| Working Paper |
File Downloads |
Abstract Views |
| Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
| 20th Symposium on Monetary and Financial Economics |
0 |
0 |
0 |
0 |
2 |
2 |
10 |
29 |
| A Bayesian approach to probability default model calibration: Theoretical and empirical insights on the Jeffreys test |
0 |
0 |
0 |
0 |
3 |
9 |
12 |
12 |
| A Comment on The Dynamic Macroeconomic Effects of Public Capital |
0 |
0 |
0 |
0 |
2 |
2 |
7 |
19 |
| A DARE for VaR |
0 |
0 |
0 |
0 |
1 |
1 |
4 |
14 |
| A Theoretical and Empirical Assessment of the Bank Lending Channel and Loan Market Disequilibrium in Poland |
0 |
0 |
0 |
118 |
0 |
5 |
16 |
436 |
| A Theoretical and Empirical Comparison of Systemic Risk Measures |
0 |
0 |
0 |
0 |
0 |
1 |
13 |
177 |
| A Theoretical and Empirical Comparison of Systemic Risk Measures |
0 |
0 |
2 |
265 |
1 |
2 |
13 |
655 |
| A Theoretical and Empirical Comparison of Systemic Risk Measures: MES versus CoVaR |
0 |
0 |
0 |
7 |
1 |
3 |
20 |
135 |
| Backtesting Expected Shortfall: Accounting for both duration and severity with bivariate orthogonal polynomials |
0 |
0 |
2 |
9 |
3 |
5 |
20 |
42 |
| Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures |
0 |
0 |
0 |
0 |
1 |
5 |
18 |
67 |
| Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures |
0 |
0 |
0 |
0 |
0 |
4 |
15 |
31 |
| Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures |
0 |
0 |
0 |
55 |
2 |
3 |
18 |
106 |
| Backtesting VaR Accuracy: A New Simple Test |
0 |
0 |
0 |
221 |
1 |
2 |
12 |
634 |
| Backtesting VaR Accuracy: A Simple and Powerful Test |
0 |
0 |
0 |
17 |
3 |
3 |
8 |
67 |
| Backtesting Value at Risk Accuracy: A New Simple Test |
0 |
0 |
0 |
0 |
1 |
1 |
4 |
24 |
| Backtesting Value at Risk Accuracy: A New Simple Test |
0 |
0 |
0 |
0 |
2 |
2 |
10 |
36 |
| Backtesting Value at Risk Accuracy: A New Simple Test |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
17 |
| Backtesting Value at Risk Accuracy: A New Simple Test |
0 |
0 |
0 |
0 |
1 |
1 |
7 |
34 |
| Backtesting Value at Risk Accuracy: A New Simple Test |
0 |
0 |
0 |
0 |
0 |
0 |
4 |
20 |
| Backtesting Value at Risk Accuracy: A New Simple Test |
0 |
0 |
0 |
0 |
2 |
2 |
5 |
32 |
| Backtesting Value-at-Risk Accuracy: A New Simple Test |
0 |
0 |
0 |
0 |
3 |
3 |
6 |
35 |
| Backtesting Value-at-Risk Accuracy: A New Simple Test |
0 |
0 |
0 |
0 |
2 |
2 |
7 |
26 |
| Backtesting Value-at-Risk: A GMM Duration-Based Test |
0 |
0 |
0 |
0 |
2 |
2 |
7 |
45 |
| Backtesting Value-at-Risk: A GMM Duration-Based Test |
0 |
0 |
0 |
0 |
3 |
4 |
13 |
43 |
| Backtesting Value-at-Risk: A GMM Duration-Based Test |
0 |
0 |
2 |
26 |
2 |
2 |
14 |
136 |
| Backtesting Value-at-Risk: A GMM Duration-Based Test |
0 |
0 |
1 |
167 |
3 |
5 |
23 |
390 |
| Backtesting Value-at-Risk: A GMM Duration-Based Test |
0 |
0 |
1 |
21 |
4 |
5 |
23 |
111 |
| Backtesting Value-at-Risk: A GMM Duration-Based-Test |
0 |
0 |
0 |
0 |
1 |
1 |
8 |
45 |
| Backtesting Value-at-Risk: A GMM Duration-based Test |
0 |
0 |
0 |
0 |
3 |
6 |
10 |
40 |
| Backtesting Value-at-Risk: A GMM Duration-based Test |
0 |
0 |
0 |
0 |
3 |
3 |
6 |
31 |
| Backtesting Value-at-Risk: A GMM Duration-based Test |
0 |
0 |
0 |
0 |
1 |
2 |
15 |
51 |
| Backtesting Value-at-Risk: From Dynamic Quantile to Dynamic Binary Tests |
0 |
0 |
1 |
259 |
4 |
5 |
18 |
623 |
| Backtesting Value-at-Risk: From Dynamic Quantile to Dynamic Binary Tests |
0 |
0 |
0 |
0 |
2 |
3 |
9 |
45 |
| Backtesting marginal expected shortfalland related systemic risk measures |
0 |
0 |
3 |
7 |
2 |
5 |
20 |
39 |
| Backtesting value-at-risk: a GMM duration-based test |
0 |
0 |
0 |
1 |
2 |
2 |
11 |
30 |
| Bactesting Var Accuracy: A New Simple Test |
0 |
0 |
0 |
0 |
1 |
3 |
8 |
35 |
| Certify reproducibility with confidential data |
0 |
0 |
0 |
0 |
0 |
0 |
7 |
14 |
| CoMargin |
1 |
1 |
1 |
160 |
3 |
3 |
18 |
466 |
| CoMargin |
0 |
0 |
0 |
0 |
1 |
1 |
13 |
18 |
| Computational Reproducibility in Finance: Evidence from 1,000 Tests |
1 |
1 |
2 |
3 |
3 |
7 |
16 |
25 |
| Computational Reproducibility in Finance: Evidence from 1,000 Tests |
0 |
0 |
0 |
0 |
5 |
7 |
17 |
18 |
| Credit Market Disequilibrium in Poland: Can We Find What We Expect? Non-Stationarity and the “Min”Condition |
0 |
0 |
0 |
155 |
1 |
3 |
16 |
499 |
| Credit Market Disequilibrium in Poland: Can we find what we expect? Non Stationarity and the Min Condition |
0 |
0 |
0 |
0 |
1 |
2 |
7 |
39 |
| Credit Market Disequilibrium in Poland: Can we find what we expect? Non stationarity and the Short Side Rule |
0 |
0 |
0 |
0 |
0 |
1 |
7 |
33 |
| Cross-country-heterogeneous and Time-varying Effects of Unconventional Monetary Policies in AEs on Portfolio Inflows to EMEs |
0 |
0 |
0 |
7 |
2 |
3 |
14 |
60 |
| Currency Crises Early Warning Systems: Why They Should Be Dynamic |
0 |
0 |
0 |
0 |
3 |
3 |
8 |
44 |
| Currency Crises Early Warning Systems: why they should be Dynamic |
0 |
0 |
0 |
34 |
2 |
3 |
13 |
110 |
| Currency Crisis Early Warning Systems: Why They should be Dynamic |
0 |
0 |
0 |
95 |
4 |
7 |
15 |
182 |
| Currency crises early warning systems: why they should be dynamic |
0 |
0 |
2 |
328 |
8 |
10 |
30 |
746 |
| Do We Need High Frequency Data to Forecast Variances? |
0 |
0 |
0 |
1 |
0 |
3 |
27 |
103 |
| Do We Need Ultra-High Frequency Data to Forecast Variances? |
0 |
0 |
0 |
35 |
2 |
4 |
19 |
142 |
| Does soft information matter for financial analysts' forecasts? A gravity model approach |
0 |
0 |
0 |
6 |
2 |
3 |
10 |
57 |
| Does the firm-analyst relationship matter in explaining analysts' earnings forecast errors? |
0 |
0 |
0 |
6 |
0 |
1 |
9 |
74 |
| Does the firm-analyst relationship matter in explaining analysts' earnings forecast errors? |
0 |
0 |
0 |
32 |
1 |
1 |
8 |
104 |
| Downgrading in the First Job: Who and Why |
0 |
0 |
0 |
0 |
2 |
2 |
7 |
27 |
| Economic Development and Energy Intensity: a Panel Data Analysis |
0 |
0 |
0 |
0 |
2 |
2 |
9 |
42 |
| Economic Development and Energy Intensity: a Panel Data Analysis |
0 |
0 |
0 |
0 |
5 |
6 |
10 |
40 |
| Energy Demand Models: A Threshold Panel Specification of the "Kuznets Curve" |
0 |
0 |
0 |
0 |
2 |
2 |
11 |
43 |
| Energy demand models: a threshold panel specification of the 'Kuznets curve' |
0 |
0 |
0 |
0 |
3 |
3 |
7 |
35 |
| Estimates of Government Net Capital Stocks for 26 Developing Countries |
0 |
0 |
0 |
0 |
2 |
3 |
13 |
48 |
| Estimates of Government Net Capital Stocks for 26 Developing Countries, 1970-2002 |
0 |
0 |
0 |
0 |
1 |
1 |
7 |
40 |
| Estimates of government net capital stocks for 26 developing countries, 1970-2002 |
0 |
0 |
0 |
216 |
1 |
2 |
13 |
472 |
| Explainable Performance |
0 |
0 |
0 |
1 |
4 |
4 |
14 |
36 |
| Explainable Performance |
0 |
1 |
3 |
15 |
3 |
4 |
17 |
27 |
| Extreme Financial Cycles |
0 |
0 |
0 |
136 |
1 |
4 |
11 |
227 |
| Financial Development and Growth: A Re-Examination using a Panel Granger Causality Test |
0 |
1 |
1 |
643 |
3 |
4 |
24 |
1,119 |
| Forecasting High-Frequency Risk Measures |
0 |
0 |
0 |
0 |
2 |
2 |
6 |
12 |
| Generalized Impulse Responses of Portfolio Default Probabilities: A Modular Framework with an Application to Geopolitical Risk |
2 |
9 |
9 |
9 |
2 |
3 |
3 |
3 |
| High-Frequency Risk Measures |
0 |
0 |
0 |
232 |
2 |
4 |
12 |
636 |
| How did the Japanese Employment System Change?Investigating the Heterogeneity of Downsizing Practices across Firms |
0 |
0 |
0 |
72 |
3 |
3 |
17 |
234 |
| How to Estimate Public Capital Productivity? |
0 |
0 |
0 |
74 |
2 |
2 |
7 |
161 |
| How to Evaluate an Early Warning System? Towards a Unified Statistical Framework for Assessing Financial Crises Forecasting Methods |
0 |
0 |
0 |
0 |
1 |
1 |
11 |
82 |
| How to evaluate an Early Warning System ? |
0 |
0 |
2 |
432 |
4 |
8 |
23 |
804 |
| How to evaluate an early warning system? Towards a united statistical framework for assessing financial crises forecasting methods |
0 |
0 |
2 |
184 |
6 |
7 |
18 |
407 |
| Implied Risk Exposures |
0 |
0 |
0 |
0 |
0 |
0 |
7 |
28 |
| Implied Risk Exposures |
0 |
0 |
0 |
179 |
2 |
2 |
6 |
387 |
| Intelligence artificielle et marché du crédit: opportunités et acceptabilité |
0 |
0 |
0 |
0 |
1 |
4 |
21 |
21 |
| Irregularly Spaced Intraday Value at Risk (ISIVaR) Models Forecasting and Predictive Abilities |
0 |
0 |
0 |
0 |
0 |
0 |
11 |
24 |
| Irregularly Spaced Intraday Value at Risk (ISIVaR) Models: Forecasting and Predictive Abilities |
0 |
0 |
0 |
73 |
1 |
1 |
10 |
210 |
| Irregularly Spaced Intraday Value at Risk (ISIVaR) Models: Forecasting and Predictive Abilities |
0 |
0 |
0 |
0 |
0 |
0 |
8 |
25 |
| Irregularly Spaced Intraday Value-at-Risk (ISIVaR) Models: Forecasting and Predictive Abilities |
0 |
0 |
0 |
0 |
0 |
1 |
10 |
51 |
| Irregularly Spaced Intraday Value-at-Risk (ISIVaR) Models: Forecasting and Predictive Abilities |
0 |
0 |
0 |
0 |
1 |
2 |
3 |
24 |
| Irregularly Spaced Intraday Value-at-Risk (ISIVaR) Models: Forecasting and Predictive Abilities |
0 |
0 |
0 |
0 |
1 |
1 |
4 |
25 |
| Irregularly Spaces Intraday Value-at-Risk (ISIVaR) Models: Forecasting and Predictive Abilities |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
19 |
| Is Public Capital Really Productive? A Methodological Reappraisal |
0 |
0 |
0 |
173 |
2 |
4 |
13 |
357 |
| Is public capital really productive? A methodological reappraisal |
0 |
0 |
0 |
0 |
1 |
1 |
10 |
39 |
| La methode d'estimation des moindres carres modifies ou fully modified |
0 |
0 |
0 |
1 |
2 |
2 |
16 |
3,770 |
| La relation firme-analyste explique-t-elle les erreurs de prévision des analystes ? |
0 |
0 |
0 |
0 |
3 |
3 |
14 |
55 |
| Loss Functions for LGD Models Comparison |
0 |
0 |
0 |
0 |
2 |
2 |
6 |
88 |
| Loss functions for LGD model comparison |
0 |
0 |
0 |
147 |
1 |
3 |
17 |
362 |
| Machine Learning and IRB Capital Requirements |
0 |
0 |
0 |
2 |
2 |
4 |
11 |
23 |
| Machine Learning and IRB Capital Requirements: Advantages, Risks, and Recommendations |
0 |
0 |
3 |
16 |
1 |
2 |
22 |
38 |
| Machine Learning and IRB Capital Requirements: Advantages, Risks, and Recommendations |
0 |
0 |
3 |
9 |
3 |
4 |
14 |
30 |
| Machine Learning et nouvelles sources de données pour le scoring de crédit |
0 |
0 |
0 |
53 |
1 |
1 |
9 |
53 |
| Machine Learning et nouvelles sources de données pour le scoring de crédit |
0 |
0 |
0 |
0 |
1 |
1 |
13 |
62 |
| Machine Learning for Credit Scoring: Improving Logistic Regression with Non Linear Decision Tree Effects |
3 |
5 |
12 |
77 |
6 |
12 |
40 |
220 |
| Machine Learning or Econometrics for Credit Scoring: Let's Get the Best of Both Worlds |
0 |
0 |
2 |
134 |
1 |
3 |
23 |
286 |
| Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds |
0 |
0 |
1 |
39 |
4 |
6 |
28 |
151 |
| Machine learning et nouvelles sources de données pour le scoring de crédit |
0 |
0 |
0 |
0 |
1 |
1 |
10 |
17 |
| Margin Backtesting |
0 |
0 |
2 |
119 |
2 |
3 |
19 |
247 |
| Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring |
0 |
0 |
2 |
11 |
2 |
5 |
22 |
45 |
| Modelling Financial Crises Mutation |
0 |
0 |
0 |
11 |
4 |
4 |
12 |
81 |
| Modèles Non Linéaires et Prévisions |
0 |
0 |
0 |
131 |
0 |
0 |
4 |
347 |
| Modèles internes des banques pour le calcul du capital réglementaire (IRB) et intelligence artificielle |
0 |
0 |
3 |
20 |
5 |
7 |
43 |
111 |
| Modèles non linéaires et prévisions |
0 |
0 |
0 |
0 |
1 |
1 |
5 |
22 |
| Modèles non linéaires et prévisions |
0 |
0 |
0 |
0 |
0 |
0 |
4 |
24 |
| Modèles à Changement de Régimes et Macro-économiques |
0 |
0 |
0 |
0 |
4 |
4 |
8 |
25 |
| Modèles à changement de régimes et macro-économiques |
0 |
0 |
0 |
0 |
2 |
3 |
9 |
26 |
| Multivariate Dynamic Probit Models: An Application to Financial Crises Mutation |
0 |
0 |
0 |
0 |
0 |
2 |
5 |
46 |
| Multivariate Dynamic Probit Models: An Application to Financial Crises Mutation |
0 |
0 |
2 |
402 |
1 |
2 |
24 |
828 |
| Network Effects and Infrastructure Productivity in Developing Countries |
0 |
0 |
0 |
7 |
5 |
6 |
11 |
55 |
| Network effects and infrastructure productivity in developing countries |
0 |
0 |
0 |
208 |
4 |
6 |
15 |
432 |
| Network effects of the productivity of infrastructure in developing countries |
0 |
1 |
1 |
942 |
2 |
7 |
23 |
1,889 |
| Networks Effects in the Productivity of Infrastructures in Developing Countries |
0 |
0 |
0 |
0 |
4 |
4 |
6 |
24 |
| Non-Standard Errors |
0 |
1 |
1 |
28 |
4 |
6 |
22 |
177 |
| Non-Standard Errors |
0 |
1 |
1 |
45 |
2 |
3 |
41 |
487 |
| Nonstandard Errors |
0 |
0 |
0 |
0 |
2 |
2 |
18 |
26 |
| Nonstandard Errors |
0 |
0 |
1 |
4 |
1 |
2 |
22 |
49 |
| Nonstandard Errors |
0 |
0 |
0 |
0 |
0 |
2 |
23 |
37 |
| Nonstandard errors |
0 |
1 |
1 |
13 |
1 |
3 |
28 |
85 |
| Pitfalls in Systemic-Risk Scoring |
0 |
0 |
0 |
0 |
2 |
6 |
14 |
82 |
| Pitfalls in systemic-risk scoring |
0 |
0 |
0 |
0 |
2 |
2 |
7 |
45 |
| Public Spending Efficiency: an Empirical Analysis for Seven Fast Growing Countries |
0 |
0 |
0 |
0 |
2 |
2 |
9 |
34 |
| Reproducibility Certification in Economics Research |
1 |
1 |
2 |
3 |
2 |
5 |
18 |
21 |
| Reproducibility of Empirical Results: Evidence from 1,000 Tests in Finance |
0 |
0 |
0 |
2 |
2 |
4 |
27 |
40 |
| Reverse Stress Testing Geopolitical Risk in Corporate Credit Portfolios: A Formal and Operational Framework |
1 |
2 |
4 |
4 |
1 |
5 |
12 |
12 |
| Revisiting Public Capital Needs: An Analysis of Growth-Maximizing Investment with Efficiency and Congestion Effects |
0 |
1 |
10 |
10 |
1 |
4 |
26 |
26 |
| Risk Measure Inference |
0 |
0 |
0 |
181 |
1 |
2 |
19 |
388 |
| Risk Measure Inference |
0 |
0 |
0 |
0 |
2 |
3 |
5 |
45 |
| RunMyCode.org: a novel dissemination and collaboration platform for executing published computational results |
0 |
0 |
1 |
86 |
3 |
3 |
14 |
387 |
| Sampling Error and Double Shrinkage Estimation of Minimum Variance Portfolios |
0 |
0 |
0 |
0 |
3 |
3 |
9 |
31 |
| Sampling error and double shrinkage estimation of minimum variance portfolios |
0 |
0 |
0 |
101 |
4 |
5 |
23 |
327 |
| Second Generation Panel Unit Root Tests |
1 |
1 |
9 |
549 |
4 |
7 |
76 |
1,709 |
| Statistique et probabilités en économie-gestion |
0 |
0 |
0 |
0 |
1 |
1 |
8 |
63 |
| Statistique et probabilités en économie-gestion (2e édition) |
0 |
0 |
0 |
0 |
3 |
5 |
12 |
33 |
| Systemic Risk Score: A Suggestion |
0 |
0 |
0 |
30 |
2 |
2 |
9 |
66 |
| Systemic Risk Score: A Suggestion |
0 |
0 |
0 |
42 |
0 |
0 |
7 |
85 |
| Systemic Risk Score: A Suggestion |
0 |
0 |
0 |
0 |
2 |
2 |
10 |
26 |
| Taux d'actualisation public, distorsions fiscales et croissance |
0 |
0 |
0 |
3 |
1 |
2 |
14 |
1,016 |
| Testing Convergence: A Panel Data Approach |
0 |
0 |
0 |
0 |
0 |
0 |
10 |
21 |
| Testing Granger Causality in Heterogeneous Panel Data Model with Fixed Coefficients |
0 |
0 |
0 |
0 |
0 |
1 |
9 |
50 |
| Testing Granger Non-Causality in Heterogeneous Panel Data Models |
0 |
0 |
0 |
0 |
1 |
1 |
12 |
53 |
| Testing Granger Non-Causality in Heterogeneous Panel Data Models with Fixed Coefficients |
0 |
0 |
0 |
0 |
2 |
2 |
9 |
102 |
| Testing Granger causality in Heterogeneous Panel Data Models with Fixed Coefficients |
0 |
0 |
0 |
0 |
0 |
1 |
4 |
53 |
| Testing Granger causality in Heterogeneous Panel Data Models with Fixed Coefficients |
0 |
0 |
0 |
0 |
0 |
0 |
5 |
60 |
| Testing Granger causality in Heterogeneous panel data models with fixed coefficients |
0 |
0 |
0 |
0 |
3 |
4 |
12 |
95 |
| Testing Interval Forecasts: A New GMM-based Test |
0 |
0 |
0 |
2 |
3 |
6 |
13 |
53 |
| Testing Interval Forecasts: a GMM-Based Approach |
0 |
0 |
0 |
0 |
0 |
0 |
8 |
30 |
| Testing for Granger Non-causality in Heterogeneous Panels |
0 |
0 |
0 |
0 |
0 |
2 |
21 |
249 |
| Testing for Granger Non-causality in Heterogeneous Panels |
1 |
2 |
12 |
1,706 |
5 |
12 |
57 |
4,178 |
| Testing interval forecasts: a GMM-based approach |
0 |
0 |
0 |
219 |
1 |
1 |
10 |
527 |
| The Collateral Risk of ETFs |
0 |
0 |
1 |
82 |
2 |
3 |
16 |
311 |
| The Counterparty Risk Exposure of ETF Investors |
0 |
0 |
0 |
63 |
1 |
3 |
17 |
193 |
| The Economics of Computational Reproducibility |
0 |
0 |
0 |
0 |
1 |
3 |
10 |
15 |
| The Economics of Computational Reproducibility |
0 |
0 |
1 |
15 |
3 |
5 |
14 |
22 |
| The Economics of Research Reproducibility |
0 |
0 |
0 |
2 |
2 |
4 |
22 |
57 |
| The Fairness of Credit Scoring Models |
0 |
0 |
2 |
5 |
3 |
4 |
34 |
53 |
| The Fairness of Credit Scoring Models |
0 |
0 |
0 |
0 |
6 |
8 |
26 |
29 |
| The Fairness of Credit Scoring Models |
0 |
1 |
2 |
7 |
2 |
4 |
38 |
61 |
| The Fairness of Credit Scoring Models |
0 |
0 |
1 |
39 |
3 |
3 |
27 |
83 |
| The Fairness of Credit Scoring Models |
0 |
0 |
0 |
0 |
11 |
13 |
30 |
65 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
0 |
0 |
0 |
1 |
12 |
51 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
0 |
0 |
2 |
2 |
7 |
77 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
0 |
0 |
0 |
0 |
6 |
53 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
0 |
0 |
0 |
1 |
6 |
42 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
0 |
0 |
2 |
3 |
12 |
48 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
0 |
0 |
0 |
0 |
20 |
50 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
0 |
0 |
0 |
0 |
10 |
49 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
0 |
0 |
2 |
2 |
11 |
47 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
1 |
1 |
1 |
26 |
4 |
4 |
16 |
176 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
0 |
0 |
0 |
53 |
60 |
140 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
0 |
0 |
0 |
0 |
9 |
43 |
| The Feldstein-Horioka Puzzle: a Panel SmoothTransition Regression Approach |
1 |
1 |
2 |
550 |
2 |
2 |
14 |
1,141 |
| The Heterogeneity of Employment Adjustment Accross Japanese Firms. A study Using Panel Data |
0 |
0 |
0 |
0 |
2 |
2 |
7 |
26 |
| The Risk Map: A New Tool for Validating Risk Models |
0 |
0 |
1 |
432 |
3 |
5 |
14 |
669 |
| The Safe-Tail Paradox: Stress Testing AI Exposure of Banks Borrowers |
0 |
1 |
3 |
3 |
4 |
9 |
11 |
11 |
| The Safe-Tail Paradox: Stress Testing AI Exposure of Banks Borrowers |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
2 |
| The at-Risk approach: a new tool for stress tests and overlays |
0 |
0 |
0 |
0 |
1 |
1 |
5 |
5 |
| The counterparty risk exposure of ETF investors |
0 |
0 |
0 |
0 |
3 |
3 |
7 |
11 |
| The heterogeneity of employment adjustment across Japanese firms. A study using panel data |
0 |
0 |
0 |
103 |
0 |
0 |
7 |
420 |
| The productivy Effects of Public Capital in Developing Countries |
0 |
0 |
0 |
0 |
0 |
3 |
10 |
43 |
| Threshold Effects in the Public Capital Productivity: An International Panel Smooth Transition Approach |
0 |
0 |
2 |
81 |
0 |
0 |
5 |
280 |
| Threshold Effects in the Public Capital Productivity: An International Panel Smooth Transition Approach |
0 |
0 |
0 |
0 |
1 |
1 |
15 |
66 |
| Threshold Effects in the Public Capital Productivity: An International Panel Smooth Transition Approach |
0 |
0 |
0 |
0 |
0 |
0 |
4 |
41 |
| Threshold Effects in the Public Capital Productivity: an International Panel Smooth Transition Approach |
0 |
0 |
0 |
0 |
1 |
2 |
9 |
39 |
| Threshold Effects in the Public Capital Productivity: an International Panel Smooth Transition Approach |
0 |
0 |
0 |
0 |
0 |
0 |
8 |
48 |
| Threshold Effects of the Public Capital Productivity: An International Panel Smooth Transition Approach |
0 |
0 |
3 |
751 |
1 |
3 |
37 |
2,029 |
| Threshold Effects of the Public Capital Productivity: An International Panel Smooth Transition Approach |
1 |
1 |
2 |
60 |
2 |
5 |
56 |
278 |
| Threshold Effects of the Public Capital Productivity: an International Panel Smooth Transition Approach |
0 |
0 |
0 |
0 |
1 |
1 |
7 |
41 |
| Threshold Effects of the Public Capital Productivity: an International Panel Smooth Transition Approach |
0 |
0 |
0 |
0 |
1 |
3 |
13 |
84 |
| Time Series for QFFE: Special Issue of the Journal of Time Series Analysis |
0 |
0 |
0 |
0 |
0 |
1 |
4 |
4 |
| Un MEDAF à plusieurs moments réalisés |
0 |
0 |
0 |
29 |
0 |
0 |
7 |
165 |
| Un MEDAF à plusieurs moments réalisés |
0 |
0 |
0 |
2 |
3 |
3 |
10 |
16 |
| Un MEDAF à plusieurs moments réalisés |
0 |
0 |
0 |
34 |
2 |
2 |
11 |
174 |
| Un Test Simple de l'Hypothèse de Non Causalité dans un Modèle de Panel Hétérogène |
0 |
0 |
0 |
0 |
1 |
2 |
7 |
38 |
| Un Test de Validité de la Value-at-Risk |
0 |
0 |
0 |
0 |
3 |
3 |
9 |
42 |
| Un test de Validité de la Value-at-risk |
0 |
0 |
0 |
0 |
1 |
1 |
7 |
29 |
| Un test simple de l'hypothèse de non causalité dans un modèle de panel hétérogène |
0 |
0 |
0 |
0 |
0 |
1 |
9 |
24 |
| Une Evaluation des Procédures de Backtesting |
0 |
0 |
2 |
180 |
1 |
3 |
14 |
494 |
| Une Synthèse des Tests de Cointégration sur Données de Panel |
0 |
0 |
0 |
0 |
1 |
2 |
9 |
70 |
| Une Synthèse des Tests de Racine Unitaire en sur Données de Panel |
0 |
0 |
0 |
0 |
2 |
4 |
14 |
63 |
| Une Synthèse des Tests de Racine Unitaire sur Données de Panel |
0 |
0 |
0 |
484 |
3 |
4 |
16 |
1,180 |
| Une synthèse des tests de co-intégration sur données de panel |
0 |
0 |
3 |
38 |
1 |
1 |
17 |
261 |
| Une synthèse des tests de cointégration sur données de panel |
0 |
0 |
0 |
269 |
2 |
3 |
19 |
829 |
| Une évaluation des procédures de Backtesting |
0 |
0 |
0 |
7 |
1 |
2 |
12 |
61 |
| Une évaluation des procédures de Backtesting: Tout va pour le mieux dans le meilleur des mondes |
0 |
0 |
0 |
0 |
0 |
0 |
6 |
28 |
| Une évaluation des procédures de Backtesting: Tout va pour le mieux dans le meilleur des mondes |
0 |
0 |
0 |
0 |
0 |
0 |
27 |
55 |
| Une évaluation des procédures de Backtesting: Tout va pour le mieux dans le meilleur des mondes |
0 |
0 |
0 |
0 |
3 |
3 |
10 |
26 |
| What would Nelson and Plosser find had they used panel unit root tests? |
0 |
0 |
0 |
0 |
1 |
1 |
10 |
11 |
| What would Nelson and Plosser find had they used panel unit root tests? |
0 |
1 |
1 |
183 |
3 |
6 |
12 |
418 |
| Where the Risks Lie: A Survey on Systemic Risk |
0 |
0 |
0 |
5 |
2 |
6 |
32 |
334 |
| Where the Risks Lie: A Survey on Systemic Risk |
0 |
0 |
1 |
120 |
3 |
7 |
17 |
388 |
| Where the Risks Lie: A Survey on Systemic Risk |
0 |
0 |
0 |
0 |
2 |
5 |
22 |
236 |
| Why don't banks lend to Egypt's private sector ? |
0 |
0 |
0 |
109 |
0 |
0 |
12 |
254 |
| ZICO: A Credit Scoring Approach to Detecting Zombie Papers |
1 |
12 |
15 |
15 |
3 |
16 |
25 |
25 |
| Total Working Papers |
15 |
47 |
150 |
13,525 |
393 |
694 |
3,064 |
45,000 |