Quadratic VariationConsistently in thePresence of Correlated MeasurementError |
0 |
0 |
0 |
0 |
0 |
1 |
7 |
41 |

Estimating Semiparametric ARCH (8) Models by Kernel Smoothing Methods |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
21 |

Estimating Semiparametric ARCH Models by Kernel Smoothing Methods |
0 |
0 |
0 |
111 |
0 |
0 |
0 |
343 |

Estimating Yield Curves by Kernel Smoothing Methods |
0 |
0 |
0 |
691 |
0 |
0 |
0 |
1,887 |

Estimating additive nonparametric models by partial Lq norm: the curse of fractionality |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
23 |

Estimating features of a distribution from binomial data |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
43 |

Estimating features of a distribution from binomial data |
0 |
0 |
0 |
105 |
0 |
1 |
3 |
712 |

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 |
0 |
24 |

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

Estimating semiparametric ARCH (∞) models by kernel smoothing methods |
0 |
1 |
1 |
3 |
0 |
1 |
2 |
30 |

Estimating the Quadratic Covariation Matrix for an Asynchronously Observed Continuous Time Signal Masked by Additive Noise |
0 |
0 |
0 |
49 |
0 |
0 |
0 |
121 |

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

Estimation and Inference in Semiparametric Quantile Factor Models |
2 |
2 |
6 |
10 |
2 |
2 |
8 |
43 |

Estimation and inference in semiparametric quantile factor models |
0 |
1 |
2 |
76 |
0 |
1 |
4 |
136 |

Estimation in semiparametric quantile factor models |
0 |
0 |
0 |
26 |
0 |
0 |
3 |
49 |

Estimation of Additive Regression Models with Links |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
91 |

Estimation of Linear Regression Models by a Spread-Tolerant Estimator |
0 |
0 |
0 |
17 |
0 |
0 |
0 |
127 |

Estimation of Semiparametric Models when the Criterion Function is not Smooth |
0 |
0 |
0 |
6 |
0 |
0 |
1 |
59 |

Estimation of a Multiplicative Correlation Structure in the Large Dimensional Case |
0 |
1 |
1 |
25 |
0 |
2 |
3 |
62 |

Estimation of a Multiplicative Covariance Structure |
0 |
0 |
0 |
26 |
0 |
0 |
2 |
29 |

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

Estimation of a Multiplicative Covariance Structure in the Large Dimensional Case |
0 |
0 |
0 |
11 |
1 |
4 |
10 |
81 |

Estimation of a Nonparametric Model for Bond Prices from Cross-Section and Time Series Information |
1 |
1 |
1 |
50 |
1 |
1 |
6 |
59 |

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 |
2 |
24 |

Estimation of semiparametric models when the criterion function is not smooth |
0 |
0 |
0 |
152 |
1 |
1 |
2 |
617 |

Estimation of semiparametric models when the criterion function is not smooth |
0 |
0 |
1 |
3 |
1 |
1 |
2 |
66 |

Estimation of tail thickness parameters from GJR-GARCH models |
0 |
0 |
1 |
257 |
1 |
2 |
8 |
837 |

Estimation of the Kronecker Covariance Model by Quadratic Form |
0 |
0 |
2 |
6 |
0 |
0 |
4 |
37 |

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 |
0 |
0 |
1 |
145 |
0 |
0 |
2 |
373 |

Evaluating Value-at-Risk Models via Quantile Regressions |
0 |
0 |
1 |
219 |
2 |
2 |
9 |
548 |

Evaluating Value-at-Risk models via Quantile Regression |
0 |
0 |
0 |
199 |
0 |
0 |
2 |
536 |

Evaluating Value-at-Risk models via Quantile regressions |
0 |
0 |
0 |
184 |
0 |
0 |
4 |
409 |

Evaluating hedge fund performance: a stochastic dominance approach |
0 |
0 |
1 |
158 |
0 |
0 |
1 |
365 |

Evaluating hedge fund performance: a stochastic dominance approach |
0 |
0 |
0 |
7 |
0 |
0 |
3 |
55 |

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

Flexible Term Structure Estimation: Which Method Is Preferred? |
0 |
0 |
2 |
186 |
0 |
0 |
2 |
484 |

Flexible Term Structure Estimation: Which Method is Preferable? |
0 |
0 |
1 |
42 |
0 |
0 |
3 |
164 |

Flexible Term Structure Estimation: Which Method is Preferred? |
0 |
0 |
1 |
216 |
0 |
0 |
1 |
484 |

Flexible term structure estimation: which method is preferable? |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
29 |

Global Bahadur representation for nonparametric censored regression quantiles and its applications |
0 |
0 |
0 |
49 |
0 |
0 |
3 |
109 |

High Dimensional Semiparametric Moment Restriction Models |
0 |
0 |
0 |
14 |
0 |
1 |
3 |
56 |

High dimensional semiparametric moment restriction models |
0 |
0 |
0 |
27 |
0 |
1 |
1 |
56 |

High dimensional semiparametric moment restriction models |
0 |
0 |
0 |
4 |
0 |
1 |
1 |
37 |

High dimensional semiparametric moment restriction models |
0 |
0 |
1 |
53 |
0 |
0 |
7 |
114 |

High dimensional semiparametric moment restriction models |
0 |
0 |
0 |
2 |
1 |
1 |
2 |
28 |

Identification and Nonparametric Estimation of a Transformed Additively Separable Model |
0 |
0 |
0 |
58 |
0 |
1 |
5 |
268 |

Identification and nonparametric estimation of a transformed additively separable model |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
52 |

Implications of High-Frequency Trading for Security Markets |
0 |
0 |
0 |
68 |
0 |
1 |
13 |
108 |

Implications of high-frequency trading for security markets |
0 |
0 |
1 |
17 |
0 |
0 |
1 |
46 |

Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance |
0 |
0 |
14 |
14 |
0 |
1 |
8 |
8 |

Inference about Realized Volatility using Infill Subsampling |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
17 |

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 |
1 |
52 |
0 |
0 |
2 |
71 |

Inference on a semiparametric model with global power law and local nonparametric trends |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
31 |

Integration and Backfitting methods in additive models: finite sample properties and comparison |
0 |
0 |
0 |
4 |
0 |
0 |
2 |
20 |

Is There Chaos in the World Economy? A Nonparametric Test Using Consistent Standard Errors |
0 |
0 |
0 |
208 |
0 |
0 |
0 |
602 |

Is There Chaos in the World Economy? A Nonparametric Test Using Consistent Standard Errors |
0 |
0 |
0 |
278 |
0 |
0 |
3 |
875 |

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 |
1 |
34 |
0 |
0 |
2 |
99 |

Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems |
0 |
0 |
0 |
206 |
0 |
0 |
0 |
912 |

Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems |
0 |
0 |
0 |
0 |
0 |
0 |
5 |
37 |

Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems |
0 |
0 |
0 |
104 |
0 |
0 |
3 |
416 |

Limit theorems for estimating the parameters of differentiated product demand systems |
0 |
0 |
0 |
1 |
0 |
1 |
2 |
45 |

Local Linear Fitting Under Near Epoch Dependence: Uniform consistency with Convergence Rates |
0 |
0 |
2 |
39 |
0 |
0 |
5 |
90 |

Local Nonlinear Least Squares Estimation: Using Parametric Information Nonparametrically |
0 |
0 |
2 |
289 |
0 |
0 |
2 |
1,148 |

Loch Linear Fitting under Near Epoch Dependence: Uniform Consistency with Convergence Rate |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
22 |

Loch linear fitting under near epoch dependence: uniform consistency with convergence rate |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
25 |

Making Inferences About Rich Country - Poor Country Convergence: The Polarization Trapezoid and Overlap measures |
0 |
0 |
0 |
72 |
0 |
0 |
0 |
389 |

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 |
241 |
0 |
0 |
2 |
805 |

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 |
5 |
0 |
0 |
1 |
38 |

Multi-step non- and semi-parametric predictive regressions for short and long horizon stock return prediction |
0 |
0 |
0 |
30 |
0 |
0 |
1 |
63 |

Multi-step non- and semi-parametric predictive regressions for short and long horizon stock return prediction |
0 |
1 |
1 |
71 |
1 |
3 |
3 |
94 |

Multiscale clustering of nonparametric regression curves |
0 |
0 |
0 |
17 |
0 |
0 |
0 |
27 |

Multivariate Variance Ratio Statistics |
0 |
0 |
0 |
6 |
0 |
0 |
1 |
29 |

Multivariate variance ratio statistics |
0 |
0 |
0 |
32 |
0 |
0 |
0 |
79 |

Non Parametric Estimation of a Polarization Measure |
0 |
0 |
1 |
33 |
0 |
0 |
4 |
99 |

Non-Standard Errors |
0 |
2 |
23 |
23 |
10 |
22 |
160 |
160 |

Non-Standard Errors |
2 |
3 |
36 |
36 |
15 |
40 |
233 |
233 |

Non-parametric transformation regression with non-stationary data |
0 |
0 |
0 |
46 |
0 |
0 |
1 |
70 |

Nonparametric Censored Regression |
0 |
0 |
1 |
410 |
1 |
1 |
3 |
1,372 |

Nonparametric Censored and Truncated Regression |
0 |
0 |
1 |
4 |
0 |
0 |
6 |
67 |

Nonparametric Censored and Truncated Regression |
0 |
0 |
1 |
478 |
0 |
0 |
3 |
1,881 |

Nonparametric Censored and Truncated Regression |
0 |
0 |
0 |
197 |
0 |
0 |
0 |
527 |

Nonparametric Estimation of Additive Seperable Regression Models |
0 |
0 |
0 |
18 |
0 |
1 |
5 |
155 |

Nonparametric Estimation of Homothetic and Homothetically Separable Functions |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
21 |

Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data |
0 |
0 |
13 |
13 |
0 |
1 |
8 |
8 |

Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data |
0 |
0 |
53 |
53 |
1 |
2 |
12 |
12 |

Nonparametric Estimation of a Multifactor Heath-Jarrow-Morton Model: An Integrated Approach |
0 |
0 |
0 |
209 |
0 |
0 |
0 |
683 |

Nonparametric Estimation of a Polarization Measure |
0 |
0 |
1 |
59 |
0 |
0 |
1 |
186 |

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

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

Nonparametric Euler Equation Identi?cation and Estimation |
0 |
0 |
1 |
26 |
0 |
1 |
6 |
27 |

Nonparametric Euler Equation Identification and Estimation |
0 |
0 |
1 |
51 |
1 |
1 |
16 |
173 |

Nonparametric Euler Equation Identification andEstimation |
1 |
1 |
1 |
46 |
1 |
3 |
8 |
93 |

Nonparametric Euler equation identification and estimation |
0 |
0 |
0 |
41 |
1 |
2 |
5 |
109 |

Nonparametric Inference for Unbalanced Time Series Data |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
19 |

Nonparametric Matching and Efficient Estimators of Homothetically Separable Functions |
0 |
0 |
0 |
135 |
0 |
0 |
4 |
725 |

Nonparametric Neural Network Estimation of Lyapunov Exponents and a Direct Test for Chaos |
0 |
0 |
0 |
368 |
0 |
0 |
5 |
1,168 |

Nonparametric Neural Network Estimation of Lyapunov Exponents and a Direct Test for Chaos |
0 |
0 |
0 |
1 |
0 |
0 |
3 |
25 |

Nonparametric Neutral Network Estimation of Lyapunov Exponents and a Direct Test for Chaos |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
20 |

Nonparametric Predictive Regressions for Stock Return Prediction |
0 |
0 |
3 |
50 |
0 |
0 |
12 |
41 |

Nonparametric Predictive Regressions for Stock Return Prediction |
2 |
2 |
2 |
122 |
2 |
3 |
4 |
116 |

Nonparametric Recovery of the Yield Curve Evolution from Cross-Section and Time Series Information |
0 |
0 |
2 |
56 |
1 |
1 |
6 |
93 |

Nonparametric Regression |
0 |
0 |
0 |
74 |
1 |
2 |
5 |
215 |

Nonparametric Regression with a Latent Time Series |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
21 |

Nonparametric Transformation to White Noise |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
36 |

Nonparametric censored and truncated regression |
0 |
0 |
0 |
4 |
0 |
0 |
1 |
74 |

Nonparametric estimation of a periodic sequence in the presence of a smooth trend |
0 |
0 |
0 |
32 |
0 |
1 |
2 |
64 |

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

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

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

Nonparametric estimation of homothetic and homothetically separable functions |
0 |
0 |
0 |
5 |
0 |
0 |
2 |
30 |

Nonparametric estimation of homothetic and homothetically separable functions |
0 |
0 |
0 |
40 |
0 |
0 |
1 |
310 |

Nonparametric estimation of infinite order regression and its application to the risk-return tradeoff |
0 |
0 |
0 |
12 |
0 |
0 |
0 |
49 |

Nonparametric estimation of multivariate elliptic densities via finite mixture sieves |
0 |
0 |
0 |
4 |
0 |
1 |
1 |
31 |

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 |
3 |
0 |
0 |
2 |
28 |

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

Nonparametric factor analysis of time series |
0 |
0 |
0 |
7 |
0 |
0 |
1 |
252 |

Nonparametric inference for unbalance time series data |
0 |
0 |
0 |
58 |
0 |
0 |
1 |
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 |
31 |

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 |
0 |
1 |

Nonparametric transformation to white noise |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
31 |

On Time Trend of COVID-19: A Panel Data Study |
0 |
0 |
17 |
84 |
1 |
5 |
48 |
321 |

On Time Trend of COVID-19: A Panel Data Study |
0 |
0 |
2 |
46 |
0 |
0 |
3 |
109 |

On Unit Free Assessment of The Extent of Multilateral Distributional Variation |
0 |
0 |
0 |
4 |
0 |
0 |
6 |
25 |

On Unit Free Assessment of The Extent of Multilateral Distributional Variation |
0 |
0 |
1 |
30 |
0 |
0 |
1 |
38 |

On a semiparametric survival model with flexible covariate effect |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
28 |

On the Time Trend of COVID-19: A Panel Data Study |
0 |
0 |
0 |
2 |
0 |
0 |
4 |
19 |

Optimal Smoothing for a Computationally and Statistically Efficient Single Index Estimator |
0 |
0 |
0 |
73 |
0 |
0 |
0 |
148 |

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 |
0 |
22 |

Pricing American Options under Stochastic Volatility and Stochastic Interest Rates |
0 |
0 |
0 |
22 |
0 |
0 |
1 |
124 |

Quantilograms under Strong Dependence |
0 |
0 |
1 |
4 |
0 |
0 |
4 |
9 |

Quantilograms under Strong Dependence |
0 |
0 |
0 |
50 |
0 |
1 |
1 |
29 |

Robust Estimation of Integrated and Spot Volatility |
1 |
2 |
4 |
36 |
2 |
3 |
7 |
23 |

Second Order Approximation in a Linear Regression with Heteroskedasticity for Unknown Form |
0 |
0 |
1 |
59 |
0 |
0 |
2 |
466 |

Second Order Approximation in the Partially Linear Regression Model |
0 |
0 |
1 |
165 |
0 |
0 |
3 |
1,266 |

Second-order approximation for adaptive regression estimators |
0 |
0 |
0 |
5 |
0 |
0 |
1 |
25 |

Semiparametric Dynamic Portfolio Choice with Multiple Conditioning Variables |
0 |
0 |
0 |
32 |
0 |
1 |
2 |
118 |

Semiparametric Estimation of Locally Stationary Diffusion Models |
0 |
0 |
0 |
6 |
0 |
0 |
3 |
29 |

Semiparametric Estimation of Markov Decision Processeswith Continuous State Space |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
28 |

Semiparametric Estimation of a Characteristic-Based Factor Model of Stock Returns |
0 |
0 |
1 |
155 |
0 |
1 |
2 |
414 |

Semiparametric Estimation of aCharacteristic-based Factor Model ofCommon Stock Returns |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
31 |

Semiparametric Model Averaging of Ultra-High Dimensional Time Series |
0 |
0 |
0 |
68 |
0 |
0 |
3 |
103 |

Semiparametric Nonlinear Panel Data Models with Measurement Error |
0 |
0 |
2 |
6 |
0 |
0 |
4 |
48 |

Semiparametric Regression Analysis under Imputation for Missing Response Data |
0 |
0 |
0 |
3 |
0 |
1 |
1 |
20 |

Semiparametric dynamic portfolio choice with multiple conditioning variables |
0 |
0 |
0 |
8 |
0 |
0 |
0 |
33 |

Semiparametric estimation of Markov decision processeswith continuous state space |
0 |
0 |
0 |
1 |
0 |
0 |
2 |
29 |

Semiparametric estimation of a characteristic-based factor model of common stock returns |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
43 |

Semiparametric estimation of locally stationary diffusion models |
0 |
0 |
0 |
6 |
0 |
0 |
1 |
24 |

Semiparametric model averaging of ultra-high dimensional time series |
0 |
0 |
0 |
32 |
2 |
3 |
6 |
69 |

Semiparametric nonlinear panel data models with measurement error |
0 |
0 |
1 |
21 |
0 |
0 |
1 |
24 |

Semiparametric regression analysis under imputation for missing response data |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
37 |

Semiparametric regression analysis under imputation for missing response data |
0 |
0 |
0 |
31 |
0 |
0 |
3 |
241 |

Semiparametric regression analysis with missing response at random |
0 |
0 |
0 |
244 |
0 |
0 |
4 |
741 |

Simple Nonparametric Estimators for the Bid-Ask Spread in the Roll Model |
0 |
0 |
1 |
20 |
0 |
0 |
2 |
44 |

Simple Nonparametric Estimators for the Bid-Ask Spread in the Roll Model |
0 |
0 |
1 |
16 |
0 |
1 |
2 |
29 |

Simple Nonparametric Estimators for the Bid-Ask Spread in the Roll Model |
0 |
0 |
0 |
36 |
1 |
1 |
1 |
81 |

Single stock circuit breakers on the London Stock Exchange: do they improve subsequent market quality? |
0 |
0 |
0 |
39 |
1 |
2 |
7 |
252 |

Some Higher Order Theory for a Consistent Nonparametric Model Specification Test |
0 |
1 |
2 |
97 |
0 |
1 |
4 |
385 |

TESTING FOR STOCHASTICMONOTONICITY |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
51 |

Testing Additivity in Generalized Nonparametric Regression Models |
0 |
0 |
0 |
134 |
0 |
1 |
1 |
903 |

Testing Additivity in Generalized Nonparametric Regression Models |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
59 |

Testing Stochastic Dominance with Many Conditioning Variables |
0 |
0 |
2 |
16 |
0 |
0 |
4 |
32 |

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

Testing for Time Stochastic Dominance |
0 |
0 |
12 |
58 |
2 |
7 |
59 |
151 |

Testing for stochastic monotonicity |
0 |
0 |
1 |
53 |
1 |
1 |
4 |
157 |

Testing for stochastic monotonicity |
0 |
0 |
1 |
2 |
0 |
0 |
2 |
52 |

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 |
1 |
645 |
0 |
0 |
4 |
3,484 |

Testing the Capital Asset Pricing Model Efficiently Under Elliptical Symmetry: A Semiparametric Approach |
0 |
0 |
0 |
244 |
0 |
0 |
1 |
1,160 |

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 |
0 |
0 |
53 |

The Asymptotic Distribution of Nonparametric Estimates of the Lyapunov Exponent for Stochastic Time Series |
0 |
0 |
0 |
243 |
0 |
0 |
1 |
958 |

The Behaviour of Betting and Currency Markets on the Night of the EU Referendum |
0 |
0 |
0 |
15 |
0 |
1 |
2 |
42 |

The Cross-Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability between Time Series |
0 |
0 |
1 |
9 |
1 |
2 |
13 |
79 |

The Effect of Fragmentation in Trading on Market Quality in the UK Equity Market |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
33 |

The Estimation of Conditional Densities |
1 |
1 |
1 |
4 |
1 |
3 |
4 |
22 |

The Existence and Asymptotic Properties of a Backfitting Projection Algorithm Under Weak Conditions |
0 |
0 |
0 |
62 |
0 |
0 |
4 |
311 |

The Existence and Asymptotic Properties of a Backfitting Projection Algorithm under Weak Conditions |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
14 |

The Froot and Stein Model Revisited |
1 |
1 |
1 |
454 |
2 |
3 |
5 |
1,424 |

The Impact of Corporate QE on Liquidity: Evidence from the UK |
0 |
0 |
1 |
36 |
1 |
1 |
8 |
70 |

The Lower Regression Function and Testing Expectation Dependence Dominance Hypotheses |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
7 |

The Lower Regression Function and Testing Expectation Dependence Dominance Hypotheses |
0 |
0 |
1 |
11 |
0 |
2 |
5 |
29 |

The October 2016 sterling flash episode: when liquidity disappeared from one of the world’s most liquid markets |
0 |
0 |
0 |
10 |
0 |
1 |
1 |
46 |

The Shape of the Risk Premium: Evidence from a Semiparametric GARCH Model |
0 |
0 |
0 |
48 |
0 |
0 |
0 |
213 |

The Shape of the Risk Premium: Evidence from a Semiparametric Garch Model |
0 |
0 |
0 |
95 |
0 |
0 |
1 |
363 |

The behaviour of betting and currency markets on the night of the EU referendum |
0 |
0 |
0 |
33 |
0 |
0 |
2 |
51 |

The behaviour of betting and currency markets on the night of the EU referendum |
0 |
0 |
0 |
18 |
0 |
0 |
1 |
29 |

The cross-quantilogram: measuring quantile dependence and testing directional predictability between time series |
0 |
0 |
0 |
42 |
0 |
1 |
10 |
98 |

The cross-sectional spillovers of single stock circuit breakers |
0 |
0 |
3 |
22 |
0 |
0 |
8 |
61 |

The effect of fragmentation in trading on market quality in the UK equity market |
0 |
0 |
0 |
27 |
0 |
0 |
0 |
78 |

The estimation of conditional densities |
0 |
0 |
0 |
6 |
0 |
0 |
1 |
28 |

The existence and asymptotic properties of a backfitting projection algorithm under weak conditions |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
40 |

The existence and asymptotic properties of a backfitting projection algorithm under weak conditions |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
24 |

The impact of corporate QE on liquidity: evidence from the UK |
0 |
0 |
1 |
39 |
1 |
3 |
10 |
109 |

The live method for generalized additive volatility models |
0 |
0 |
0 |
1 |
2 |
2 |
3 |
35 |

The shape of the risk premium: evidence from a semiparametric GARCH model |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
28 |

Uniform Bahadur Representation for LocalPolynomial Estimates of M-Regressionand Its Application to The Additive Model |
0 |
0 |
0 |
2 |
0 |
0 |
2 |
30 |

When will the Covid-19 pandemic peak? |
0 |
0 |
0 |
0 |
0 |
4 |
4 |
5 |

When will the Covid-19 pandemic peak? |
0 |
0 |
3 |
50 |
0 |
0 |
6 |
141 |

Yield Curve Estimation by Kernel Smoothing |
0 |
0 |
0 |
48 |
0 |
0 |
2 |
178 |

Yield Curve Estimation by Kernel Smoothing Methods |
0 |
0 |
0 |
356 |
0 |
0 |
0 |
757 |

Yield Curve Estimation by Kernel Smoothing Methods |
0 |
2 |
2 |
8 |
0 |
2 |
4 |
65 |

Yield curve estimation by kernel smoothing methods |
0 |
0 |
0 |
5 |
0 |
1 |
2 |
33 |

Total Working Papers |
17 |
37 |
369 |
19,043 |
88 |
253 |
1,457 |
66,078 |