| Working Paper |
File Downloads |
Abstract Views |
| Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
| A Dynamic Bivariate Poisson Model for Analysing and Forecasting Match Results in the English Premier League |
1 |
2 |
4 |
261 |
4 |
5 |
22 |
600 |
| A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations |
0 |
0 |
0 |
64 |
0 |
0 |
1 |
195 |
| A Dynamic Yield Curve Model with Stochastic Volatility and Non-Gaussian Interactions: An Empirical Study of Non-standard Monetary Policy in the Euro Area |
1 |
1 |
2 |
77 |
4 |
8 |
25 |
179 |
| A Forty Year Assessment of Forecasting the Boat Race |
1 |
3 |
4 |
76 |
1 |
3 |
5 |
74 |
| A General Framework for Observation Driven Time-Varying Parameter Models |
0 |
1 |
2 |
170 |
1 |
8 |
10 |
401 |
| A General Framework for Observation Driven Time-Varying Parameter Models |
0 |
0 |
0 |
113 |
0 |
1 |
4 |
282 |
| A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk |
0 |
0 |
1 |
122 |
0 |
0 |
2 |
460 |
| A Note on “Continuous Invertibility and Stable QML Estimation of the EGARCH(1,1) Model” |
0 |
0 |
0 |
18 |
0 |
1 |
1 |
23 |
| A Time-Varying Parameter Model for Local Explosions |
0 |
0 |
2 |
69 |
0 |
8 |
25 |
108 |
| A statistical model of the global carbon budget |
1 |
1 |
5 |
32 |
2 |
5 |
15 |
67 |
| Accelerating GARCH and Score-Driven Models: Optimality, Estimation and Forecasting |
0 |
0 |
4 |
39 |
0 |
2 |
8 |
58 |
| An Hourly Periodic State Space Model for Modelling French National Electricity Load |
0 |
0 |
1 |
230 |
0 |
0 |
4 |
569 |
| An efficient and simple simulation smoother for state space time series analysis |
0 |
0 |
0 |
179 |
4 |
11 |
40 |
1,190 |
| Analyzing the Term Structure of Interest Rates using the Dynamic Nelson-Siegel Model with Time-Varying Parameters |
1 |
2 |
11 |
299 |
2 |
5 |
29 |
716 |
| Bayesian Dynamic Modeling of High-Frequency Integer Price Changes |
0 |
0 |
1 |
60 |
0 |
0 |
4 |
49 |
| Bayesian Risk Forecasting for Long Horizons |
0 |
0 |
0 |
37 |
0 |
1 |
4 |
90 |
| Beta observation-driven models with exogenous regressors: a joint analysis of realized correlation and leverage effects |
0 |
0 |
1 |
41 |
0 |
1 |
4 |
47 |
| Business and Default Cycles for Credit Risk |
0 |
1 |
4 |
888 |
1 |
11 |
29 |
1,910 |
| Common and Idiosyncratic Conditional Volatility Factors: Theory and Empirical Evidence |
0 |
0 |
0 |
20 |
0 |
0 |
0 |
21 |
| Common business and housing market cycles in the Euro area from a multivariate decomposition |
0 |
0 |
0 |
187 |
2 |
10 |
22 |
427 |
| Computing Observation Weights for Signal Extraction and Filtering |
0 |
0 |
0 |
259 |
0 |
1 |
1 |
620 |
| Constructing seasonally adjusted data with time-varying confidence intervals |
0 |
0 |
0 |
24 |
0 |
0 |
1 |
98 |
| Convergence in European GDP Series |
0 |
0 |
2 |
490 |
0 |
1 |
6 |
2,263 |
| Credit Cycles and Macro Fundamentals |
0 |
0 |
2 |
285 |
1 |
5 |
15 |
861 |
| Credit cycles and macro fundamentals |
1 |
1 |
1 |
180 |
2 |
7 |
18 |
594 |
| Dynamic Factor Analysis in The Presence of Missing Data |
1 |
1 |
7 |
205 |
2 |
3 |
17 |
403 |
| Dynamic Factor Models with Clustered Loadings: Forecasting Education Flows using Unemployment Data |
0 |
0 |
1 |
18 |
1 |
1 |
6 |
56 |
| Dynamic Factor Models with Smooth Loadings for Analyzing the Term Structure of Interest Rates |
0 |
0 |
0 |
66 |
0 |
1 |
1 |
223 |
| Dynamic factor models with macro, frailty and industry effects for US default counts: the credit crisis of 2008 |
0 |
0 |
0 |
54 |
0 |
0 |
1 |
157 |
| Dynamic term structure models with score-driven time-varying parameters: estimation and forecasting |
0 |
3 |
4 |
72 |
0 |
4 |
7 |
94 |
| Empirical Bayes Methods for Dynamic Factor Models |
0 |
0 |
0 |
101 |
0 |
1 |
3 |
109 |
| Estimating Systematic Continuous-time Trends in Recidivism using a Non-Gaussian Panel Data Model |
0 |
0 |
2 |
71 |
0 |
0 |
4 |
511 |
| Estimation of final standings in football competitions with premature ending: the case of COVID-19 |
0 |
0 |
0 |
8 |
0 |
1 |
11 |
59 |
| Exact Score for Time Series Models in State Space Form (Now published in Biometrika (1992), 79, 4, pp.283-6.) |
0 |
0 |
0 |
0 |
0 |
0 |
7 |
42 |
| Extracting Business Cycles using Semi-parametric Time-varying Spectra with Applications to US Macroeconomic Time Series |
0 |
0 |
0 |
102 |
0 |
1 |
1 |
314 |
| Extracting a Robust U.S. Business Cycle Using a Time-Varying Multivariate Model-Based Bandpass Filter |
0 |
0 |
1 |
77 |
5 |
7 |
9 |
161 |
| Fast Efficient Importance Sampling by State Space Methods |
0 |
0 |
0 |
79 |
0 |
1 |
2 |
185 |
| Fast Estimation of Parameters in State Space Models |
0 |
0 |
0 |
0 |
1 |
1 |
3 |
854 |
| Fast Filtering and Smoothing for Multivariate State Space Models |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
3 |
| Fast Filtering and Smoothing for Multivariate State Space Models |
0 |
0 |
0 |
12 |
0 |
2 |
3 |
50 |
| Feasible Invertibility Conditions and Maximum Likelihood Estimation for Observation-Driven Models |
0 |
0 |
0 |
14 |
1 |
1 |
2 |
37 |
| Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
22 |
| Finding the European crime drop using a panel data model with stochastic trends |
0 |
0 |
10 |
10 |
0 |
0 |
1 |
1 |
| Forecasting Cross-Sections of Frailty-Correlated Default |
0 |
0 |
0 |
73 |
1 |
1 |
3 |
266 |
| Forecasting Daily Time Series using Periodic Unobserved Components Time Series Models |
0 |
0 |
2 |
351 |
1 |
2 |
6 |
1,134 |
| Forecasting Daily Variability of the S&P 100 Stock Index using Historical, Realised and Implied Volatility Measurements |
1 |
2 |
3 |
953 |
1 |
3 |
9 |
2,485 |
| Forecasting Football Match Results in National League Competitions Using Score-Driven Time Series Models |
0 |
0 |
3 |
144 |
1 |
6 |
23 |
155 |
| Forecasting Interest Rates with Shifting Endpoints |
0 |
1 |
3 |
79 |
0 |
1 |
7 |
196 |
| Forecasting Macroeconomic Variables using Collapsed Dynamic Factor Analysis |
0 |
0 |
0 |
180 |
0 |
0 |
3 |
405 |
| Forecasting daily variability of the S\&P 100 stock index using historical, realised and implied volatility measurements |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
1,096 |
| Forecasting economic time series using score-driven dynamic models with mixed-data sampling |
0 |
0 |
0 |
54 |
0 |
0 |
1 |
69 |
| Forecasting in a changing world: from the great recession to the COVID-19 pandemic |
0 |
0 |
1 |
88 |
1 |
3 |
11 |
111 |
| Forecasting the U.S. Term Structure of Interest Rates using a Macroeconomic Smooth Dynamic Factor Model |
0 |
0 |
0 |
90 |
1 |
2 |
5 |
178 |
| Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility |
0 |
0 |
0 |
810 |
0 |
1 |
1 |
2,112 |
| Generalized Autoregressive Method of Moments |
1 |
1 |
1 |
74 |
1 |
2 |
10 |
129 |
| Generalized Dynamic Panel Data Models with Random Effects for Cross-Section and Time |
0 |
0 |
0 |
72 |
0 |
0 |
0 |
198 |
| Global Credit Risk: World, Country and Industry Factors |
0 |
0 |
0 |
26 |
1 |
2 |
14 |
140 |
| Global credit risk: world country and industry factors |
0 |
0 |
0 |
31 |
0 |
0 |
4 |
101 |
| In-Sample Bounds for Time-Varying Parameters of Observation Driven Models |
0 |
0 |
1 |
15 |
0 |
0 |
1 |
53 |
| In-Sample Confidence Bands and Out-of-Sample Forecast Bands for Time-Varying Parameters in Observation Driven Models |
1 |
1 |
3 |
61 |
2 |
2 |
5 |
63 |
| Information Theoretic Optimality of Observation Driven Time Series Models |
0 |
0 |
0 |
45 |
0 |
0 |
2 |
88 |
| Interaction between Supply and Demand Shocks in Production and Employment |
0 |
0 |
0 |
387 |
0 |
0 |
0 |
3,792 |
| Interaction between supply and demand in production and employment |
0 |
0 |
0 |
23 |
0 |
0 |
0 |
165 |
| Intervention Time Series Analysis of Crime Rates |
0 |
0 |
2 |
710 |
0 |
1 |
4 |
2,350 |
| Intraday Stochastic Volatility in Discrete Price Changes: the Dynamic Skellam Model |
0 |
0 |
0 |
53 |
0 |
1 |
4 |
88 |
| Intraday Stock Price Dependence using Dynamic Discrete Copula Distributions |
0 |
0 |
0 |
35 |
0 |
0 |
2 |
94 |
| Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models |
0 |
0 |
1 |
72 |
0 |
0 |
1 |
62 |
| Joint Modelling and Estimation of Global and Local Cross-Sectional Dependence in Large Panels |
0 |
0 |
1 |
26 |
0 |
2 |
7 |
36 |
| Likelihood Functions for State Space Models with Diffuse Initial Conditions |
0 |
1 |
2 |
165 |
0 |
5 |
11 |
488 |
| Likelihood-based Analysis for Dynamic Factor Models |
0 |
0 |
2 |
289 |
1 |
3 |
9 |
559 |
| Long Memory Dynamics for Multivariate Dependence under Heavy Tails |
0 |
0 |
1 |
47 |
2 |
4 |
10 |
157 |
| Long Memory Modelling of Inflation with Stochastic Variance and Structural Breaks |
0 |
0 |
1 |
133 |
0 |
0 |
2 |
344 |
| Long memory modelling of inflation with stochastic variance and structural breaks |
0 |
0 |
0 |
47 |
0 |
1 |
3 |
206 |
| Low Frequency and Weighted Likelihood Solutions for Mixed Frequency Dynamic Factor Models |
0 |
1 |
3 |
50 |
0 |
1 |
4 |
90 |
| Macro, Industry and Frailty Effects in Defaults: The 2008 Credit Crisis in Perspective |
0 |
0 |
1 |
57 |
0 |
1 |
4 |
159 |
| Maximum Likelihood Estimation for Score-Driven Models |
0 |
0 |
2 |
58 |
1 |
1 |
9 |
182 |
| Maximum Likelihood Estimation for correctly Specified Generalized Autoregressive Score Models: Feedback Effects, Contraction Conditions and Asymptotic Properties |
0 |
1 |
1 |
48 |
0 |
2 |
5 |
109 |
| Maximum Likelihood Estimation of Stochastic Volatility Models |
0 |
0 |
0 |
1,036 |
1 |
1 |
6 |
2,504 |
| Maximum likelihood estimation for dynamic factor models with missing data |
0 |
0 |
1 |
9 |
0 |
2 |
6 |
79 |
| Measuring Asymmetric Stochastic Cycle Components in U.S. Macroeconomic Time Series |
0 |
1 |
3 |
222 |
0 |
1 |
3 |
761 |
| Measuring Financial Cycles in a Model-Based Analysis: Empirical Evidence for the United States and the Euro Area |
1 |
1 |
1 |
89 |
2 |
2 |
5 |
125 |
| Measuring Synchronisation and Convergence of Business Cycles |
0 |
0 |
1 |
363 |
0 |
0 |
3 |
886 |
| Messy Time Series: A Unified Approach - (Now published in 'Advances in Econometrics', 13 (1998)pp.103-143.) |
0 |
0 |
0 |
0 |
3 |
3 |
13 |
98 |
| Missing Observations in Observation-Driven Time Series Models |
0 |
0 |
0 |
45 |
0 |
1 |
4 |
75 |
| Model-based Business Cycle and Financial Cycle Decomposition for Europe and the U.S |
0 |
2 |
3 |
92 |
1 |
10 |
32 |
220 |
| Model-based Measurement of Actual Volatility in High-Frequency Data |
0 |
0 |
0 |
232 |
0 |
1 |
2 |
761 |
| Model-based Measurement of Latent Risk in Time Series with Applications |
0 |
1 |
2 |
153 |
0 |
1 |
2 |
675 |
| Modeling Dynamic Volatilities and Correlations under Skewness and Fat Tails |
0 |
0 |
1 |
52 |
0 |
0 |
3 |
148 |
| Modeling Trigonometric Seasonal Components for Monthly Economic Time Series |
0 |
0 |
1 |
76 |
0 |
0 |
4 |
233 |
| Modeling, Forecasting, and Nowcasting U.S. CO2 Emissions Using Many Macroeconomic Predictors |
1 |
1 |
1 |
87 |
2 |
4 |
8 |
123 |
| Modelling bid-ask spreads in competitive dealership markets |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
22 |
| Modelling bid-ask spreads in competitive dealership markets |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
5 |
| Models with Time-varying Mean and Variance: A Robust Analysis of U.S. Industrial Production |
0 |
0 |
0 |
67 |
1 |
3 |
4 |
178 |
| Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models |
0 |
0 |
0 |
54 |
0 |
0 |
3 |
164 |
| Multivariate Structural Time Series Models - (Now published in 'System Dynamics in Economic and Financial Models', CHeij, H Schumacher, B Hanzon and C Praagman (eds.) John Wiley & Sons, Chichester (1997), pp.269-298.) |
0 |
0 |
0 |
0 |
0 |
3 |
10 |
115 |
| Nowcasting and Forecasting Economic Growth in the Euro Area using Principal Components |
0 |
0 |
5 |
107 |
0 |
2 |
11 |
112 |
| Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models |
1 |
1 |
3 |
75 |
1 |
1 |
3 |
149 |
| Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk |
0 |
0 |
2 |
56 |
0 |
0 |
2 |
153 |
| Observation driven mixed-measurement dynamic factor models with an application to credit risk |
0 |
0 |
3 |
44 |
0 |
0 |
4 |
150 |
| On Importance Sampling for State Space Models |
0 |
0 |
2 |
181 |
0 |
1 |
4 |
524 |
| Optimal Formulations for Nonlinear Autoregressive Processes |
0 |
0 |
1 |
52 |
0 |
0 |
4 |
96 |
| Partially Censored Posterior for Robust and Efficient Risk Evaluation |
0 |
0 |
0 |
20 |
0 |
0 |
0 |
33 |
| Partially Censored Posterior for robust and efficient risk evaluation |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
17 |
| Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices |
0 |
0 |
0 |
352 |
0 |
1 |
2 |
952 |
| Periodic Heteroskedastic RegARFIMA models for daily electricity spot prices |
1 |
1 |
1 |
177 |
1 |
1 |
2 |
574 |
| Periodic Seasonal Reg-ARFIMA-GARCH Models for Daily Electricity Spot Prices |
0 |
0 |
2 |
477 |
0 |
0 |
6 |
1,218 |
| Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment |
1 |
1 |
1 |
113 |
1 |
1 |
2 |
325 |
| Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models |
0 |
0 |
2 |
92 |
2 |
2 |
10 |
230 |
| Pro-Cyclicality, Empirical Credit Cycles, and Capital Buffer Formation |
0 |
0 |
0 |
307 |
1 |
1 |
3 |
812 |
| Realized Wishart-GARCH: A Score-driven Multi-Asset Volatility Model |
0 |
0 |
4 |
83 |
0 |
1 |
7 |
92 |
| Regime switches in the volatility and correlation of financial institutions |
0 |
1 |
2 |
100 |
0 |
3 |
6 |
185 |
| Round-the-Clock Price Discovery for Cross-Listed Stocks: US-Dutch Evidence |
0 |
1 |
3 |
245 |
0 |
1 |
4 |
1,048 |
| Seasonality with Trend and Cycle Interactions in Unobserved Components Models |
0 |
0 |
3 |
220 |
0 |
3 |
13 |
641 |
| Signal Extraction and the Formulation of Unobserved Components Models |
0 |
0 |
0 |
0 |
1 |
1 |
2 |
8 |
| Signal Extraction and the Formulation of Unobserved Components Models |
0 |
0 |
1 |
19 |
0 |
0 |
2 |
64 |
| Smooth Dynamic Factor Analysis with an Application to the U.S. Term Structure of Interest Rates |
0 |
1 |
1 |
97 |
0 |
1 |
1 |
217 |
| Spillover Dynamics for Systemic Risk Measurement using Spatial Financial Time Series Models |
0 |
1 |
2 |
65 |
0 |
1 |
5 |
105 |
| Spillover dynamics for systemic risk measurement using spatial financial time series models |
1 |
1 |
1 |
45 |
1 |
1 |
4 |
142 |
| Spline Smoothing over Difficult Regions |
0 |
0 |
1 |
65 |
0 |
0 |
1 |
188 |
| Spot Variance Path Estimation and its Application to High Frequency Jump Testing |
0 |
1 |
3 |
56 |
0 |
3 |
7 |
167 |
| Stationarity and Ergodicity of Univariate Generalized Autoregressive Score Processes |
0 |
0 |
0 |
58 |
0 |
0 |
3 |
125 |
| Statistical Algorithms for Models in State Space Using SsfPack 2.2 |
0 |
0 |
2 |
20 |
0 |
0 |
8 |
101 |
| Statistical Algorithms for Models in State Space Using SsfPack 2.2 |
0 |
0 |
0 |
3 |
0 |
1 |
2 |
8 |
| Stock Index Volatility Forecasting with High Frequency Data |
0 |
0 |
2 |
857 |
0 |
0 |
7 |
2,208 |
| Structural Intervention Time Series Analysis of Crime Rates: The Impact of Sentence Reform in Virginia |
0 |
0 |
1 |
51 |
0 |
0 |
1 |
207 |
| Systemic Risk Diagnostics |
0 |
0 |
1 |
93 |
0 |
1 |
2 |
211 |
| Systemic risk diagnostics: coincident indicators and early warning signals |
0 |
0 |
1 |
147 |
0 |
1 |
4 |
460 |
| Temporal, Spatial, Economic and Crime Factors in Illicit Drug Usage across European Cities |
0 |
0 |
1 |
27 |
0 |
0 |
2 |
55 |
| Testing for Parameter Instability in Competing Modeling Frameworks |
0 |
1 |
3 |
21 |
0 |
1 |
4 |
74 |
| Testing the Assumptions Behind the Use of Importance Sampling |
0 |
0 |
0 |
105 |
1 |
1 |
2 |
491 |
| The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures |
0 |
0 |
0 |
76 |
0 |
0 |
2 |
123 |
| The Dynamic Factor Network Model with an Application to Global Credit-Risk |
0 |
0 |
0 |
14 |
0 |
0 |
0 |
56 |
| The Dynamic Skellam Model with Applications |
1 |
1 |
3 |
34 |
2 |
3 |
7 |
131 |
| The Effect of the Great Moderation on the U.S. Business Cycle in a Time-varying Multivariate Trend-cycle Model |
0 |
0 |
1 |
86 |
0 |
1 |
2 |
234 |
| The Information in Systemic Risk Rankings |
0 |
1 |
3 |
28 |
1 |
2 |
5 |
91 |
| The Modelling and Seasonal Adjustment of Weekly Observations - (Now published in 'Journal of Business and Economic Statistics', 15 (1997), pp.354-368.) |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
33 |
| The Multi-State Latent Factor Intensity Model for Credit Rating Transitions |
0 |
0 |
1 |
235 |
0 |
0 |
8 |
676 |
| The Stochastic Volatility in Mean Model |
0 |
0 |
0 |
492 |
0 |
0 |
4 |
1,097 |
| The analysis and forecasting of ATP tennis matches using a high-dimensional dynamic model |
0 |
1 |
4 |
78 |
0 |
3 |
22 |
89 |
| The dynamic factor network model with an application to global credit risk |
0 |
0 |
0 |
40 |
0 |
0 |
1 |
124 |
| The information in systemic risk rankings |
0 |
0 |
2 |
39 |
1 |
1 |
6 |
138 |
| Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives |
0 |
0 |
0 |
16 |
0 |
0 |
1 |
63 |
| Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
| Time Series Modelling of Daily Tax Revenues |
0 |
0 |
0 |
329 |
0 |
1 |
1 |
880 |
| Time Series Models with a Common Stochastic Variance for Analysing Economic Time Series |
0 |
0 |
1 |
482 |
0 |
3 |
5 |
1,437 |
| Time Varying Transition Probabilities for Markov Regime Switching Models |
0 |
1 |
4 |
125 |
11 |
14 |
31 |
420 |
| Time-Series Modelling of Daily Tax Revenues |
0 |
0 |
0 |
290 |
0 |
0 |
0 |
1,068 |
| Time-varying state correlations in state space models and their estimation via indirect inference |
0 |
1 |
4 |
38 |
0 |
1 |
8 |
29 |
| Tracking Growth and the Business Cycle: a Stochastic Common Cycle Model for the Euro Area |
0 |
0 |
0 |
44 |
0 |
0 |
2 |
217 |
| Tracking Growth and the Business Cycle: a Stochastic Common Cycle Model for the Euro Area |
0 |
0 |
0 |
272 |
0 |
1 |
2 |
949 |
| Unobserved Components with Stochastic Volatility in U.S. Inflation: Estimation and Signal Extraction |
0 |
0 |
2 |
85 |
0 |
1 |
5 |
138 |
| Vector Autoregressions with Dynamic Factor Coefficients and Conditionally Heteroskedastic Errors |
1 |
1 |
3 |
37 |
3 |
3 |
9 |
31 |
| Total Working Papers |
18 |
46 |
202 |
20,309 |
85 |
262 |
926 |
60,382 |
| Journal Article |
File Downloads |
Abstract Views |
| Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
| A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations |
0 |
0 |
1 |
56 |
0 |
1 |
4 |
196 |
| A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations |
0 |
0 |
0 |
26 |
0 |
0 |
2 |
98 |
| A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk |
0 |
0 |
1 |
51 |
0 |
0 |
3 |
198 |
| A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League |
0 |
1 |
7 |
54 |
0 |
3 |
31 |
216 |
| A non-Gaussian generalization of the Airline model for robust seasonal adjustment |
0 |
1 |
2 |
68 |
0 |
1 |
2 |
302 |
| A time-varying parameter model for local explosions |
0 |
1 |
1 |
3 |
1 |
4 |
10 |
15 |
| Accelerating score-driven time series models |
0 |
1 |
2 |
15 |
0 |
1 |
5 |
74 |
| Amendments and Corrections |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
9 |
| An hourly periodic state space model for modelling French national electricity load |
0 |
0 |
1 |
49 |
0 |
0 |
3 |
194 |
| Analyzing the Term Structure of Interest Rates Using the Dynamic Nelson–Siegel Model With Time-Varying Parameters |
1 |
2 |
2 |
45 |
1 |
3 |
3 |
154 |
| Bayesian Dynamic Modeling of High-Frequency Integer Price Changes |
0 |
0 |
2 |
3 |
0 |
0 |
3 |
24 |
| Business and default cycles for credit risk |
0 |
0 |
1 |
452 |
0 |
1 |
8 |
1,226 |
| Business and default cycles for credit risk |
0 |
0 |
2 |
3 |
0 |
0 |
2 |
10 |
| Computing observation weights for signal extraction and filtering |
0 |
2 |
7 |
167 |
0 |
2 |
16 |
416 |
| Constructing Seasonally Adjusted Data with Time‐varying Confidence Intervals |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
11 |
| Convergence in European GDP series: a multivariate common converging trend-cycle decomposition |
0 |
0 |
1 |
176 |
0 |
0 |
2 |
624 |
| Credit cycles and macro fundamentals |
0 |
0 |
1 |
198 |
2 |
7 |
15 |
578 |
| Detecting shocks: Outliers and breaks in time series |
0 |
0 |
3 |
130 |
1 |
1 |
7 |
338 |
| Diagnostic Checking of Unobserved-Components Time Series Models |
0 |
0 |
0 |
0 |
1 |
3 |
10 |
765 |
| Discussion of ‘MCMC‐based inference’ by R. Paap |
0 |
0 |
0 |
11 |
1 |
1 |
1 |
61 |
| Dynamic Factor Models With Macro, Frailty, and Industry Effects for U.S. Default Counts: The Credit Crisis of 2008 |
1 |
1 |
2 |
27 |
1 |
2 |
4 |
122 |
| Dynamic discrete copula models for high‐frequency stock price changes |
0 |
0 |
0 |
1 |
0 |
1 |
5 |
22 |
| Dynamic factor models with clustered loadings: Forecasting education flows using unemployment data |
0 |
0 |
2 |
4 |
0 |
0 |
3 |
7 |
| Dynamic factors in periodic time-varying regressions with an application to hourly electricity load modelling |
0 |
0 |
0 |
12 |
0 |
0 |
1 |
54 |
| Economic Trends and Cycles in Crime: A Study for England and Wales |
1 |
1 |
1 |
72 |
2 |
2 |
4 |
257 |
| Empirical Bayes Methods for Dynamic Factor Models |
0 |
0 |
0 |
13 |
0 |
0 |
2 |
110 |
| Empirical credit cycles and capital buffer formation |
0 |
0 |
1 |
141 |
0 |
1 |
4 |
393 |
| Estimating Stochastic Volatility Models: A Comparison of Two Importance Samplers |
0 |
0 |
1 |
176 |
0 |
0 |
1 |
372 |
| Estimating systematic continuous‐time trends in recidivism using a non‐Gaussian panel data model |
0 |
0 |
0 |
16 |
0 |
0 |
1 |
105 |
| Estimation of stochastic volatility models via Monte Carlo maximum likelihood |
1 |
2 |
7 |
478 |
2 |
4 |
19 |
1,071 |
| Exact maximum likelihood estimation for non-stationary periodic time series models |
0 |
0 |
0 |
32 |
0 |
0 |
1 |
182 |
| Exponentionally weighted methods for forecasting intraday time series with multiple seasonal cycles: Comments |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
37 |
| Extracting a robust US business cycle using a time-varying multivariate model-based bandpass filter |
0 |
0 |
0 |
132 |
0 |
1 |
4 |
355 |
| Fast Filtering and Smoothing for Multivariate State Space Models |
0 |
1 |
1 |
4 |
0 |
5 |
9 |
21 |
| Filtering and smoothing of state vector for diffuse state‐space models |
0 |
4 |
10 |
374 |
1 |
7 |
16 |
697 |
| Forecasting and nowcasting economic growth in the euro area using factor models |
2 |
2 |
3 |
21 |
2 |
2 |
6 |
81 |
| Forecasting daily time series using periodic unobserved components time series models |
0 |
0 |
1 |
51 |
1 |
1 |
3 |
141 |
| Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements |
0 |
2 |
17 |
387 |
0 |
3 |
32 |
1,071 |
| Forecasting economic time series using score-driven dynamic models with mixed-data sampling |
0 |
0 |
0 |
2 |
1 |
1 |
4 |
27 |
| Forecasting football match results in national league competitions using score-driven time series models |
0 |
3 |
8 |
41 |
3 |
9 |
26 |
174 |
| Forecasting interest rates with shifting endpoints |
0 |
0 |
2 |
25 |
0 |
0 |
3 |
95 |
| Forecasting macroeconomic variables using collapsed dynamic factor analysis |
0 |
1 |
3 |
58 |
0 |
3 |
16 |
177 |
| Forecasting the US term structure of interest rates using a macroeconomic smooth dynamic factor model |
0 |
0 |
0 |
19 |
1 |
2 |
5 |
95 |
| GENERALIZED AUTOREGRESSIVE SCORE MODELS WITH APPLICATIONS |
1 |
3 |
10 |
94 |
3 |
9 |
29 |
311 |
| Generalized dynamic panel data models with random effects for cross-section and time |
0 |
0 |
0 |
41 |
0 |
0 |
0 |
222 |
| Global Credit Risk: World, Country and Industry Factors |
0 |
0 |
0 |
7 |
1 |
2 |
6 |
85 |
| In-sample confidence bands and out-of-sample forecast bands for time-varying parameters in observation-driven models |
2 |
2 |
4 |
26 |
2 |
2 |
8 |
86 |
| Information-theoretic optimality of observation-driven time series models for continuous responses |
1 |
2 |
3 |
18 |
1 |
3 |
5 |
56 |
| Interaction between structural and cyclical shocks in production and employment |
0 |
0 |
0 |
21 |
0 |
0 |
0 |
69 |
| Intervention time series analysis of crime rates: The case of sentence reform in Virginia |
0 |
0 |
0 |
22 |
0 |
0 |
1 |
114 |
| Intraday Stochastic Volatility in Discrete Price Changes: The Dynamic Skellam Model |
0 |
0 |
0 |
1 |
0 |
1 |
4 |
27 |
| Joint Bayesian Analysis of Parameters and States in Nonlinear non‐Gaussian State Space Models |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
19 |
| Joint Decomposition of Business and Financial Cycles: Evidence from Eight Advanced Economies |
0 |
0 |
5 |
7 |
0 |
1 |
10 |
14 |
| Kalman filtering and smoothing for model‐based signal extraction that depend on time‐varying spectra |
0 |
0 |
0 |
35 |
0 |
0 |
0 |
114 |
| Likelihood functions for state space models with diffuse initial conditions |
0 |
0 |
4 |
31 |
0 |
1 |
7 |
108 |
| Likelihood‐based dynamic factor analysis for measurement and forecasting |
0 |
0 |
0 |
12 |
1 |
1 |
2 |
66 |
| Long memory dynamics for multivariate dependence under heavy tails |
0 |
0 |
0 |
13 |
1 |
2 |
3 |
89 |
| Long memory with stochastic variance model: A recursive analysis for US inflation |
0 |
0 |
0 |
10 |
0 |
0 |
0 |
54 |
| Long-term forecasting of El Niño events via dynamic factor simulations |
1 |
2 |
3 |
13 |
2 |
3 |
5 |
36 |
| Maximum likelihood estimation for dynamic factor models with missing data |
0 |
1 |
8 |
109 |
0 |
1 |
12 |
289 |
| Maximum likelihood estimation for score-driven models |
1 |
2 |
4 |
6 |
2 |
3 |
14 |
20 |
| Measuring Synchronization and Convergence of Business Cycles for the Euro area, UK and US* |
0 |
0 |
0 |
141 |
0 |
0 |
1 |
321 |
| Measuring financial cycles in a model-based analysis: Empirical evidence for the United States and the euro area |
1 |
1 |
2 |
65 |
2 |
4 |
15 |
204 |
| Missing observations in observation-driven time series models |
1 |
1 |
3 |
6 |
2 |
2 |
11 |
19 |
| Modeling Around-the-Clock Price Discovery for Cross-Listed Stocks Using State Space Methods |
0 |
0 |
1 |
80 |
0 |
2 |
5 |
202 |
| Modeling frailty-correlated defaults using many macroeconomic covariates |
0 |
0 |
2 |
64 |
0 |
2 |
8 |
249 |
| Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors |
1 |
2 |
3 |
14 |
3 |
5 |
16 |
54 |
| Modelling trigonometric seasonal components for monthly economic time series |
0 |
0 |
1 |
51 |
1 |
2 |
3 |
253 |
| Model‐based measurement of latent risk in time series with applications |
0 |
1 |
1 |
28 |
0 |
1 |
2 |
119 |
| Modified efficient importance sampling for partially non‐Gaussian state space models |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
9 |
| Monte Carlo Estimation for Nonlinear Non-Gaussian State Space Models |
0 |
1 |
2 |
111 |
0 |
2 |
4 |
229 |
| Monte Carlo Likelihood Estimation for Three Multivariate Stochastic Volatility Models |
0 |
1 |
1 |
77 |
0 |
1 |
2 |
204 |
| Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models |
0 |
0 |
0 |
5 |
1 |
1 |
3 |
67 |
| Multivariate non‐linear time series modelling of exposure and risk in road safety research |
0 |
0 |
0 |
15 |
0 |
1 |
1 |
64 |
| Nonlinear autoregressive models with optimality properties |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
13 |
| Nowcasting and forecasting global financial sector stress and credit market dislocation |
0 |
0 |
0 |
19 |
0 |
0 |
1 |
88 |
| Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State-Space Models |
0 |
0 |
0 |
15 |
0 |
1 |
3 |
59 |
| Observation-Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk |
0 |
0 |
5 |
41 |
0 |
2 |
10 |
188 |
| Partially censored posterior for robust and efficient risk evaluation |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
15 |
| Periodic Seasonal Reg-ARFIMAGARCH Models for Daily Electricity Spot Prices |
0 |
1 |
5 |
113 |
0 |
1 |
9 |
287 |
| Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment* |
0 |
0 |
0 |
21 |
0 |
0 |
2 |
113 |
| Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models |
0 |
1 |
6 |
30 |
1 |
3 |
20 |
135 |
| Realized Wishart-GARCH: A Score-driven Multi-Asset Volatility Model |
0 |
0 |
2 |
17 |
0 |
0 |
10 |
78 |
| SMOOTH DYNAMIC FACTOR ANALYSIS WITH APPLICATION TO THE US TERM STRUCTURE OF INTEREST RATES |
0 |
1 |
1 |
13 |
0 |
2 |
5 |
56 |
| Seasonality with trend and cycle interactions in unobserved components models |
0 |
0 |
0 |
36 |
0 |
1 |
3 |
148 |
| Signal extraction and the formulation of unobserved components models |
0 |
0 |
0 |
4 |
0 |
0 |
1 |
1,460 |
| Special Issue on Nonlinear Modelling and Financial Econometrics |
0 |
0 |
0 |
30 |
0 |
0 |
0 |
88 |
| Spillover dynamics for systemic risk measurement using spatial financial time series models |
0 |
1 |
4 |
32 |
0 |
1 |
12 |
134 |
| Spot Variance Path Estimation and Its Application to High-Frequency Jump Testing |
0 |
0 |
0 |
27 |
1 |
2 |
11 |
146 |
| State Space Models With a Common Stochastic Variance |
0 |
0 |
1 |
115 |
0 |
0 |
2 |
192 |
| Statistical Software for State Space Methods |
0 |
0 |
0 |
26 |
0 |
0 |
2 |
159 |
| Statistical algorithms for models in state space using SsfPack 2.2 |
0 |
0 |
0 |
1 |
0 |
0 |
3 |
1,274 |
| Testing for Parameter Instability across Different Modeling Frameworks |
0 |
1 |
1 |
4 |
1 |
2 |
2 |
26 |
| Testing the assumptions behind importance sampling |
0 |
0 |
0 |
66 |
0 |
0 |
1 |
268 |
| The Analysis of Stochastic Volatility in the Presence of Daily Realized Measures |
0 |
0 |
0 |
31 |
0 |
0 |
6 |
122 |
| The Modeling and Seasonal Adjustment of Weekly Observations |
0 |
0 |
0 |
0 |
1 |
2 |
5 |
896 |
| The analysis and forecasting of tennis matches by using a high dimensional dynamic model |
0 |
0 |
3 |
4 |
0 |
0 |
6 |
15 |
| The dynamic factor network model with an application to international trade |
0 |
0 |
2 |
18 |
0 |
1 |
7 |
86 |
| The information in systemic risk rankings |
0 |
3 |
4 |
21 |
0 |
4 |
8 |
87 |
| The multi-state latent factor intensity model for credit rating transitions |
0 |
0 |
1 |
150 |
0 |
0 |
11 |
459 |
| The stochastic volatility in mean model: empirical evidence from international stock markets |
0 |
0 |
1 |
466 |
1 |
2 |
5 |
1,414 |
| The stochastic volatility in mean model: empirical evidence from international stock markets |
0 |
1 |
1 |
4 |
0 |
1 |
2 |
28 |
| Time Series Modelling of Daily Tax Revenues |
0 |
0 |
0 |
33 |
0 |
0 |
0 |
109 |
| Time series analysis of non‐Gaussian observations based on state space models from both classical and Bayesian perspectives |
0 |
0 |
2 |
172 |
0 |
1 |
4 |
380 |
| Time-Varying Transition Probabilities for Markov Regime Switching Models |
0 |
0 |
2 |
11 |
0 |
2 |
4 |
43 |
| Tracking the Business Cycle of the Euro Area: A Multivariate Model-Based Bandpass Filter |
0 |
2 |
3 |
173 |
1 |
4 |
5 |
356 |
| Unobserved components with stochastic volatility: Simulation‐based estimation and signal extraction |
0 |
1 |
2 |
2 |
0 |
4 |
9 |
20 |
| Weighted maximum likelihood for dynamic factor analysis and forecasting with mixed frequency data |
0 |
0 |
3 |
32 |
1 |
2 |
10 |
139 |
| Total Journal Articles |
15 |
56 |
199 |
6,453 |
50 |
162 |
657 |
24,029 |
LogEc is hosted by the
Örebro University School of Business.