| Working Paper |
File Downloads |
Abstract Views |
| Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
| A Review of Nonfundamentalness and Identification in Structural VAR Models |
0 |
0 |
0 |
95 |
0 |
1 |
1 |
244 |
| A Robust Criterion for Determining the Number of Factors in Approximate Factor Models |
0 |
0 |
0 |
103 |
0 |
0 |
2 |
298 |
| A Robust Criterion for Determining the Number of Static Factors in Approximate Factor Models |
0 |
0 |
0 |
63 |
0 |
1 |
2 |
226 |
| A network analysis of the volatility of high-dimensionalfinancial series |
0 |
0 |
0 |
22 |
0 |
1 |
4 |
85 |
| A review of nonfundamentalness and identification in structural VAR models |
0 |
0 |
0 |
183 |
1 |
1 |
3 |
613 |
| A robust criterion for determining the number of static factors in approximate factor models |
0 |
1 |
1 |
133 |
0 |
2 |
6 |
443 |
| Asymptotic Theory of Principal Component Analysis for High-Dimensional Time Series Data under a Factor Structure |
0 |
0 |
0 |
73 |
0 |
1 |
8 |
32 |
| Asymptotic equivalence of Principal Components and Quasi Maximum Likelihood estimators in Large Approximate Factor Models |
0 |
0 |
0 |
13 |
0 |
0 |
4 |
19 |
| Common Factors, Trends, and Cycles in Large Datasets |
1 |
1 |
2 |
88 |
1 |
2 |
4 |
107 |
| Determining the dimension of factor structures in non-stationary large datasets |
0 |
0 |
0 |
34 |
0 |
0 |
0 |
35 |
| Determining the dimension of factor structures in non-stationary large datasets |
1 |
1 |
2 |
91 |
1 |
3 |
5 |
24 |
| Determining the rank of cointegration with infinite variance |
0 |
0 |
0 |
14 |
1 |
1 |
2 |
29 |
| Disentangling Systematic and Idiosyncratic Dynamics in Panels of Volatility Measures |
0 |
0 |
0 |
72 |
0 |
1 |
2 |
134 |
| Disentangling Systematic and Idiosyncratic Risk for Large Panels of Assets |
0 |
0 |
0 |
58 |
0 |
0 |
1 |
170 |
| Do Euro Area Countries Respond Asymmetrically to the Common Monetary Policy? |
0 |
0 |
0 |
4 |
0 |
0 |
0 |
110 |
| Do Euro area countries respond asymmetrically to the common monetary policy? |
0 |
0 |
0 |
21 |
0 |
0 |
0 |
143 |
| Do Intellectual Property Rights Influence Cross-Border Mergers and Acquisitions ? |
0 |
0 |
0 |
65 |
0 |
0 |
3 |
162 |
| Do National Account Statistics Underestimate US Real Output Growth? |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
13 |
| Dynamic Factor Models, Cointegration and Error Correction Mechanisms |
0 |
0 |
0 |
167 |
0 |
0 |
1 |
225 |
| Dynamic Factor Models, Cointegration, and Error Correction Mechanisms |
0 |
0 |
0 |
55 |
0 |
0 |
0 |
116 |
| Dynamic Factor Models: a Genealogy |
0 |
1 |
1 |
8 |
1 |
3 |
8 |
24 |
| Dynamic Factor Models: a Genealogy |
0 |
0 |
1 |
30 |
1 |
1 |
4 |
42 |
| Estimation and forecasting in large datasets with conditionally heteroskedastic dynamic common factors |
0 |
1 |
1 |
88 |
0 |
1 |
1 |
246 |
| Estimation of large approximate dynamic matrix factor models based on the EM algorithm and Kalman filtering |
0 |
0 |
9 |
9 |
0 |
1 |
15 |
15 |
| Factor Network Autoregressions |
1 |
4 |
32 |
206 |
2 |
9 |
57 |
147 |
| Factoring in the micro: a transaction-level dynamic factor approach to the decomposition of export volatility |
0 |
0 |
1 |
39 |
0 |
0 |
6 |
102 |
| Generalized Dynamic Factor Models and Volatilities. Recovering the Market Volatility Shocks |
0 |
0 |
1 |
140 |
1 |
1 |
4 |
165 |
| Generalized Dynamic Factor Models and Volatilities: Consistency, Rates, and Prediction Intervals |
0 |
0 |
0 |
28 |
1 |
1 |
3 |
61 |
| Generalized Dynamic Factor Models and Volatilities: Consistency, rates, and prediction intervals |
0 |
0 |
0 |
3 |
1 |
1 |
2 |
49 |
| Generalized Dynamic Factor Models and Volatilities: Estimation and Forecasting |
0 |
0 |
0 |
163 |
0 |
1 |
2 |
174 |
| Generalized dynamic factor models and volatilities estimation and forecasting |
0 |
0 |
0 |
8 |
2 |
2 |
3 |
35 |
| Generalized dynamic factor models and volatilities: recovering the market volatility shocks |
0 |
0 |
0 |
11 |
0 |
0 |
2 |
57 |
| Hierarchical DCC-HEAVY Model for High-Dimensional Covariance Matrices |
0 |
0 |
0 |
22 |
0 |
0 |
25 |
43 |
| Identification of Global and National Shocks in International Financial Markets via General Dynamic Factor Models |
1 |
1 |
1 |
32 |
2 |
3 |
5 |
79 |
| Identification of global and local shocks in international financial markets via general dynamic factor models |
0 |
0 |
0 |
43 |
0 |
0 |
2 |
105 |
| Identifying the Community Structure of the International-Trade Multi Network |
0 |
0 |
1 |
98 |
0 |
0 |
2 |
221 |
| Identifying the Independent Sources of Consumption Variation |
0 |
0 |
0 |
24 |
0 |
0 |
1 |
113 |
| Identifying the independent sources of consumption variation |
0 |
0 |
0 |
15 |
0 |
0 |
2 |
31 |
| Immigrants' legal status, permanence in the destination country and the distribution of consumption expenditure |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Immigrant’s legal status, permanence in the destination country and the distribution of consumption expenditure |
0 |
0 |
0 |
31 |
0 |
0 |
2 |
224 |
| Inference in heavy-tailed non-stationary multivariate time series |
0 |
0 |
1 |
79 |
1 |
5 |
11 |
177 |
| Inferential Theory for Generalized Dynamic Factor Models |
0 |
0 |
2 |
78 |
1 |
2 |
6 |
179 |
| Intellectual property rights, imitation, and development. The effect on cross-border mergers and acquisitions |
0 |
0 |
0 |
39 |
0 |
0 |
2 |
48 |
| Large datasets for the Euro Area and its member countries and the dynamic effects of the common monetary policy |
0 |
3 |
17 |
17 |
0 |
5 |
22 |
22 |
| Measuring Euro Area Monetary Policy Transmission in a Structural Dynamic Factor Model |
0 |
0 |
0 |
97 |
0 |
0 |
0 |
229 |
| Measuring the Euro Area Output Gap |
3 |
6 |
6 |
6 |
2 |
5 |
5 |
5 |
| Measuring the Euro Area Output Gap |
0 |
1 |
5 |
5 |
1 |
5 |
26 |
26 |
| Modelling Large Dimensional Datasets with Markov Switching Factor Models |
0 |
1 |
3 |
72 |
0 |
2 |
10 |
41 |
| Multidimensional dynamic factor models |
0 |
0 |
0 |
29 |
0 |
0 |
3 |
16 |
| Multinetwork of international trade: A commodity-specific analysis |
0 |
0 |
0 |
65 |
0 |
2 |
5 |
185 |
| Nets: Network Estimation for Time Series |
0 |
0 |
1 |
578 |
1 |
1 |
11 |
1,299 |
| Nets: Network estimation for time series |
0 |
0 |
0 |
83 |
0 |
0 |
5 |
209 |
| Nets: network estimation for time series |
0 |
0 |
1 |
37 |
0 |
0 |
3 |
90 |
| Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series |
1 |
1 |
1 |
73 |
1 |
2 |
3 |
120 |
| Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series |
0 |
0 |
0 |
54 |
0 |
0 |
1 |
95 |
| Non-Stationary Dynamic Factor Models for Large Datasets |
0 |
0 |
1 |
132 |
3 |
4 |
7 |
220 |
| On approximating the distributions of goodness-of-fit test statistics based on the empirical distribution function: The case of unknown parameters |
0 |
0 |
0 |
60 |
0 |
0 |
3 |
236 |
| On the Sources of Euro Area Money Demand Stability. A Time-Varying Cointegration Analysis |
0 |
0 |
0 |
133 |
0 |
0 |
1 |
327 |
| On the Stability of Euro Area Money Demand and its Implications for Monetary Policy |
0 |
0 |
0 |
91 |
0 |
0 |
1 |
200 |
| On the distributional properties of household consumption expenditures. The case of Italy |
0 |
0 |
0 |
52 |
0 |
0 |
0 |
211 |
| On the stability of euro area money demand and its implications for monetary policy |
0 |
0 |
0 |
8 |
0 |
0 |
1 |
34 |
| Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM algorithm |
0 |
1 |
2 |
2 |
2 |
6 |
14 |
14 |
| Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM algorithm |
0 |
0 |
3 |
63 |
0 |
0 |
12 |
108 |
| Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review |
2 |
2 |
6 |
79 |
3 |
3 |
11 |
32 |
| Quasi Maximum Likelihood Estimation of Non-Stationary Large Approximate Dynamic Factor Models |
0 |
0 |
1 |
29 |
0 |
0 |
3 |
67 |
| Sequential testing for structural stability in approximate factor models |
0 |
0 |
0 |
18 |
0 |
0 |
2 |
40 |
| Sequential testing for structural stability in approximate factor models |
0 |
0 |
0 |
49 |
0 |
0 |
1 |
61 |
| Simultaneous multiple change-point and factor analysis for high-dimensional time series |
0 |
0 |
0 |
17 |
0 |
0 |
0 |
39 |
| Spatio-Temporal Patterns of the International Merger and Acquisition Network |
0 |
0 |
0 |
37 |
0 |
0 |
0 |
54 |
| Spatio-temporal patterns of the international merger and acquisition network |
0 |
0 |
0 |
9 |
0 |
0 |
5 |
25 |
| The Canonical Decomposition of Factor Models: Weak Factors are Everywhere |
0 |
1 |
4 |
9 |
0 |
1 |
15 |
27 |
| The Distribution of Consumption-Expenditure Budget Shares. Evidence from Italian Households |
0 |
0 |
0 |
46 |
1 |
2 |
3 |
199 |
| The Distribution of Consumption-Expenditure Budget Shares. Evidence from Italian Households |
0 |
0 |
0 |
22 |
0 |
0 |
0 |
243 |
| The Dynamic, the Static, and the Weak Factor Models and the Analysis of High-Dimensional Time Series |
1 |
1 |
4 |
4 |
1 |
3 |
21 |
22 |
| The Dynamic, the Static, and the Weak: Factor models and the analysis of high-dimensional time series |
0 |
0 |
19 |
19 |
2 |
4 |
18 |
18 |
| The Euro Area has a growth problem |
0 |
0 |
6 |
6 |
1 |
2 |
8 |
8 |
| The Multi-Network of International Trade: A Commodity-Specific Analysis |
0 |
0 |
1 |
88 |
0 |
0 |
1 |
165 |
| The Rank of a System of Engel Curves. How Many Common Factors? |
0 |
0 |
0 |
13 |
0 |
0 |
0 |
66 |
| The distribution of households consumption-expenditure budget shares |
0 |
0 |
0 |
157 |
0 |
1 |
4 |
828 |
| Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness |
0 |
0 |
0 |
10 |
0 |
1 |
3 |
42 |
| Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness |
1 |
1 |
4 |
78 |
2 |
3 |
11 |
163 |
| Time-varying general dynamic factor models and the measurement of financial connectedness |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
31 |
| Total Working Papers |
12 |
28 |
141 |
4,898 |
37 |
97 |
455 |
11,382 |
LogEc is hosted by the
Örebro University School of Business.