| 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 |
0 |
6 |
9 |
27 |
| A Comment on The Dynamic Macroeconomic Effects of Public Capital |
0 |
0 |
0 |
0 |
0 |
2 |
3 |
14 |
| A DARE for VaR |
0 |
0 |
0 |
0 |
0 |
2 |
4 |
12 |
| A Theoretical and Empirical Assessment of the Bank Lending Channel and Loan Market Disequilibrium in Poland |
0 |
0 |
0 |
118 |
3 |
7 |
10 |
429 |
| A Theoretical and Empirical Comparison of Systemic Risk Measures |
0 |
0 |
0 |
0 |
0 |
8 |
16 |
172 |
| A Theoretical and Empirical Comparison of Systemic Risk Measures |
0 |
1 |
2 |
265 |
0 |
5 |
11 |
649 |
| A Theoretical and Empirical Comparison of Systemic Risk Measures: MES versus CoVaR |
0 |
0 |
0 |
7 |
2 |
8 |
12 |
124 |
| Backtesting Expected Shortfall: Accounting for both duration and severity with bivariate orthogonal polynomials |
0 |
1 |
2 |
9 |
5 |
7 |
16 |
34 |
| Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures |
0 |
0 |
0 |
0 |
2 |
10 |
14 |
59 |
| Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures |
0 |
0 |
0 |
0 |
0 |
4 |
5 |
20 |
| Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures |
0 |
0 |
1 |
55 |
2 |
6 |
12 |
97 |
| Backtesting VaR Accuracy: A New Simple Test |
0 |
0 |
0 |
221 |
0 |
7 |
9 |
629 |
| Backtesting VaR Accuracy: A Simple and Powerful Test |
0 |
0 |
0 |
17 |
0 |
2 |
9 |
62 |
| Backtesting Value at Risk Accuracy: A New Simple Test |
0 |
0 |
0 |
0 |
0 |
2 |
5 |
29 |
| Backtesting Value at Risk Accuracy: A New Simple Test |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
20 |
| 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 |
0 |
0 |
2 |
28 |
| Backtesting Value at Risk Accuracy: A New Simple Test |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
15 |
| Backtesting Value at Risk Accuracy: A New Simple Test |
0 |
0 |
0 |
0 |
0 |
5 |
5 |
31 |
| Backtesting Value-at-Risk Accuracy: A New Simple Test |
0 |
0 |
0 |
0 |
0 |
1 |
6 |
31 |
| Backtesting Value-at-Risk Accuracy: A New Simple Test |
0 |
0 |
0 |
0 |
2 |
3 |
4 |
22 |
| Backtesting Value-at-Risk: A GMM Duration-Based Test |
0 |
0 |
1 |
25 |
0 |
3 |
10 |
128 |
| Backtesting Value-at-Risk: A GMM Duration-Based Test |
0 |
0 |
0 |
0 |
0 |
5 |
6 |
36 |
| Backtesting Value-at-Risk: A GMM Duration-Based Test |
0 |
0 |
2 |
167 |
0 |
13 |
20 |
381 |
| Backtesting Value-at-Risk: A GMM Duration-Based Test |
0 |
0 |
0 |
0 |
0 |
3 |
8 |
42 |
| Backtesting Value-at-Risk: A GMM Duration-Based Test |
0 |
1 |
2 |
21 |
1 |
6 |
12 |
99 |
| Backtesting Value-at-Risk: A GMM Duration-Based-Test |
0 |
0 |
0 |
0 |
0 |
3 |
5 |
40 |
| Backtesting Value-at-Risk: A GMM Duration-based Test |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
26 |
| Backtesting Value-at-Risk: A GMM Duration-based Test |
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0 |
0 |
0 |
1 |
3 |
4 |
33 |
| Backtesting Value-at-Risk: A GMM Duration-based Test |
0 |
0 |
0 |
0 |
0 |
4 |
8 |
44 |
| Backtesting Value-at-Risk: From Dynamic Quantile to Dynamic Binary Tests |
0 |
0 |
0 |
0 |
0 |
1 |
6 |
38 |
| Backtesting Value-at-Risk: From Dynamic Quantile to Dynamic Binary Tests |
0 |
0 |
0 |
258 |
0 |
3 |
10 |
612 |
| Backtesting marginal expected shortfalland related systemic risk measures |
1 |
1 |
2 |
5 |
1 |
3 |
13 |
24 |
| Backtesting value-at-risk: a GMM duration-based test |
0 |
0 |
0 |
1 |
2 |
6 |
9 |
25 |
| Bactesting Var Accuracy: A New Simple Test |
0 |
0 |
0 |
0 |
0 |
3 |
4 |
30 |
| Certify reproducibility with confidential data |
0 |
0 |
0 |
0 |
0 |
5 |
9 |
14 |
| CoMargin |
0 |
0 |
0 |
0 |
2 |
8 |
12 |
16 |
| CoMargin |
0 |
0 |
0 |
159 |
0 |
9 |
11 |
458 |
| Computational Reproducibility in Finance: Evidence from 1,000 Tests |
1 |
1 |
1 |
2 |
1 |
5 |
12 |
15 |
| Computational Reproducibility in Finance: Evidence from 1,000 Tests |
0 |
0 |
0 |
0 |
1 |
3 |
7 |
7 |
| Credit Market Disequilibrium in Poland: Can We Find What We Expect? Non-Stationarity and the “Min”Condition |
0 |
0 |
0 |
155 |
0 |
5 |
13 |
492 |
| Credit Market Disequilibrium in Poland: Can we find what we expect? Non Stationarity and the Min Condition |
0 |
0 |
0 |
0 |
0 |
4 |
5 |
37 |
| 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 |
29 |
| Cross-country-heterogeneous and Time-varying Effects of Unconventional Monetary Policies in AEs on Portfolio Inflows to EMEs |
0 |
0 |
3 |
7 |
0 |
6 |
13 |
53 |
| Currency Crises Early Warning Systems: Why They Should Be Dynamic |
0 |
0 |
0 |
0 |
0 |
5 |
6 |
41 |
| Currency Crises Early Warning Systems: why they should be Dynamic |
0 |
0 |
1 |
34 |
1 |
3 |
9 |
103 |
| Currency Crisis Early Warning Systems: Why They should be Dynamic |
0 |
0 |
3 |
95 |
0 |
7 |
13 |
174 |
| Currency crises early warning systems: why they should be dynamic |
0 |
1 |
5 |
328 |
3 |
8 |
16 |
728 |
| Do We Need High Frequency Data to Forecast Variances? |
0 |
0 |
0 |
1 |
0 |
17 |
19 |
94 |
| Do We Need Ultra-High Frequency Data to Forecast Variances? |
0 |
0 |
0 |
35 |
1 |
6 |
9 |
130 |
| Does soft information matter for financial analysts' forecasts? A gravity model approach |
0 |
0 |
0 |
6 |
0 |
1 |
4 |
48 |
| Does the firm-analyst relationship matter in explaining analysts' earnings forecast errors? |
0 |
0 |
0 |
32 |
0 |
4 |
7 |
100 |
| Does the firm-analyst relationship matter in explaining analysts' earnings forecast errors? |
0 |
0 |
0 |
6 |
0 |
4 |
7 |
71 |
| Downgrading in the First Job: Who and Why |
0 |
0 |
0 |
0 |
2 |
4 |
4 |
24 |
| Economic Development and Energy Intensity: a Panel Data Analysis |
0 |
0 |
0 |
0 |
1 |
3 |
3 |
36 |
| Economic Development and Energy Intensity: a Panel Data Analysis |
0 |
0 |
0 |
0 |
0 |
1 |
4 |
32 |
| Energy Demand Models: A Threshold Panel Specification of the "Kuznets Curve" |
0 |
0 |
0 |
0 |
0 |
6 |
9 |
39 |
| Energy demand models: a threshold panel specification of the 'Kuznets curve' |
0 |
0 |
0 |
0 |
0 |
3 |
3 |
31 |
| Estimates of Government Net Capital Stocks for 26 Developing Countries |
0 |
0 |
0 |
0 |
1 |
6 |
8 |
41 |
| Estimates of Government Net Capital Stocks for 26 Developing Countries, 1970-2002 |
0 |
0 |
0 |
0 |
0 |
4 |
7 |
38 |
| Estimates of government net capital stocks for 26 developing countries, 1970-2002 |
0 |
0 |
1 |
216 |
0 |
5 |
10 |
468 |
| Explainable Performance |
1 |
1 |
1 |
13 |
2 |
5 |
11 |
16 |
| Explainable Performance |
0 |
0 |
0 |
1 |
0 |
6 |
8 |
30 |
| Extreme Financial Cycles |
0 |
0 |
0 |
136 |
0 |
2 |
3 |
218 |
| Financial Development and Growth: A Re-Examination using a Panel Granger Causality Test |
0 |
0 |
1 |
642 |
2 |
15 |
19 |
1,110 |
| Forecasting High-Frequency Risk Measures |
0 |
0 |
0 |
0 |
0 |
2 |
6 |
8 |
| High-Frequency Risk Measures |
0 |
0 |
0 |
232 |
1 |
5 |
10 |
630 |
| How did the Japanese Employment System Change?Investigating the Heterogeneity of Downsizing Practices across Firms |
0 |
0 |
1 |
72 |
0 |
7 |
12 |
226 |
| How to Estimate Public Capital Productivity? |
0 |
0 |
0 |
74 |
0 |
1 |
2 |
156 |
| How to Evaluate an Early Warning System? Towards a Unified Statistical Framework for Assessing Financial Crises Forecasting Methods |
0 |
0 |
0 |
0 |
1 |
4 |
10 |
76 |
| How to evaluate an Early Warning System ? |
0 |
1 |
2 |
431 |
2 |
9 |
14 |
791 |
| How to evaluate an early warning system? Towards a united statistical framework for assessing financial crises forecasting methods |
0 |
0 |
1 |
183 |
0 |
5 |
12 |
396 |
| Implied Risk Exposures |
0 |
0 |
0 |
179 |
0 |
3 |
6 |
384 |
| Implied Risk Exposures |
0 |
0 |
0 |
0 |
1 |
4 |
8 |
26 |
| Irregularly Spaced Intraday Value at Risk (ISIVaR) Models Forecasting and Predictive Abilities |
0 |
0 |
0 |
0 |
2 |
3 |
4 |
17 |
| Irregularly Spaced Intraday Value at Risk (ISIVaR) Models: Forecasting and Predictive Abilities |
0 |
0 |
0 |
73 |
2 |
4 |
5 |
204 |
| Irregularly Spaced Intraday Value at Risk (ISIVaR) Models: Forecasting and Predictive Abilities |
0 |
0 |
0 |
0 |
0 |
3 |
4 |
21 |
| Irregularly Spaced Intraday Value-at-Risk (ISIVaR) Models: Forecasting and Predictive Abilities |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
22 |
| Irregularly Spaced Intraday Value-at-Risk (ISIVaR) Models: Forecasting and Predictive Abilities |
0 |
0 |
0 |
0 |
1 |
3 |
6 |
45 |
| Irregularly Spaced Intraday Value-at-Risk (ISIVaR) Models: Forecasting and Predictive Abilities |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
22 |
| Irregularly Spaces Intraday Value-at-Risk (ISIVaR) Models: Forecasting and Predictive Abilities |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
16 |
| Is Public Capital Really Productive? A Methodological Reappraisal |
0 |
0 |
1 |
173 |
1 |
3 |
6 |
347 |
| Is public capital really productive? A methodological reappraisal |
0 |
0 |
0 |
0 |
1 |
5 |
8 |
34 |
| La methode d'estimation des moindres carres modifies ou fully modified |
0 |
0 |
0 |
1 |
1 |
4 |
11 |
3,759 |
| La relation firme-analyste explique-t-elle les erreurs de prévision des analystes ? |
0 |
0 |
0 |
0 |
0 |
4 |
6 |
45 |
| Loss Functions for LGD Models Comparison |
0 |
0 |
0 |
0 |
0 |
2 |
5 |
84 |
| Loss functions for LGD model comparison |
0 |
0 |
0 |
147 |
1 |
3 |
10 |
352 |
| Machine Learning and IRB Capital Requirements |
0 |
0 |
0 |
2 |
0 |
3 |
5 |
15 |
| Machine Learning and IRB Capital Requirements: Advantages, Risks, and Recommendations |
1 |
2 |
2 |
15 |
2 |
10 |
18 |
28 |
| Machine Learning and IRB Capital Requirements: Advantages, Risks, and Recommendations |
1 |
1 |
4 |
8 |
1 |
1 |
11 |
20 |
| Machine Learning et nouvelles sources de données pour le scoring de crédit |
0 |
0 |
2 |
53 |
3 |
6 |
11 |
50 |
| Machine Learning et nouvelles sources de données pour le scoring de crédit |
0 |
0 |
0 |
0 |
2 |
7 |
16 |
58 |
| Machine Learning for Credit Scoring: Improving Logistic Regression with Non Linear Decision Tree Effects |
0 |
2 |
3 |
68 |
5 |
13 |
32 |
195 |
| Machine Learning or Econometrics for Credit Scoring: Let's Get the Best of Both Worlds |
0 |
2 |
4 |
134 |
1 |
11 |
26 |
276 |
| Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds |
0 |
0 |
1 |
38 |
0 |
9 |
29 |
135 |
| Machine learning et nouvelles sources de données pour le scoring de crédit |
0 |
0 |
0 |
0 |
0 |
7 |
9 |
14 |
| Margin Backtesting |
0 |
0 |
1 |
117 |
1 |
5 |
13 |
234 |
| Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring |
0 |
1 |
1 |
10 |
3 |
7 |
15 |
30 |
| Modelling Financial Crises Mutation |
0 |
0 |
0 |
11 |
1 |
3 |
6 |
74 |
| Modèles Non Linéaires et Prévisions |
0 |
0 |
0 |
131 |
0 |
2 |
2 |
345 |
| Modèles internes des banques pour le calcul du capital réglementaire (IRB) et intelligence artificielle |
0 |
1 |
8 |
19 |
1 |
5 |
27 |
77 |
| Modèles non linéaires et prévisions |
0 |
0 |
0 |
0 |
0 |
2 |
4 |
22 |
| Modèles non linéaires et prévisions |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
19 |
| Modèles à Changement de Régimes et Macro-économiques |
0 |
0 |
0 |
0 |
1 |
3 |
4 |
20 |
| Modèles à changement de régimes et macro-économiques |
0 |
0 |
0 |
0 |
0 |
3 |
4 |
20 |
| Multivariate Dynamic Probit Models: An Application to Financial Crises Mutation |
0 |
2 |
4 |
402 |
2 |
11 |
18 |
817 |
| Multivariate Dynamic Probit Models: An Application to Financial Crises Mutation |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
43 |
| Network Effects and Infrastructure Productivity in Developing Countries |
0 |
0 |
0 |
7 |
0 |
4 |
6 |
48 |
| Network effects and infrastructure productivity in developing countries |
0 |
0 |
0 |
208 |
2 |
5 |
5 |
422 |
| Network effects of the productivity of infrastructure in developing countries |
0 |
0 |
4 |
941 |
2 |
11 |
20 |
1,878 |
| Networks Effects in the Productivity of Infrastructures in Developing Countries |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
19 |
| Non-Standard Errors |
0 |
0 |
2 |
44 |
4 |
18 |
38 |
470 |
| Non-Standard Errors |
0 |
0 |
0 |
27 |
0 |
6 |
20 |
163 |
| Nonstandard Errors |
0 |
1 |
2 |
4 |
1 |
10 |
25 |
39 |
| Nonstandard Errors |
0 |
0 |
0 |
0 |
0 |
5 |
23 |
23 |
| Nonstandard Errors |
0 |
0 |
0 |
0 |
1 |
8 |
16 |
16 |
| Nonstandard errors |
0 |
0 |
1 |
12 |
2 |
11 |
28 |
71 |
| Pitfalls in Systemic-Risk Scoring |
0 |
0 |
0 |
0 |
2 |
6 |
7 |
74 |
| Pitfalls in systemic-risk scoring |
0 |
0 |
0 |
0 |
1 |
3 |
6 |
42 |
| Public Spending Efficiency: an Empirical Analysis for Seven Fast Growing Countries |
0 |
0 |
0 |
0 |
2 |
5 |
5 |
30 |
| Reproducibility Certification in Economics Research |
0 |
0 |
0 |
2 |
4 |
11 |
17 |
47 |
| Reproducibility Certification in Economics Research |
0 |
0 |
0 |
1 |
1 |
4 |
9 |
10 |
| Reproducibility of Empirical Results: Evidence from 1,000 Tests in Finance |
0 |
0 |
0 |
2 |
1 |
12 |
19 |
27 |
| Revisiting Public Capital Needs: An Analysis of Growth-Maximizing Investment with Efficiency and Congestion Effects |
1 |
7 |
7 |
7 |
3 |
16 |
16 |
16 |
| Risk Measure Inference |
0 |
0 |
0 |
0 |
1 |
2 |
4 |
42 |
| Risk Measure Inference |
0 |
0 |
0 |
181 |
2 |
10 |
11 |
380 |
| RunMyCode.org: a novel dissemination and collaboration platform for executing published computational results |
0 |
0 |
4 |
85 |
0 |
4 |
30 |
379 |
| Sampling Error and Double Shrinkage Estimation of Minimum Variance Portfolios |
0 |
0 |
0 |
0 |
0 |
3 |
4 |
25 |
| Sampling error and double shrinkage estimation of minimum variance portfolios |
0 |
0 |
0 |
101 |
3 |
11 |
14 |
318 |
| Second Generation Panel Unit Root Tests |
2 |
3 |
20 |
545 |
4 |
30 |
103 |
1,675 |
| Statistique et probabilités en économie-gestion |
0 |
0 |
0 |
0 |
0 |
2 |
6 |
59 |
| Statistique et probabilités en économie-gestion (2e édition) |
0 |
0 |
0 |
0 |
1 |
5 |
9 |
26 |
| Systemic Risk Score: A Suggestion |
0 |
0 |
0 |
42 |
0 |
6 |
7 |
84 |
| Systemic Risk Score: A Suggestion |
0 |
0 |
0 |
0 |
2 |
3 |
5 |
20 |
| Systemic Risk Score: A Suggestion |
0 |
0 |
0 |
30 |
2 |
6 |
6 |
63 |
| Taux d'actualisation public, distorsions fiscales et croissance |
0 |
0 |
1 |
3 |
1 |
3 |
7 |
1,008 |
| Testing Convergence: A Panel Data Approach |
0 |
0 |
0 |
0 |
1 |
4 |
8 |
16 |
| Testing Granger Causality in Heterogeneous Panel Data Model with Fixed Coefficients |
0 |
0 |
0 |
0 |
0 |
2 |
7 |
46 |
| Testing Granger Non-Causality in Heterogeneous Panel Data Models |
0 |
0 |
0 |
0 |
1 |
6 |
9 |
49 |
| Testing Granger Non-Causality in Heterogeneous Panel Data Models with Fixed Coefficients |
0 |
0 |
0 |
0 |
0 |
4 |
11 |
99 |
| Testing Granger causality in Heterogeneous Panel Data Models with Fixed Coefficients |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
57 |
| Testing Granger causality in Heterogeneous Panel Data Models with Fixed Coefficients |
0 |
0 |
0 |
0 |
0 |
1 |
4 |
50 |
| Testing Granger causality in Heterogeneous panel data models with fixed coefficients |
0 |
0 |
0 |
0 |
0 |
5 |
13 |
90 |
| Testing Interval Forecasts: A New GMM-based Test |
0 |
0 |
0 |
2 |
2 |
4 |
5 |
45 |
| Testing Interval Forecasts: a GMM-Based Approach |
0 |
0 |
0 |
0 |
1 |
5 |
8 |
27 |
| Testing for Granger Non-causality in Heterogeneous Panels |
0 |
0 |
0 |
0 |
5 |
11 |
19 |
241 |
| Testing for Granger Non-causality in Heterogeneous Panels |
0 |
2 |
14 |
1,697 |
4 |
17 |
52 |
4,145 |
| Testing interval forecasts: a GMM-based approach |
0 |
0 |
0 |
219 |
0 |
4 |
9 |
522 |
| The Collateral Risk of ETFs |
0 |
0 |
2 |
82 |
0 |
8 |
14 |
305 |
| The Counterparty Risk Exposure of ETF Investors |
0 |
0 |
0 |
63 |
2 |
7 |
10 |
185 |
| The Economics of Computational Reproducibility |
0 |
0 |
0 |
0 |
0 |
4 |
6 |
9 |
| The Economics of Computational Reproducibility |
0 |
0 |
0 |
14 |
0 |
4 |
5 |
12 |
| The Fairness of Credit Scoring Models |
0 |
0 |
0 |
0 |
2 |
10 |
15 |
15 |
| The Fairness of Credit Scoring Models |
0 |
0 |
0 |
0 |
0 |
8 |
15 |
46 |
| The Fairness of Credit Scoring Models |
1 |
1 |
2 |
39 |
3 |
14 |
25 |
71 |
| The Fairness of Credit Scoring Models |
1 |
1 |
3 |
4 |
5 |
11 |
31 |
32 |
| The Fairness of Credit Scoring Models |
0 |
1 |
1 |
6 |
10 |
20 |
34 |
48 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
0 |
0 |
0 |
4 |
10 |
43 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
0 |
0 |
1 |
5 |
8 |
42 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
0 |
0 |
1 |
10 |
13 |
43 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
0 |
0 |
0 |
3 |
5 |
44 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
0 |
0 |
1 |
4 |
10 |
41 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
0 |
0 |
0 |
3 |
5 |
50 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
0 |
0 |
0 |
4 |
10 |
46 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
0 |
0 |
0 |
4 |
6 |
85 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
0 |
0 |
0 |
2 |
5 |
73 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
1 |
25 |
0 |
6 |
11 |
169 |
| The Feldstein-Horioka Puzzle: a Panel Smooth Transition Regression Approach |
0 |
0 |
0 |
0 |
0 |
3 |
8 |
40 |
| The Feldstein-Horioka Puzzle: a Panel SmoothTransition Regression Approach |
0 |
1 |
2 |
549 |
0 |
8 |
17 |
1,137 |
| The Heterogeneity of Employment Adjustment Accross Japanese Firms. A study Using Panel Data |
0 |
0 |
0 |
0 |
0 |
3 |
5 |
23 |
| The Risk Map: A New Tool for Validating Risk Models |
0 |
0 |
1 |
432 |
0 |
3 |
8 |
660 |
| The at-Risk approach: a new tool for stress tests and overlays |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| The counterparty risk exposure of ETF investors |
0 |
0 |
0 |
0 |
0 |
2 |
4 |
7 |
| The heterogeneity of employment adjustment across Japanese firms. A study using panel data |
0 |
0 |
0 |
103 |
0 |
3 |
5 |
416 |
| The productivy Effects of Public Capital in Developing Countries |
0 |
0 |
0 |
0 |
0 |
3 |
4 |
37 |
| Threshold Effects in the Public Capital Productivity: An International Panel Smooth Transition Approach |
0 |
0 |
0 |
0 |
1 |
1 |
4 |
40 |
| Threshold Effects in the Public Capital Productivity: An International Panel Smooth Transition Approach |
0 |
0 |
0 |
0 |
4 |
6 |
12 |
61 |
| Threshold Effects in the Public Capital Productivity: An International Panel Smooth Transition Approach |
0 |
1 |
4 |
81 |
1 |
2 |
8 |
278 |
| Threshold Effects in the Public Capital Productivity: an International Panel Smooth Transition Approach |
0 |
0 |
0 |
0 |
0 |
5 |
6 |
35 |
| Threshold Effects in the Public Capital Productivity: an International Panel Smooth Transition Approach |
0 |
0 |
0 |
0 |
3 |
5 |
7 |
46 |
| Threshold Effects of the Public Capital Productivity: An International Panel Smooth Transition Approach |
0 |
1 |
2 |
59 |
27 |
40 |
48 |
264 |
| Threshold Effects of the Public Capital Productivity: An International Panel Smooth Transition Approach |
0 |
1 |
4 |
750 |
6 |
17 |
55 |
2,016 |
| Threshold Effects of the Public Capital Productivity: an International Panel Smooth Transition Approach |
0 |
0 |
0 |
0 |
1 |
8 |
9 |
79 |
| Threshold Effects of the Public Capital Productivity: an International Panel Smooth Transition Approach |
0 |
0 |
0 |
0 |
1 |
3 |
3 |
37 |
| Un MEDAF à plusieurs moments réalisés |
0 |
0 |
1 |
34 |
2 |
6 |
7 |
169 |
| Un MEDAF à plusieurs moments réalisés |
0 |
0 |
0 |
29 |
0 |
5 |
6 |
163 |
| Un MEDAF à plusieurs moments réalisés |
0 |
0 |
0 |
2 |
0 |
4 |
4 |
10 |
| Un Test Simple de l'Hypothèse de Non Causalité dans un Modèle de Panel Hétérogène |
0 |
0 |
0 |
0 |
0 |
0 |
5 |
33 |
| Un Test de Validité de la Value-at-Risk |
0 |
0 |
0 |
0 |
0 |
3 |
5 |
37 |
| Un test de Validité de la Value-at-risk |
0 |
0 |
0 |
0 |
0 |
4 |
5 |
26 |
| Un test simple de l'hypothèse de non causalité dans un modèle de panel hétérogène |
0 |
0 |
0 |
0 |
1 |
7 |
9 |
22 |
| Une Evaluation des Procédures de Backtesting |
0 |
0 |
1 |
179 |
0 |
4 |
14 |
486 |
| Une Synthèse des Tests de Cointégration sur Données de Panel |
0 |
0 |
0 |
0 |
0 |
2 |
9 |
66 |
| Une Synthèse des Tests de Racine Unitaire en sur Données de Panel |
0 |
0 |
0 |
0 |
2 |
4 |
9 |
53 |
| Une Synthèse des Tests de Racine Unitaire sur Données de Panel |
0 |
0 |
0 |
484 |
1 |
4 |
8 |
1,169 |
| Une synthèse des tests de co-intégration sur données de panel |
0 |
1 |
4 |
36 |
2 |
12 |
30 |
256 |
| Une synthèse des tests de cointégration sur données de panel |
0 |
0 |
0 |
269 |
0 |
6 |
11 |
816 |
| Une évaluation des procédures de Backtesting |
0 |
0 |
0 |
7 |
1 |
6 |
9 |
55 |
| Une évaluation des procédures de Backtesting: Tout va pour le mieux dans le meilleur des mondes |
0 |
0 |
0 |
0 |
2 |
6 |
8 |
22 |
| Une évaluation des procédures de Backtesting: Tout va pour le mieux dans le meilleur des mondes |
0 |
0 |
0 |
0 |
0 |
5 |
5 |
33 |
| Une évaluation des procédures de Backtesting: Tout va pour le mieux dans le meilleur des mondes |
0 |
0 |
0 |
0 |
0 |
3 |
3 |
25 |
| What would Nelson and Plosser find had they used panel unit root tests? |
0 |
0 |
6 |
182 |
0 |
3 |
18 |
411 |
| What would Nelson and Plosser find had they used panel unit root tests? |
0 |
0 |
0 |
0 |
2 |
7 |
9 |
10 |
| Where the Risks Lie: A Survey on Systemic Risk |
0 |
0 |
1 |
120 |
2 |
6 |
13 |
380 |
| Where the Risks Lie: A Survey on Systemic Risk |
0 |
0 |
0 |
5 |
6 |
14 |
26 |
318 |
| Where the Risks Lie: A Survey on Systemic Risk |
0 |
0 |
0 |
0 |
1 |
10 |
14 |
224 |
| Why don't banks lend to Egypt's private sector ? |
0 |
0 |
0 |
109 |
0 |
5 |
7 |
247 |
| Total Working Papers |
10 |
40 |
153 |
13,433 |
223 |
1,142 |
2,233 |
43,352 |