Working Paper |
File Downloads |
Abstract Views |

Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |

"Rotterdam Econometrics": an analysis of publications of the econometric institute 1956-2004 |
0 |
0 |
0 |
6 |
0 |
1 |
1 |
32 |

"Rotterdam econometrics": publications of the econometric institute 1956-2005 |
0 |
0 |
1 |
4 |
0 |
1 |
4 |
34 |

A BAYESIAN ANALYSIS OF THE UNIT ROOT HYPOTHESIS |
0 |
0 |
1 |
1 |
0 |
0 |
4 |
10 |

A BAYESIAN ANALYSIS OF THE UNIT ROOT IN REAL EXCHANGE RATES |
0 |
0 |
0 |
2 |
1 |
2 |
5 |
19 |

A Bayesian Analysis of the PPP Puzzle using an Unobserved Components Model |
0 |
0 |
0 |
95 |
0 |
0 |
3 |
427 |

A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance |
1 |
1 |
5 |
5 |
2 |
3 |
22 |
22 |

A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance |
0 |
0 |
33 |
33 |
1 |
2 |
46 |
46 |

A Bayesian analysis of the PPP puzzle using an unobserved components model |
0 |
0 |
0 |
7 |
0 |
2 |
4 |
50 |

A Class of Adaptive EM-based Importance Sampling Algorithms for Efficient and Robust Posterior and Predictive Simulation |
0 |
0 |
0 |
22 |
0 |
0 |
5 |
90 |

A Class of Adaptive Importance Sampling Weighted EM Algorithms for Efficient and Robust Posterior and Predictive Simulation |
0 |
0 |
2 |
24 |
0 |
0 |
3 |
94 |

A Comparative Study of Monte Carlo Methods for Efficient Evaluation of Marginal Likelihood |
0 |
0 |
1 |
29 |
1 |
10 |
32 |
126 |

A Simple Strategy to prune Neural Networks with an Application to Economic Time Series |
0 |
0 |
0 |
83 |
0 |
0 |
0 |
214 |

A product of multivariate T densities as upper bound for the posterior kernel of simultaneous equation model parameters |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
36 |

A product of multivariate T densities as upper bound for the posterior kernel of simultaneous equation model parameters |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
12 |

A reconsideration of the Angrist-Krueger analysis on returns to education |
0 |
0 |
3 |
86 |
3 |
4 |
24 |
433 |

A simple strategy to prune neural networks with an application to economic time series |
0 |
0 |
0 |
16 |
0 |
0 |
0 |
49 |

ADAPTIVE POLAR SAMPLING WITH AN APPLICATION TO A BAYES MEASURE OF VALUE-AT-RISK |
0 |
0 |
0 |
0 |
1 |
2 |
5 |
418 |

AN ALGORITHM FOR THE COMPUTATION OF POSTERIOR MOMENTS AND DENSITIES USING SIMPLE IMPORTANCE SAMPLING |
0 |
0 |
0 |
1 |
0 |
1 |
1 |
13 |

AdMit: Adaptive Mixtures of Student-t Distributions |
0 |
0 |
0 |
42 |
0 |
2 |
6 |
196 |

Adaptive Mixture of Student-t distributions as a Flexible Candidate Distribution for Efficient Simulation: the R Package AdMit |
0 |
0 |
0 |
40 |
0 |
0 |
0 |
187 |

Adaptive Polar Sampling |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
162 |

Adaptive Polar Sampling with an Application to a Bayes Measure of Value-at-Risk |
0 |
0 |
0 |
6 |
0 |
1 |
3 |
85 |

Adaptive Polar Sampling with an Application to a Bayes Measure of Value-at-Risk |
0 |
0 |
0 |
181 |
0 |
1 |
4 |
1,002 |

Adaptive Polar Sampling: A New MC Technique for the Analysis of Ill-behaved Surfaces |
0 |
0 |
0 |
24 |
0 |
0 |
0 |
516 |

Adaptive mixture of Student-t distributions as a flexible candidate distribution for efficient simulation: the R package AdMit |
0 |
0 |
0 |
63 |
0 |
0 |
1 |
288 |

Adaptive polar sampling with an application to a Bayes measure of value-at-risk |
0 |
0 |
0 |
10 |
0 |
2 |
10 |
523 |

Adaptive polar sampling, a class of flexibel and robust Monte Carlo integration methods |
0 |
0 |
0 |
6 |
0 |
2 |
6 |
64 |

Adaptive polar sampling: a new MC technique for the analysis of ill behaved surfaces |
0 |
0 |
0 |
0 |
1 |
2 |
3 |
47 |

Adaptive radial-based direction sampling: some flexible and robust Monte Carlo integration methods |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
15 |

Adaptive radial-based direction sampling; Some flexible and robust Monte Carlo integration methods |
0 |
0 |
0 |
18 |
0 |
2 |
4 |
102 |

BAYESIAN ESTIMATES OF EQUATION SYSTEM PARAMETERS An Application of Integration by Monte Carlo |
0 |
0 |
1 |
2 |
1 |
2 |
9 |
23 |

BAYESIAN ESTIMATES OF EQUATION SYSTEM PARAMETERS An Unorthodox Application of Monte Carlo |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
8 |

BAYESIAN SPECIFICATION ANALYSIS AND ESTIMATION OF SIMULTANEOUS EQUATION MODELS USING MONTE CARLO METHODS |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
837 |

Backtesting Value-at-Risk using Forecasts for Multiple Horizons, a Comment on the Forecast Rationality Tests of A.J. Patton and A. Timmermann |
0 |
1 |
1 |
81 |
0 |
4 |
4 |
100 |

Bayes Estimates of Markov Trends in possibly Cointegrated Series: An Application to US Consumption and Income |
0 |
0 |
0 |
128 |
0 |
0 |
3 |
541 |

Bayes estimates of Markov trends in possibly cointegrated series: an application to US consumption and income |
0 |
0 |
1 |
17 |
0 |
0 |
4 |
97 |

Bayes estimates of multimodal density features using DNA and Economic Data |
0 |
0 |
8 |
8 |
1 |
2 |
11 |
11 |

Bayes estimates of the cyclical component in twentieth centruy US gross domestic product |
0 |
0 |
1 |
42 |
0 |
1 |
3 |
101 |

Bayes model averaging of cyclical decompositions in economic time series |
0 |
0 |
0 |
13 |
0 |
0 |
1 |
47 |

Bayesian Analysis of Boundary and Near-Boundary Evidence in Econometric Models with Reduced Rank |
0 |
0 |
0 |
51 |
0 |
0 |
0 |
31 |

Bayesian Analysis of Instrumental Variable Models: Acceptance-Rejection within Direct Monte Carlo |
0 |
0 |
0 |
37 |
0 |
1 |
7 |
183 |

Bayesian Approaches to Cointegration |
0 |
0 |
2 |
273 |
1 |
2 |
21 |
609 |

Bayesian Averaging over Many Dynamic Model Structures with Evidence on the Great Ratios and Liquidity Trap Risk |
0 |
0 |
0 |
55 |
0 |
1 |
2 |
133 |

Bayesian Combinations of Stock Price Predictions with an Application to the Amsterdam Exchange Index |
0 |
0 |
2 |
43 |
1 |
3 |
7 |
144 |

Bayesian Forecasting of US Growth using Basic Time Varying Parameter Models and Expectations Data |
0 |
0 |
0 |
47 |
0 |
0 |
1 |
68 |

Bayesian Forecasting of Value at Risk and Expected Shortfall using Adaptive Importance Sampling |
0 |
0 |
1 |
81 |
0 |
0 |
6 |
242 |

Bayesian Model Averaging in Vector Autoregressive Processes with an Investigation of Stability of the US Great Ratios and Risk of a Liquidity Trap in the USA, UK and Japan |
0 |
0 |
1 |
55 |
0 |
0 |
5 |
203 |

Bayesian Model Selection with an Uninformative Prior |
0 |
0 |
1 |
252 |
0 |
0 |
16 |
914 |

Bayesian Simultaneous Equations Analysis using Reduced Rank Structures |
0 |
0 |
1 |
23 |
1 |
6 |
8 |
117 |

Bayesian Simultaneous Equations Analysis using Reduced Rank Structures |
0 |
0 |
1 |
123 |
0 |
0 |
8 |
446 |

Bayesian analysis of boundary and near-boundary evidence in econometric models with reduced rank |
0 |
0 |
0 |
27 |
1 |
2 |
4 |
27 |

Bayesian approaches to cointegratrion |
0 |
0 |
0 |
31 |
0 |
0 |
0 |
94 |

Bayesian model averaging in vector autoregressive processes with an investigation of stability of the US great ratios and risk of a liquidity trap in the USA, UK and Japan |
0 |
0 |
1 |
18 |
1 |
3 |
5 |
89 |

Bayesian model selection for a sharp null and a diffuse alternative with econometric applications |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
60 |

Bayesian near-boundary analysis in basic macroeconomic time series models |
0 |
0 |
2 |
85 |
0 |
1 |
3 |
168 |

Bayesian specification analysis and estimation of simultaneous equation models using Monte Carlo methods |
0 |
0 |
0 |
7 |
0 |
0 |
0 |
27 |

Bayesian specification analysis and estimation of simultaneous equation models using Monte Carlo methods |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
76 |

Censored Posterior and Predictive Likelihood in Left-Tail Prediction for Accurate Value at Risk Estimation |
0 |
0 |
0 |
50 |
0 |
0 |
3 |
97 |

Combination Schemes for Turning Point Predictions |
0 |
0 |
0 |
67 |
0 |
3 |
6 |
144 |

Combination schemes for turning point predictions |
0 |
0 |
0 |
19 |
0 |
2 |
6 |
125 |

Combination schemes for turning point predictions |
0 |
0 |
0 |
57 |
1 |
2 |
9 |
112 |

Combined Density Nowcasting in an Uncertain Economic Environment |
0 |
0 |
0 |
11 |
2 |
5 |
15 |
74 |

Combined Density Nowcasting in an uncertain economic environment |
0 |
0 |
1 |
49 |
0 |
2 |
7 |
95 |

Combined Forecasts from Linear and Nonlinear Time Series Models |
0 |
0 |
0 |
264 |
0 |
1 |
5 |
697 |

Combined forecasts from linear and nonlinear time series models |
0 |
0 |
0 |
11 |
0 |
0 |
5 |
76 |

Combining Predictive Densities using Bayesian Filtering with Applications to US Economics Data |
0 |
0 |
0 |
40 |
0 |
2 |
2 |
85 |

Combining Predictive Densities using Nonlinear Filtering with Applications to US Economics Data |
0 |
0 |
1 |
16 |
0 |
1 |
3 |
68 |

Combining predictive densities using Bayesian filtering with applications to US economic data |
0 |
0 |
0 |
55 |
2 |
2 |
5 |
163 |

Combining predictive densities using Bayesian filtering with applications to US economics data |
0 |
0 |
0 |
66 |
0 |
0 |
3 |
109 |

Comparison of the Anderson-Rubin test for overidentification and the Johansen test for cointegration |
0 |
0 |
0 |
49 |
1 |
4 |
18 |
281 |

Cyclical Components in Economic Time Series: a Bayesian Approach |
0 |
0 |
1 |
374 |
0 |
0 |
5 |
1,222 |

Cyclical components in economic time series |
0 |
0 |
1 |
93 |
0 |
2 |
7 |
184 |

Cyclical components in economic time series: A Bayesian approach |
0 |
0 |
0 |
160 |
0 |
1 |
2 |
565 |

Daily Exchange Rate Behaviour and Hedging of Currency Risk |
0 |
0 |
0 |
167 |
0 |
1 |
3 |
493 |

Daily Exchange Rate Behaviour and Hedging of Currency Risk |
0 |
0 |
0 |
479 |
1 |
1 |
3 |
1,641 |

Daily Exchange Rate Behaviour and Hedging of Currency Risk |
0 |
0 |
0 |
516 |
1 |
3 |
6 |
2,404 |

Daily exchange rate behaviour and hedging of currency risk |
0 |
0 |
1 |
27 |
0 |
0 |
1 |
110 |

Daily exchange rate behaviour and hedging of currency risk |
0 |
0 |
1 |
21 |
2 |
2 |
4 |
99 |

Distributional Dynamics using Quartic-based State-Space models |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
4 |

Distributional Dynamics using Quartic-based State-Space models |
0 |
0 |
0 |
0 |
1 |
2 |
2 |
9 |

Distributional Dynamics using Quartic-based State-Space models |
0 |
0 |
0 |
0 |
0 |
2 |
4 |
6 |

Distributional Dynamics using Quartic-based State-Space models |
0 |
0 |
0 |
0 |
1 |
2 |
2 |
16 |

Divergent Priors and well Behaved Bayes Factors |
0 |
0 |
0 |
31 |
0 |
0 |
2 |
138 |

Dynamic Predictive Density Combinations for Large Data Sets in Economics and Finance |
0 |
1 |
14 |
74 |
2 |
3 |
32 |
155 |

Dynamic predictive density combinations for large data sets in economics and finance |
0 |
0 |
1 |
33 |
0 |
2 |
9 |
100 |

EXPERIMENTS WITH SOME ALTERNATIVES FOR SIMPLE IMPORTANCE SAMPLING IN MONTE CARLO INTEGRATION |
0 |
1 |
3 |
13 |
6 |
11 |
20 |
52 |

Editors' introduction. First Riverboat conference on Bayesian econometrics and statistics |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
16 |

Efficient Sampling from Non-Standard Distributions Using Neural NetworkApproximations |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
153 |

Evidence on Features of a DSGE Business Cycle Model from Bayesian Model Averaging |
0 |
0 |
1 |
55 |
0 |
1 |
2 |
123 |

Evidence on a DSGE Business Cycle model subject to Neutral and Investment-Specific Technology Shocks using Bayesian Model Averaging |
0 |
0 |
0 |
52 |
0 |
0 |
1 |
115 |

Evidence on a Real Business Cycle Model with Neutral and Investment-Specific Technology Shocks using Bayesian Model Averaging |
0 |
0 |
1 |
34 |
0 |
0 |
2 |
75 |

Evidence on a Real Business Cycle model with Neutral and Investment-Specific Technology Shocks using Bayesian Model Averaging |
0 |
1 |
1 |
61 |
0 |
2 |
2 |
118 |

Exceptions to Bartlett’s Paradox |
0 |
0 |
1 |
149 |
0 |
2 |
13 |
664 |

Explaining Adaptive Radial-Based Direction Sampling |
0 |
0 |
0 |
7 |
0 |
0 |
0 |
56 |

FURTHER EXPERIENCE IN BAYESIAN ANALYSIS USING MONTE CARLO INTEGRATION |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
11 |

Forecast Accuracy and Economic Gains from Bayesian Model Averaging using Time Varying Weights |
0 |
0 |
0 |
96 |
0 |
1 |
5 |
246 |

Forecast Density Combinations of Dynamic Models and Data Driven Portfolio Strategies |
0 |
0 |
1 |
31 |
0 |
3 |
7 |
49 |

Forecast Density Combinations of Dynamic Models and Data Driven Portfolio Strategies |
0 |
0 |
1 |
13 |
0 |
2 |
10 |
39 |

Forecast Density Combinations with Dynamic Learning for Large Data Sets in Economics and Finance |
1 |
1 |
4 |
48 |
1 |
5 |
22 |
70 |

Forecast accuracy and economic gains from Bayesian model averaging using time varying weight |
0 |
0 |
2 |
96 |
0 |
0 |
3 |
158 |

Forecast density combinations with dynamic learning for large data sets in economics and finance |
0 |
2 |
12 |
31 |
0 |
6 |
27 |
48 |

Functional approximations to posterior densities: a neural network approach to efficient sampling |
0 |
0 |
0 |
5 |
0 |
0 |
1 |
37 |

Gibbs sampling in econometric practice |
0 |
2 |
3 |
58 |
0 |
6 |
10 |
169 |

Historical Developments in Bayesian Econometrics after Cowles Foundation Monographs 10, 14 |
0 |
0 |
0 |
15 |
0 |
3 |
8 |
82 |

Improper priors with well defined Bayes Factors |
0 |
0 |
0 |
261 |
0 |
2 |
7 |
949 |

Improper priors with well defined Bayes Factors |
0 |
0 |
0 |
19 |
0 |
2 |
3 |
89 |

Instrumental Variables, Errors in Variables, and Simultaneous Equations Models: Applicability and Limitations of Direct Monte Carlo |
0 |
1 |
2 |
55 |
0 |
3 |
8 |
197 |

Interactions between Eurozone and US Booms and Busts: A Bayesian Panel Markov-switching VAR Model |
0 |
0 |
1 |
24 |
0 |
0 |
5 |
99 |

Interactions between eurozone and US booms and busts: A Bayesian panel Markov-switching VAR model |
0 |
0 |
1 |
46 |
0 |
1 |
6 |
163 |

Interactions between eurozone and US booms and busts: A Bayesian panel Markov-switching VAR model |
0 |
0 |
2 |
68 |
2 |
4 |
14 |
194 |

Interactions between eurozone and US booms and busts: A Bayesian panel Markov-switching VAR model |
0 |
0 |
0 |
55 |
0 |
2 |
11 |
176 |

Interconnections between Eurozone and US Booms and Busts using a Bayesian Panel Markov-Switching VAR Mode |
0 |
2 |
4 |
92 |
1 |
5 |
13 |
110 |

Jan Tinbergen (1903-1994) |
0 |
0 |
1 |
28 |
1 |
2 |
10 |
119 |

LIKELIHOOD DIAGNOSTICS AND BAYESIAN ANALYSIS OF A MICRO-ECONOMIC DISEQUILIBRIUM MODEL FOR RETAIL SERVICES |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
15 |

Learning to Average Predictively over Good and Bad: Comment on: Using Stacking to Average Bayesian Predictive Distributions |
0 |
0 |
1 |
36 |
0 |
1 |
6 |
31 |

MONTE CARLO ANALYSIS OF SKEW POSTERIOR DISTRIBUTIONS: AN ILLUSTRATIVE ECONOMETRIC EXAMPLE |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
5 |

Model Uncertainty and Bayesian Model Averaging in Vector Autoregressive Processes |
0 |
0 |
0 |
190 |
0 |
0 |
2 |
456 |

Model uncertainty and Bayesian model averaging in vector autoregressive processes |
0 |
0 |
0 |
7 |
0 |
1 |
1 |
47 |

Modelling option prices using neural networks |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
274 |

Natural conjugate priors for the instrumental variables regression model applied to the Angrist-Krueger data |
0 |
0 |
0 |
24 |
0 |
1 |
3 |
89 |

Neural network analysis of varying trends in real exchange rates |
0 |
0 |
0 |
17 |
0 |
0 |
0 |
48 |

Neural network approximations to posterior densities: an analytical approach |
0 |
0 |
0 |
3 |
0 |
1 |
1 |
36 |

Neural network based approximations to posterior densities: a class of flexible sampling methods with applications to reduced rank models |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
51 |

Neural networks as econometric tool |
0 |
0 |
0 |
46 |
1 |
1 |
3 |
123 |

Neural networks as econometric tool |
2 |
3 |
11 |
196 |
3 |
5 |
34 |
627 |

Note on neural network sampling for Bayesian inference of mixture processes |
0 |
0 |
0 |
3 |
0 |
1 |
1 |
44 |

Oil Price Shocks and Long Run Price and Import Demand Behavior |
0 |
0 |
0 |
26 |
0 |
1 |
1 |
115 |

On Bayesian routes to unit roots |
0 |
0 |
0 |
52 |
2 |
5 |
8 |
275 |

On Bayesian structural inference in a simultaneous equation model |
0 |
0 |
0 |
9 |
0 |
1 |
2 |
46 |

On the Practice of Bayesian Inference in Basic Economic Time Series Models using Gibbs Sampling |
0 |
0 |
0 |
138 |
0 |
1 |
2 |
481 |

On the Rise of Bayesian Econometrics after Cowles Foundation Monographs 10, 14 |
0 |
0 |
2 |
266 |
2 |
5 |
23 |
447 |

On the Variation of Hedging Decisions in Daily Currency Risk Management |
0 |
0 |
0 |
281 |
0 |
0 |
1 |
937 |

On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: An application of flexible sampling methods using neural networks |
0 |
0 |
0 |
21 |
0 |
0 |
1 |
145 |

On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks |
0 |
0 |
0 |
2 |
0 |
3 |
4 |
23 |

On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: an application of flexible sampling methods using neural networks |
0 |
1 |
1 |
3 |
0 |
2 |
3 |
51 |

On the variation of hedging decisions in daily currency risk management |
0 |
0 |
0 |
13 |
0 |
1 |
4 |
78 |

POSTERIOR ANALYSIS OF KLEIN'S MODEL |
0 |
0 |
1 |
1 |
0 |
1 |
2 |
8 |

POSTERIOR ANALYSIS OF POSSIBLY INTEGRATED TIME SERIES WITH AN APPLICATION TO REAL GNP |
0 |
0 |
0 |
0 |
1 |
1 |
5 |
10 |

POSTERIOR MOMENTS COMPUTED BY MIXED INTEGRATION |
0 |
0 |
1 |
1 |
0 |
1 |
3 |
11 |

POSTERIOR MOMENTS COMPUTED BY MIXED INTEGRATION |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
5 |

POSTERIOR MOMENTS OF THE KLEIN-GOLDBERGER MODEL |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
6 |

PREDICTIVE MOMENTS OF SIMULTANEOUS ECONOMETRIC MODELS A Bayesian Approach |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
4 |

Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo Matlab Toolbox |
0 |
0 |
0 |
118 |
0 |
4 |
13 |
473 |

Parallel Sequential Monte Carlo for Efficient Density Combination: The Deco Matlab Toolbox |
0 |
0 |
0 |
33 |
0 |
1 |
9 |
112 |

Parallel Sequential Monte Carlo for Efficient Density Combination: The Deco Matlab Toolbox |
0 |
0 |
0 |
78 |
0 |
1 |
3 |
176 |

Parallelization Experience with Four Canonical Econometric Models using ParMitISEM |
0 |
0 |
0 |
16 |
0 |
1 |
3 |
55 |

Parallelization experience with four canonical econometric models using ParMitISEM |
0 |
0 |
0 |
9 |
0 |
1 |
4 |
48 |

Partially Censored Posterior for Robust and Efficient Risk Evaluation |
0 |
0 |
0 |
20 |
0 |
2 |
6 |
30 |

Partially Censored Posterior for robust and efficient risk evaluation |
0 |
1 |
1 |
2 |
0 |
1 |
6 |
16 |

Possibly Ill-behaved Posteriors in Econometric Models |
0 |
0 |
0 |
44 |
1 |
2 |
3 |
260 |

Posterior-Predictive Evidence on US Inflation using Extended New Keynesian Phillips Curve Models with Non-filtered Data |
0 |
0 |
0 |
34 |
0 |
2 |
3 |
115 |

Posterior-Predictive Evidence on US Inflation using Extended Phillips Curve Models with non-filtered Data |
1 |
1 |
1 |
60 |
1 |
1 |
6 |
212 |

Posterior-Predictive Evidence on US Inflation using Phillips Curve Models with Non-Filtered Time Series |
0 |
0 |
0 |
82 |
2 |
7 |
18 |
199 |

Predictive gains from forecast combinations using time-varying model weights |
0 |
0 |
1 |
28 |
0 |
2 |
8 |
106 |

Quantifying time-varying forecast uncertainty and risk for the real price of oil |
11 |
11 |
11 |
11 |
7 |
7 |
7 |
7 |

Quantifying time-varying forecast uncertainty and risk for the real price of oil |
2 |
5 |
9 |
9 |
2 |
13 |
23 |
23 |

Quantifying time-varying forecast uncertainty and risk for the real price of oil |
0 |
0 |
24 |
24 |
2 |
8 |
25 |
25 |

Return and Risk of Pairs Trading using a Simulation-based Bayesian Procedure for Predicting Stable Ratios of Stock Prices |
0 |
0 |
3 |
38 |
0 |
1 |
10 |
151 |

Robust Optimization of the Equity Momentum Strategy |
0 |
0 |
0 |
104 |
0 |
2 |
9 |
356 |

SOME ADVANCES IN BAYESIAN ESTIMATION METHODS USING MONTE CARLO INTEGRATION |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
4 |

Simulation based Bayesian econometric inference: principles and some recent computational advances |
1 |
1 |
1 |
29 |
1 |
1 |
1 |
96 |

Simulation based bayesian econometric inference: principles and some recent computational advances |
0 |
0 |
2 |
17 |
0 |
1 |
3 |
55 |

Some advances in Bayesian estimations methods using Monte Carlo Integration |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
5 |

Testing for Integration using Evolving Trend and Seasonals Models: A Bayesian Approach |
0 |
0 |
0 |
98 |
0 |
3 |
3 |
324 |

Testing for Integration using Evolving Trend and Seasonals Models: A Bayesian Approach |
0 |
0 |
0 |
117 |
0 |
1 |
2 |
642 |

Testing for integration using evolving trend and seasonal models: A Bayesian approach |
0 |
0 |
0 |
7 |
0 |
1 |
3 |
95 |

The AdMit Package |
0 |
0 |
0 |
11 |
0 |
1 |
4 |
67 |

The Evolution of Forecast Density Combinations in Economics |
1 |
3 |
14 |
127 |
6 |
13 |
43 |
186 |

The R Package MitISEM: Mixture of Student-t Distributions using Importance Sampling Weighted Expectation Maximization for Efficient and Robust Simulation |
0 |
0 |
0 |
44 |
0 |
2 |
4 |
197 |

The R package MitISEM: Efficient and robust simulation procedures for Bayesian inference |
0 |
0 |
0 |
28 |
0 |
0 |
1 |
36 |

The R package MitISEM: efficient and robust simulation procedures for Bayesian inference |
0 |
0 |
0 |
25 |
0 |
1 |
2 |
147 |

The R-package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference |
0 |
0 |
0 |
8 |
0 |
1 |
2 |
49 |

The Value of Structural Information in the VAR Model |
0 |
0 |
0 |
77 |
0 |
0 |
0 |
267 |

The Value of Structural Information in the VAR Model |
0 |
0 |
0 |
69 |
0 |
0 |
2 |
306 |

The value of structural information in the VAR model |
0 |
0 |
0 |
15 |
0 |
2 |
2 |
68 |

Time-varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies |
0 |
0 |
3 |
62 |
4 |
6 |
14 |
77 |

Time-varying Combinations of Predictive Densities using Nonlinear Filtering |
0 |
0 |
3 |
77 |
1 |
3 |
13 |
129 |

To Bridge, to Warp or to Wrap? A Comparative Study of Monte Carlo Methods for Efficient Evaluation of Marginal Likelihoods |
0 |
0 |
0 |
50 |
1 |
1 |
1 |
194 |

Trends and cycles in economic time series: A Bayesian approach |
0 |
1 |
3 |
215 |
0 |
3 |
9 |
410 |

Twentieth century shocks, trends and cycles in industrialized nations |
0 |
0 |
1 |
5 |
0 |
2 |
5 |
47 |

Valuing structure, model uncertainty and model averaging in vector autoregressive processes |
0 |
0 |
0 |
17 |
0 |
0 |
0 |
47 |

Weakly informative priors and well behaved Bayes factors |
0 |
0 |
0 |
9 |
0 |
1 |
2 |
80 |

Total Working Papers |
20 |
40 |
226 |
9,600 |
79 |
316 |
1,109 |
34,737 |