| Working Paper |
File Downloads |
Abstract Views |
| Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
| A Functional Connectivity Approach for Modeling Cross-Sectional Dependence with an Application to the Estimation of Hedonic Housing Prices in Paris |
0 |
0 |
0 |
23 |
1 |
2 |
8 |
146 |
| A Robust Hausman-Taylor Estimator |
0 |
0 |
0 |
113 |
5 |
9 |
21 |
387 |
| Adaptive estimation of heteroskedastic error component model |
0 |
0 |
1 |
132 |
1 |
4 |
9 |
362 |
| Assessing the Contribution of R&D to Total Factor Productivity – a Bayesian Approach to Account for Heterogeneity And Heteroscedasticity |
0 |
0 |
0 |
16 |
1 |
2 |
8 |
68 |
| Bayesian Panel Quantile Regression for Binary Outcomes with Correlated Random Effects: An Application on Crime Recidivism in Canada |
0 |
0 |
0 |
9 |
3 |
4 |
13 |
39 |
| Bayesian Panel Quantile Regression for Binary Outcomes with Correlated Random Effects: An Application on Crime Recidivism in Canada |
0 |
0 |
0 |
15 |
1 |
1 |
5 |
38 |
| Bayesian Panel Quantile Regression for Binary Outcomes with Correlated Random Effects: An Application on Crime Recidivism in Canada |
0 |
0 |
0 |
13 |
2 |
2 |
3 |
29 |
| Bayesian panel quantile regression for binary outcomes with correlated random effects: an application on crime recidivism in Canada |
0 |
0 |
0 |
0 |
3 |
4 |
9 |
11 |
| Carbon Dioxide Emissions and Economic Activities: A Mean Field Variational Bayes Semiparametric Panel Data Model with Random Coefficients |
0 |
0 |
0 |
0 |
0 |
2 |
7 |
7 |
| Comparison of forecast performance for homogeneous, heterogeneous and shrinkage estimators |
0 |
0 |
0 |
0 |
0 |
3 |
8 |
9 |
| Crowding-out effects of cruise tourism on stay-over tourism within the Caribbean. A non parametric panel data evidence |
0 |
0 |
0 |
46 |
1 |
1 |
5 |
185 |
| Fixed effects, random effects or Hausman Taylor? |
0 |
0 |
0 |
1 |
0 |
1 |
10 |
13 |
| Forecasting with Spatial Panel Data |
0 |
0 |
0 |
31 |
2 |
6 |
11 |
121 |
| Forecasting with Spatial Panel Data |
0 |
0 |
0 |
248 |
2 |
5 |
20 |
493 |
| Growth Empirics: A Bayesian Semiparametric Model with Random Coefficients for a Panel of OECD Countries |
0 |
0 |
0 |
18 |
4 |
5 |
15 |
63 |
| HOW IMPORTANT IS INNOVATION? A BAYESIAN FACTOR-AUGMENTED PRODUCTIVITY MODEL BASED ON PANEL DATA |
0 |
0 |
0 |
0 |
4 |
4 |
7 |
7 |
| Hedonic Housing Prices in Paris: An Unbalanced Spatial Lag Pseudo-Panel Model with Nested Random Effects |
0 |
0 |
0 |
61 |
2 |
10 |
18 |
208 |
| Heteroskedasticity and Random Coefficient Model on Panel Data |
0 |
0 |
0 |
41 |
2 |
3 |
11 |
103 |
| Homogeneous, heterogeneous or shrinkage estimators? Some empirical evidence from French regional gasoline consumption |
0 |
0 |
0 |
0 |
0 |
0 |
23 |
23 |
| Homogeneous, heterogeneous or shrinkage estimators? Some empirical evidence from French regional gasoline consumption |
0 |
0 |
1 |
393 |
0 |
2 |
17 |
1,342 |
| Households gasoline expenditures in French communes: a two-part hierarchical model with correlated random effects |
0 |
0 |
0 |
10 |
2 |
3 |
10 |
37 |
| How important is innovation? A Bayesian factor-augmented productivity model on panel data |
0 |
0 |
0 |
60 |
2 |
7 |
17 |
826 |
| How important is innovation? A Bayesian factor-augmented productivity model on panel data |
0 |
0 |
0 |
26 |
2 |
4 |
7 |
182 |
| How important is innovation?: A Bayesian factor-augmented productivity model on panel data |
0 |
0 |
0 |
62 |
1 |
3 |
12 |
135 |
| Hétérogénéité de l'offre et de la demande touristiques des communes de la Martinique: une estimation non paramétrique sur données de panel |
0 |
0 |
0 |
31 |
0 |
0 |
7 |
255 |
| Infections, Accidents and Nursing Overtime in a Neonatal Intensive Care Unit: A Bayesian Semiparametric Panel Data Logit Model |
0 |
0 |
0 |
9 |
2 |
4 |
11 |
29 |
| Infections, Accidents and Nursing Overtime in a Neonatal Intensive Care Unit: A Bayesian Semiparametric Panel Data Logit Model |
0 |
0 |
0 |
31 |
3 |
4 |
8 |
54 |
| Infections, accidents and nursing overtime in a neonatal intensive care unit |
0 |
0 |
0 |
0 |
2 |
3 |
8 |
9 |
| Is urban sprawl stimulated by economic growth ? A hierarchical Bayes estimation on the largest metropolitan areas in France |
0 |
1 |
1 |
142 |
1 |
2 |
8 |
434 |
| Joint LM Test for Homoskedasticity in a One-Way error Component Model |
0 |
0 |
0 |
286 |
1 |
3 |
8 |
1,058 |
| Joint LM test for homoskedasticity in a one-way error component model |
0 |
0 |
0 |
145 |
3 |
24 |
41 |
631 |
| L’industrie de croisière aux Caraïbes tend-elle à évincer l’industrie du tourisme de séjour? |
0 |
0 |
0 |
27 |
2 |
2 |
5 |
88 |
| ML estimation and LM tests for panel SUR with spatial lag and spatial errors: An application to hedonic housing prices in Paris |
0 |
0 |
0 |
31 |
3 |
3 |
9 |
145 |
| Maximum Likelihood Estimation and Lagrange Multiplier Tests for Panel Seemingly Unrelated Regressions with Spatial Lag and Spatial Errors: An Application to Hedonic Housing Prices in Paris |
0 |
0 |
1 |
190 |
3 |
4 |
12 |
540 |
| Nighttime Light Pollution and Economic Activities: A Spatio-Temporal Model with Common Factors for US Counties |
0 |
0 |
0 |
11 |
1 |
6 |
16 |
36 |
| Nighttime light pollution and economic activities: A spatio-temporal model with common factors for US counties |
0 |
0 |
1 |
16 |
0 |
2 |
15 |
30 |
| Panel Unit Root Tests and Spatial Dependence |
0 |
0 |
0 |
230 |
2 |
5 |
20 |
440 |
| Panel Unit Root Tests and Spatial Dependence |
0 |
0 |
0 |
16 |
1 |
2 |
17 |
102 |
| Patents and R&D spillovers in some European regions: a dynamic count panel data model |
0 |
0 |
0 |
26 |
1 |
1 |
8 |
51 |
| Robust Dynamic Panel Data Models Using e-Contamination |
0 |
0 |
0 |
18 |
2 |
3 |
15 |
63 |
| Robust Dynamic Panel Data Models Using ε-contamination |
0 |
0 |
1 |
38 |
2 |
3 |
12 |
51 |
| Robust Dynamic Panel Data Models Using 𝛆𝛆-Contamination |
0 |
0 |
0 |
26 |
4 |
9 |
14 |
43 |
| Robust Dynamic Space-Time Panel Data Models Using ?-Contamination: An Application to Crop Yields and Climate Change |
0 |
0 |
0 |
71 |
3 |
4 |
16 |
33 |
| Robust Dynamic Space-Time Panel Data Models Using ε-contamination: An Application to Crop Yields and Climate Change |
0 |
0 |
1 |
68 |
2 |
3 |
17 |
21 |
| Robust Linear Static Panel Data Models Using ?-Contamination |
0 |
1 |
1 |
14 |
0 |
2 |
7 |
55 |
| Robust Linear Static Panel Data Models Using ε-Contamination |
0 |
0 |
0 |
12 |
1 |
2 |
5 |
36 |
| Robust dynamic space-time panel data models using ε-contamination: An application to crop yields and climate change |
0 |
0 |
0 |
45 |
1 |
1 |
7 |
22 |
| Robust dynamic space\textendashtime panel data models using \textdollar\textdollar\\varepsilon \textdollar\textdollar-contamination: an application to crop yields and climate change |
0 |
0 |
0 |
0 |
3 |
4 |
9 |
10 |
| Robust linear static panel data models using e-contamination |
0 |
0 |
0 |
24 |
3 |
7 |
12 |
88 |
| Robust linear static panel data models using epsilon-contamination |
0 |
0 |
0 |
38 |
2 |
4 |
13 |
88 |
| Robust linear static panel data models using ε-contamination |
0 |
0 |
0 |
17 |
2 |
7 |
7 |
67 |
| Robust linear static panel data models using≤ssmml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="mml166" display="inline" overflow="scroll" altimg="si85.gif"\greater≤ssmml:mi\greater\upepsilon≤ss/mml:mi\greater≤ss/mml:math\greater-contamination |
0 |
0 |
0 |
0 |
2 |
3 |
9 |
10 |
| Seemingly Unrelated Regression with Measurement Error: Estimation via Markov chain Monte Carlo and Mean Field Variational Bayes Approximation |
0 |
0 |
0 |
17 |
4 |
5 |
16 |
38 |
| Testing the Fixed Effects Restrictions? A Monte Carlo Study of Chamberlain's Minimum Chi-Squared Test |
0 |
0 |
0 |
165 |
6 |
9 |
17 |
475 |
| Testing the fixed effects restrictions? A Monte Carlo study of Chamberlain’s Minimum Chi-Squared test |
0 |
0 |
0 |
14 |
2 |
2 |
8 |
112 |
| The main determinants of the demand for public transport: a comparative analysis of England and France using shrinkage estimators |
0 |
0 |
0 |
0 |
6 |
6 |
10 |
14 |
| Tobin q: Forecast performance for hierarchical Bayes, shrinkage, heterogeneous and homogeneous panel data estimators |
0 |
0 |
0 |
0 |
1 |
3 |
8 |
8 |
| Two-part hierarchical model with correlated random effects: an Application on households gasoline expenditures in French communes |
0 |
0 |
1 |
8 |
0 |
1 |
4 |
35 |
| Using Machine Learning to Predict Nosocomial Infections and Medical Accidents in a NICU |
0 |
0 |
1 |
4 |
4 |
4 |
13 |
18 |
| Using Machine Learning to Predict Nosocomial Infections and Medical Accidents in a NICU |
0 |
0 |
0 |
7 |
2 |
5 |
15 |
41 |
| Using machine learning to predict nosocomial infections and medical accidents in a NICU |
0 |
0 |
0 |
0 |
2 |
2 |
9 |
15 |
| Total Working Papers |
0 |
2 |
10 |
3,095 |
120 |
241 |
710 |
10,079 |
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