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Abstract Views |
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12 months |
Total |
Last month |
3 months |
12 months |
Total |
| A 1-1 poly-t random variable generator with application to Monte Carlo integration |
0 |
0 |
0 |
6 |
0 |
0 |
0 |
16 |
| A Bayesian method of change-point estimation with recurrent regimes: application to GARCH models |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
| A Comparison Of Forecasting Procedures For Macroeconomic Series: The Contribution Of Structural Break Models |
0 |
0 |
0 |
52 |
0 |
1 |
2 |
82 |
| A Comparison of Financial Duration Models via Density Forecasts |
0 |
0 |
0 |
362 |
0 |
1 |
4 |
809 |
| A Comparison of Forecasting Procedures For Macroeconomic Series: The Contribution of Structural Break Models |
0 |
0 |
0 |
178 |
0 |
1 |
2 |
218 |
| A Comparison of Forecasting Procedures for Macroeconomic Series: the Contribution of Structural Break Models |
0 |
0 |
0 |
83 |
0 |
0 |
2 |
147 |
| A Component GARCH Model with Time Varying Weights |
0 |
0 |
1 |
196 |
0 |
0 |
2 |
414 |
| A Dynamic Component Model for Forecasting High-Dimensional Realized Covariances Matrices |
0 |
0 |
0 |
6 |
0 |
1 |
3 |
23 |
| A Gibbs sampling approach to cointegration |
1 |
1 |
1 |
36 |
1 |
1 |
2 |
107 |
| A New Approach to Volatility Modeling: The High-Dimensional Markov Model |
0 |
0 |
0 |
34 |
0 |
1 |
2 |
65 |
| A New Class of Multivariate skew Densities, with Application to GARCH Models |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
540 |
| A comparison of Forecasting Procedures for Macroeconomic Series: The Contribution of Structural Break Models |
0 |
0 |
0 |
59 |
0 |
0 |
1 |
152 |
| A comparison of financial duration models via density forecast |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
62 |
| A comparison of financial duration models via density forecasts |
0 |
0 |
0 |
4 |
0 |
0 |
1 |
43 |
| A comparison of financial duration models via density forecasts |
0 |
0 |
0 |
81 |
1 |
1 |
2 |
1,142 |
| A comparison of forecasting procedures for macroeconomic series: the contribution of structural break models |
0 |
0 |
0 |
61 |
0 |
0 |
1 |
76 |
| A component GARCH model with time varying weights |
0 |
0 |
0 |
14 |
0 |
0 |
1 |
64 |
| A component GARCH model with time varying weights |
0 |
0 |
0 |
0 |
0 |
2 |
4 |
1,056 |
| A component GARCH model with time varying weights |
0 |
0 |
0 |
87 |
0 |
0 |
0 |
287 |
| A dynamic component model for forecasting high-dimensional realized covariance matrices |
0 |
0 |
0 |
68 |
0 |
1 |
1 |
149 |
| A dynamic component model for forecasting high-dimensional realized covariance matrices |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
43 |
| A new approach to volatility modeling: the High-Dimensional Markov model |
0 |
0 |
0 |
81 |
0 |
0 |
2 |
151 |
| A new approach: the factorial hidden Markov volatility model |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
25 |
| A new class of multivariate skew densities, with application to GARCH models |
0 |
0 |
0 |
99 |
0 |
1 |
1 |
237 |
| A new class of multivariate skew densities, with application to generalized autoregressive conditional heteroscedasticity models |
0 |
0 |
0 |
17 |
0 |
0 |
2 |
52 |
| ADAPTIVE POLAR SAMPLING WITH AN APPLICATION TO A BAYES MEASURE OF VALUE-AT-RISK |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
423 |
| Adaptive Polar Sampling |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
162 |
| Adaptive Polar Sampling with an Application to a Bayes Measure of Value-at-Risk |
0 |
0 |
0 |
182 |
0 |
1 |
2 |
1,006 |
| Adaptive Polar Sampling with an Application to a Bayes Measure of Value-at-Risk |
0 |
0 |
0 |
6 |
0 |
2 |
2 |
88 |
| Adaptive Polar Sampling: A New MC Technique for the Analysis of Ill-behaved Surfaces |
0 |
0 |
0 |
24 |
0 |
0 |
0 |
516 |
| Adaptive polar sampling with an application to a Bayes measure of value-at-risk |
0 |
0 |
0 |
10 |
0 |
1 |
3 |
533 |
| Adaptive polar sampling, a class of flexibel and robust Monte Carlo integration methods |
0 |
0 |
0 |
6 |
0 |
1 |
1 |
65 |
| Adaptive polar sampling: a new MC technique for the analysis of ill behaved surfaces |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
51 |
| Adaptive radial-based direction sampling: some flexible and robust Monte Carlo integration methods |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
17 |
| Adaptive radial-based direction sampling; Some flexible and robust Monte Carlo integration methods |
0 |
0 |
0 |
19 |
1 |
2 |
3 |
109 |
| An export model for the Belgian industry |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
11 |
| Approximate HPD regions for testing residual autocorrelation using augmented regressions |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
18 |
| Approximate HPD regions for testing residual autocorrelation using augmented regressions |
0 |
0 |
0 |
8 |
0 |
1 |
2 |
304 |
| Art experts and auctions Are pre-sale estimates unbiased and fully informative? |
0 |
0 |
1 |
74 |
0 |
0 |
1 |
244 |
| Art experts and auctions are pre-sale estimates unbiased and fully informative? |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
50 |
| Art experts and auctions:are pre-sale estimates unbiased and fully informative |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
44 |
| Asymmetric ACD models: Introducing price information in ACD models |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
26 |
| Asymmetric ACD models: introducing price information in ACD models with a two state transition model |
0 |
0 |
0 |
78 |
1 |
2 |
3 |
1,057 |
| Asymmetric Models for Realized Covariances |
0 |
0 |
4 |
4 |
0 |
1 |
11 |
11 |
| Asymmetric Models for Realized Covariances |
1 |
1 |
8 |
8 |
1 |
2 |
18 |
18 |
| Autoregressive Moving Average Infinite Hidden Markov-Switching Models |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
23 |
| Autoregressive moving average infinite hidden Markov-switching models |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
25 |
| Autoregressive moving average infinite hidden markov-switching models |
0 |
0 |
1 |
52 |
0 |
0 |
3 |
114 |
| BAYESIAN CLUSTERING OF SIMILAR MULTIVARIATE GARCH MODELS |
0 |
0 |
1 |
195 |
0 |
1 |
4 |
497 |
| BAYESIAN LIMITED INFORMATION ANALYSIS REVISITED |
0 |
0 |
0 |
1 |
1 |
2 |
2 |
19 |
| Bayesian Inference in Dynamic Disequilibrium Models: an Application to the Polish Credit Market |
0 |
0 |
0 |
133 |
0 |
0 |
0 |
400 |
| Bayesian Inference on GARCH Models Using the Gibbs Sampler |
0 |
0 |
0 |
1 |
0 |
0 |
3 |
1,688 |
| Bayesian Inference on GARCH Models using the Gibbs Sampler |
0 |
0 |
0 |
58 |
0 |
0 |
0 |
1,181 |
| Bayesian Option Pricing Using Asymmetric GARCH |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
1,570 |
| Bayesian Option Pricing using Asymmetric Garch Models |
0 |
0 |
0 |
2 |
0 |
2 |
3 |
1,001 |
| Bayesian and classical econometric modeling of time series |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
15 |
| Bayesian clustering of many GARCH models |
0 |
0 |
0 |
43 |
0 |
0 |
1 |
120 |
| Bayesian clustering of many GARCH models |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
27 |
| Bayesian diagnostics for heterogeneity |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
19 |
| Bayesian inference for the mixed conditional heteroskedasticity model |
0 |
0 |
0 |
63 |
0 |
0 |
0 |
323 |
| Bayesian inference for the mixed conditional heteroskedasticity model |
0 |
0 |
0 |
14 |
0 |
1 |
5 |
108 |
| Bayesian inference for the mixed conditional heteroskedasticity model |
0 |
0 |
0 |
41 |
0 |
0 |
2 |
234 |
| Bayesian inference for the mixed conditional heteroskedasticity model |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
32 |
| Bayesian inference in dynamic disequilibrium models: an application to the Polish credit market |
0 |
0 |
0 |
8 |
0 |
2 |
2 |
39 |
| Bayesian inference in dynamic disequilibrium models: an application to the Polish credit market |
0 |
0 |
0 |
25 |
0 |
0 |
0 |
122 |
| Bayesian inference on GARCH models using the Gibbs sampler |
0 |
0 |
0 |
25 |
0 |
0 |
3 |
94 |
| Bayesian methods |
1 |
2 |
4 |
416 |
1 |
2 |
19 |
688 |
| Bayesian option pricing using asymmetric GARCH |
0 |
0 |
0 |
22 |
0 |
1 |
3 |
82 |
| Bayesian option pricing using asymmetric GARCH models |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
30 |
| Bayesian specification analysis and estimation of simultaneous equation models using Monte Carlo methods |
0 |
0 |
0 |
7 |
0 |
1 |
2 |
30 |
| Bayesian specification analysis and estimation of simultaneous equation models using Monte Carlo methods |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
80 |
| Computationally efficient inference procedures for vast dimensional realized covariance models |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
29 |
| Computationally efficient inference procedures for vast dimensional realized covariance models |
0 |
0 |
0 |
34 |
0 |
0 |
0 |
100 |
| DCC and DECO-HEAVY: a multivariate GARCH model based on realized variances and correlations |
0 |
0 |
0 |
157 |
0 |
3 |
6 |
354 |
| DCC-HEAVY: A multivariate GARCH model based on realized variances and correlations |
1 |
1 |
1 |
23 |
1 |
1 |
4 |
90 |
| Do Art Experts make Rational Estimates of Pre-Sale Prices ? |
0 |
0 |
0 |
16 |
0 |
0 |
0 |
83 |
| Dynamic conditional correlation models for realized covariance matrices |
1 |
2 |
5 |
130 |
1 |
5 |
11 |
375 |
| Dynamic latent factor models for intensity processes |
0 |
0 |
0 |
114 |
0 |
0 |
1 |
327 |
| Econometric analysis of intra-daily trading activity on the Tokyo Stock Exchange |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
27 |
| Econometrics |
0 |
0 |
0 |
23 |
0 |
0 |
0 |
105 |
| Econometrics |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
41 |
| Editors' introduction. First Riverboat conference on Bayesian econometrics and statistics |
0 |
0 |
0 |
0 |
0 |
3 |
3 |
22 |
| Efficient importance sampling for ML estimation of SCD models |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
26 |
| Efficient importance sampling for ML estimation of SCD models |
0 |
0 |
0 |
47 |
1 |
1 |
1 |
191 |
| Efficient importance sampling for ML estimation of SCD models |
0 |
0 |
0 |
21 |
0 |
0 |
2 |
119 |
| Estimating End-Use Demand: A Bayesian Approach |
0 |
0 |
0 |
6 |
0 |
0 |
0 |
365 |
| Estimating End-use Demand: a Bayesian Approach |
0 |
0 |
0 |
2 |
1 |
1 |
1 |
22 |
| Estimating and forecasting structural breaks in financial time series |
0 |
0 |
0 |
106 |
1 |
1 |
3 |
350 |
| Estimating end-use demand: A Bayesian approach |
0 |
0 |
1 |
1 |
0 |
0 |
1 |
29 |
| Estimation and Empirical Performance of Non-Scalar DCC Models |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
14 |
| Estimation and empirical performance of non-scalar dynamic conditional correlation models |
0 |
0 |
0 |
51 |
0 |
0 |
0 |
62 |
| Exchange Rate Volatility and the Mixture of Distribution Hypothesis |
0 |
0 |
0 |
166 |
0 |
1 |
1 |
569 |
| Exchange rate volatility and the mixture of distribution hypothesis |
0 |
0 |
0 |
2 |
0 |
0 |
1 |
29 |
| Exchange rate volatility and the mixture of distribution hypothesis |
0 |
0 |
0 |
37 |
0 |
0 |
1 |
153 |
| Explaining Adaptive Radial-Based Direction Sampling |
0 |
0 |
0 |
7 |
0 |
0 |
0 |
59 |
| Forecasting a long memory process subject to structural breaks |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
| Forecasting comparison of long term component dynamic models for realized covariance matrices |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
21 |
| Forecasting comparison of long term component dynamic models for realized covariance matrices |
0 |
0 |
0 |
39 |
1 |
1 |
2 |
87 |
| Forecasting long memory processes subject to structural breaks |
0 |
0 |
0 |
54 |
1 |
2 |
2 |
156 |
| General to Specific Modelling of Exchange Rate Volatility: a Forecast Evaluation |
0 |
0 |
0 |
174 |
0 |
0 |
1 |
518 |
| General to specific modelling of exchange rate volatility: a forecast evaluation |
0 |
0 |
0 |
149 |
0 |
1 |
2 |
508 |
| General to specific modelling of exchange rate volatility: a forecast evaluation |
0 |
0 |
0 |
9 |
0 |
1 |
1 |
143 |
| General-to-specific modelling of exchange rate volatility: a forecast evaluation |
0 |
0 |
0 |
1 |
0 |
1 |
1 |
42 |
| Gibbs sampling approach to cointegration |
0 |
0 |
0 |
0 |
1 |
1 |
2 |
17 |
| High frequency finance |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
41 |
| High frequency financial econometrics. Recent developments |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
74 |
| Identification Restrictions and Posterior Densities in Cointegrated Gaussian VAR Systems |
0 |
1 |
1 |
50 |
1 |
2 |
3 |
212 |
| Identification restrictions and posterior densities in cointegrated Gaussian VAR system |
0 |
0 |
0 |
0 |
1 |
3 |
3 |
44 |
| Identifying long-run behaviour with non-stationary data |
0 |
0 |
0 |
22 |
0 |
0 |
0 |
454 |
| Intra-Daily FX Optimal Portfolio Allocation |
0 |
0 |
1 |
218 |
0 |
2 |
6 |
1,013 |
| Intra-daily FX optimal portfolio allocation |
0 |
0 |
1 |
35 |
0 |
0 |
4 |
134 |
| Marginal Likelihood for Markov-Switching and Change-Point GARCH Models |
0 |
0 |
0 |
21 |
0 |
0 |
0 |
183 |
| Marginal Likelihood for Markov-Switching and Change-Point Garch Models |
0 |
0 |
0 |
31 |
0 |
0 |
0 |
105 |
| Marginal Likelihood for Markov-switching and Change-point Garch Models |
0 |
0 |
0 |
55 |
0 |
1 |
2 |
161 |
| Marginal likelihood for Markov-switching and change-point GARCH models |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
| Marginal likelihood for Markov-switching and change-point GARCH models |
0 |
0 |
0 |
13 |
0 |
0 |
3 |
77 |
| Modeling Realized Covariance Matrices: A Class of Hadamard Exponential Models |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
9 |
| Modeling and predicting intra-day price movements in stock markets with autoregressive conditional duration models |
0 |
0 |
0 |
0 |
0 |
1 |
3 |
27 |
| Modeling the Dependence of Conditional Correlations on Volatility |
0 |
0 |
0 |
148 |
0 |
2 |
4 |
222 |
| Modeling the dependence of conditional correlations on market volatility |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
36 |
| Modeling the dependence of conditional correlations on volatility |
0 |
0 |
0 |
27 |
1 |
1 |
1 |
64 |
| Modelling Financial High Frequency Data Using Point Processes |
0 |
1 |
1 |
254 |
0 |
1 |
7 |
677 |
| Modelling Realized Covariance Matrices: a Class of Hadamard Exponential Models |
0 |
0 |
1 |
44 |
0 |
1 |
5 |
65 |
| Modelling Realized Covariance Matrices: a Class of Hadamard Exponential Models |
0 |
0 |
0 |
7 |
0 |
0 |
2 |
24 |
| Modelling financial high frequency data using point processes |
0 |
0 |
0 |
95 |
1 |
1 |
5 |
352 |
| Modelling financial high frequency data using point processes |
0 |
0 |
0 |
101 |
0 |
0 |
1 |
308 |
| Modelling financial high frequency data using point processes |
0 |
0 |
0 |
0 |
0 |
0 |
4 |
47 |
| Modelling interest rates with a cointegrated VAR-GARCH model |
0 |
0 |
2 |
165 |
0 |
0 |
4 |
1,984 |
| Modelling multivariate volatility of electricity futures |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
6 |
| Multiplicative Conditional Correlation Models for Realized Covariance Matrices |
0 |
0 |
0 |
37 |
0 |
2 |
3 |
106 |
| Multivariate GARCH models and their Estimation |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
529 |
| Multivariate GARCH models: a survey |
0 |
0 |
0 |
475 |
1 |
1 |
2 |
1,139 |
| Multivariate GARCH models: a survey |
0 |
0 |
0 |
40 |
0 |
1 |
7 |
222 |
| Multivariate mixed normal conditional heteroskedasticity |
0 |
0 |
0 |
21 |
0 |
0 |
1 |
103 |
| Multivariate mixed normal conditional heteroskedasticity |
0 |
0 |
0 |
149 |
0 |
0 |
1 |
473 |
| Multivariate mixed normal conditional heteroskedasticity |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
17 |
| Multivariate volatility modeling of electricity futures |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
| Multivariate volatility modeling of electricity futures |
0 |
0 |
0 |
66 |
0 |
0 |
1 |
209 |
| Multivariate volatility modeling of electricity futures |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
12 |
| Multivariate volatility modeling of electricity futures |
0 |
0 |
0 |
20 |
1 |
1 |
2 |
112 |
| News announcements, market activity and volatility in the Euro/Dollar foreign exchange market |
0 |
0 |
0 |
66 |
1 |
1 |
3 |
218 |
| News announcements, market activity and volatility in the euro/dollar foreign exchange market |
0 |
0 |
0 |
6 |
1 |
1 |
2 |
55 |
| Nonlinearities and Regimes in Conditional Correlations with Different Dynamics |
0 |
0 |
0 |
39 |
0 |
0 |
0 |
57 |
| Nonlinearities and regimes in conditional correlations with different dynamics |
0 |
0 |
0 |
0 |
0 |
2 |
3 |
11 |
| Nonlinearities and regimes in conditional correlations with different dynamics |
0 |
0 |
0 |
18 |
0 |
0 |
1 |
48 |
| On Marginal Likelihood Computation in Change-point Models |
0 |
0 |
0 |
113 |
0 |
0 |
1 |
357 |
| On marginal likelihood computation in change-point models |
0 |
0 |
0 |
33 |
0 |
0 |
0 |
98 |
| On marginal likelihood computation in change-point models |
0 |
0 |
0 |
6 |
0 |
0 |
2 |
37 |
| On the Weak Consistency of the Quasi-Maximum Likelihood Estimator in VAR Models with BEKK-GARCH(1,q) Errors |
0 |
0 |
0 |
55 |
0 |
0 |
1 |
871 |
| Posterior moments of elasticities between real wages and unemployment in Belgium: an application of Bayesian inference by Monte Carlo integration |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
14 |
| Posterior moments of elasticities between real wages and unemployment in Belgium: an application of Bayesian inference by Monte Carlo integration |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
19 |
| Ranking economics departments in Europe: a statistical approach |
1 |
1 |
1 |
243 |
1 |
1 |
2 |
744 |
| Ranking economics departments in Europe: a statistical approach |
0 |
0 |
0 |
8 |
0 |
0 |
1 |
57 |
| Realized Covariance Models with Time-varying Parameters and Spillover Effects |
1 |
1 |
3 |
17 |
1 |
1 |
7 |
24 |
| Recent developments in the econometrics of financial markets using intra-day data |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
8 |
| Regime switching GARCH models |
1 |
1 |
3 |
606 |
2 |
3 |
9 |
1,293 |
| Regime switching GARCH models |
0 |
0 |
0 |
80 |
0 |
0 |
5 |
265 |
| Regime switching GARCH models |
0 |
0 |
1 |
189 |
0 |
1 |
6 |
535 |
| State-Space Models on the Stiefel Manifold with A New Approach to Nonlinear Filtering |
0 |
0 |
0 |
81 |
0 |
0 |
0 |
67 |
| State-space models on the Stiefel Manifold with a new approach to nonlinear filtering |
0 |
0 |
0 |
0 |
1 |
2 |
4 |
14 |
| Stochastic conditional intensity processes |
0 |
0 |
0 |
6 |
1 |
1 |
5 |
37 |
| THE "PATHOLOGY" OF THE NATURAL CONJUGATE PRIOR DENSITY IN THE REGRESSION MODEL |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
911 |
| THE LAW OF LARGE (SMALL?) NUMBERS AND THE DEMAND FOR INSURANCE |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1,021 |
| The "pathology" of the natural conjugate prior density in the regression model |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
8 |
| The Contribution of Structural Break Models to Forecasting Macroeconomic Series |
0 |
1 |
2 |
383 |
1 |
2 |
6 |
688 |
| The Contribution of Structural Break Models to Forecating Macroeconomic Series |
0 |
0 |
0 |
0 |
1 |
1 |
3 |
45 |
| The Resistible Decline of European Science |
0 |
0 |
0 |
80 |
0 |
0 |
0 |
334 |
| The Resistible Decline of European Science |
0 |
0 |
0 |
4 |
1 |
1 |
10 |
79 |
| The contribution of realized covariance models to the economic value of volatility timing |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| The contribution of realized covariance models to the economic value of volatility timing |
0 |
0 |
1 |
25 |
0 |
2 |
7 |
36 |
| The law of large (small?) numbers and the demand for insurance |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
27 |
| The logarithmic ACD model: an application to market microstructure and NASDAQ |
0 |
0 |
1 |
57 |
0 |
0 |
2 |
1,886 |
| The logarithmic ACD model: an application to the bid-ask quote process of three NYSE stocks |
0 |
0 |
0 |
0 |
0 |
0 |
5 |
76 |
| The moments of Log-ACD models |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
55 |
| The moments of Log-ACD models |
0 |
0 |
0 |
51 |
2 |
2 |
4 |
206 |
| The resistible decline of European Science |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
28 |
| The resistible decline of European science |
0 |
0 |
0 |
1 |
1 |
1 |
3 |
47 |
| The resistible decline of European science |
0 |
0 |
0 |
39 |
0 |
0 |
0 |
207 |
| The stochastic conditional duration model: a latent factor model for the analysis of financial durations |
0 |
0 |
1 |
67 |
0 |
0 |
3 |
1,221 |
| The stochastic conditional duration model: a latent factor model for the analysis of financial durations |
0 |
0 |
0 |
0 |
0 |
1 |
5 |
42 |
| The stochastic conditional duration model: a latent variable model for the analysis of financial durations |
0 |
0 |
0 |
4 |
0 |
0 |
1 |
23 |
| Theory and Inference for a Markov-Switching GARCH Model |
0 |
0 |
3 |
552 |
0 |
0 |
5 |
1,355 |
| Theory and inference for a Markov switching GARCH model |
0 |
0 |
1 |
54 |
0 |
1 |
4 |
163 |
| Theory and inference for a Markov switching GARCH model |
0 |
0 |
1 |
130 |
0 |
1 |
3 |
346 |
| Theory and inference for a Markov switching Garch model |
0 |
0 |
0 |
4 |
0 |
0 |
2 |
45 |
| Theory and inference for a Markov switching Garch model |
0 |
0 |
0 |
391 |
0 |
0 |
2 |
732 |
| Trends and breaking points in the Bayesian econometric literature |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
18 |
| Volatility Models |
0 |
0 |
0 |
0 |
0 |
2 |
4 |
47 |
| Volatility Models |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
23 |
| Volatility models |
0 |
1 |
2 |
313 |
0 |
3 |
7 |
644 |
| We modeled long memory with just one lag! |
0 |
0 |
0 |
0 |
1 |
1 |
3 |
5 |
| We modeled long memory with just one lag! |
0 |
0 |
1 |
55 |
1 |
1 |
6 |
36 |
| We modeled long memory with just one lag! |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
| Total Working Papers |
8 |
14 |
56 |
10,309 |
38 |
117 |
432 |
48,741 |