| Working Paper |
File Downloads |
Abstract Views |
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3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
| A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series |
0 |
0 |
1 |
252 |
0 |
0 |
8 |
728 |
| A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series |
0 |
0 |
0 |
582 |
0 |
0 |
4 |
1,694 |
| A Comparison of Estimation Methods for Dynamic Factor Models of Large Dimensions |
0 |
0 |
0 |
4 |
0 |
0 |
3 |
26 |
| A Comparison of Methods for the Construction of Composite Coincident and Leading Indexes for the UK |
0 |
0 |
0 |
3 |
1 |
1 |
1 |
26 |
| A Comparison of Mixed Frequency Approaches for Modelling Euro Area Macroeconomic Variables |
0 |
4 |
5 |
152 |
0 |
6 |
11 |
276 |
| A Markov-Switching Vector Equilibrium Correction Model of the UK Labour Market |
0 |
0 |
0 |
571 |
0 |
0 |
1 |
1,538 |
| A Measure for Credibility: Tracking US Monetary Developments |
0 |
0 |
2 |
42 |
0 |
0 |
6 |
171 |
| A Measure for Credibility: Tracking US Monetary Developments |
0 |
0 |
0 |
56 |
0 |
0 |
1 |
170 |
| A Monthly Indicator of the Euro Area GDP |
0 |
0 |
5 |
91 |
1 |
3 |
16 |
313 |
| A Monthly Indicator of the Euro Area GDP |
0 |
0 |
2 |
218 |
0 |
1 |
4 |
466 |
| A Parametric Estimation Method for Dynamic Factor Models of Large Dimensions |
0 |
0 |
0 |
188 |
0 |
0 |
4 |
618 |
| A Shrinkage Instrumental Variable Estimator for Large Datasets |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
17 |
| A Shrinkage Instrumental Variable Estimator for Large Datasets |
1 |
1 |
1 |
8 |
1 |
1 |
3 |
40 |
| A Similarity-based Approach for Macroeconomic Forecasting |
0 |
0 |
0 |
61 |
0 |
0 |
7 |
101 |
| A Simple Benchmark for Forecasts of Growth and Inflation |
0 |
0 |
0 |
190 |
0 |
0 |
2 |
598 |
| A survey of econometric methods for mixed-frequency data |
0 |
2 |
6 |
279 |
1 |
3 |
14 |
595 |
| A survey of econometric methods for mixed-frequency data |
0 |
2 |
4 |
160 |
0 |
3 |
14 |
348 |
| Addressing COVID-19 Outliers in BVARs with Stochastic Volatility |
0 |
0 |
3 |
37 |
0 |
1 |
9 |
88 |
| Addressing COVID-19 Outliers in BVARs with Stochastic Volatility |
1 |
1 |
2 |
117 |
5 |
5 |
17 |
237 |
| Addressing COVID-19 outliers in BVARs with stochastic volatility |
0 |
1 |
7 |
44 |
2 |
4 |
22 |
100 |
| An Overview of the Factor-augmented Error-Correction Model |
2 |
2 |
6 |
204 |
6 |
6 |
24 |
227 |
| An estimated DSGE model of a Small Open Economy within the Monetary Union: Forecasting and Structural Analysis |
0 |
0 |
0 |
118 |
0 |
0 |
1 |
271 |
| An estimated DSGE model of a Small Open Economy within the Monetary Union: Forecasting and Structural Analysis |
1 |
3 |
3 |
154 |
2 |
5 |
6 |
239 |
| Are There Any Reliable Leading Indicators for U.S. Inflation and GDP Growth? |
0 |
0 |
2 |
575 |
1 |
1 |
7 |
2,409 |
| Are There Any Reliable Leading Indicators for US Inflation and GDP Growth? |
0 |
0 |
0 |
304 |
0 |
0 |
2 |
997 |
| Assessing International Commonality in Macroeconomic Uncertainty and Its Effects |
0 |
0 |
0 |
15 |
0 |
0 |
1 |
40 |
| Assessing International Commonality in Macroeconomic Uncertainty and Its Effects |
1 |
1 |
1 |
51 |
1 |
1 |
5 |
74 |
| Assessing International Commonality in Macroeconomic Uncertainty and Its Effects |
0 |
0 |
0 |
67 |
0 |
1 |
1 |
120 |
| Asymmetries in Financial Spillovers |
0 |
1 |
11 |
11 |
1 |
4 |
20 |
20 |
| Bayesian Neural Networks for Macroeconomic Analysis |
0 |
2 |
6 |
133 |
0 |
6 |
18 |
51 |
| Bayesian Neural Networks for Macroeconomic Analysis |
1 |
1 |
2 |
2 |
1 |
2 |
11 |
11 |
| Bayesian VARs: Specification Choices and Forecast Accuracy |
1 |
2 |
3 |
185 |
2 |
4 |
8 |
434 |
| Bayesian VARs: specification choices and forecast accuracy |
0 |
0 |
5 |
429 |
2 |
4 |
20 |
669 |
| Bayesian modelling of VAR precision matrices using stochastic block networks |
0 |
1 |
14 |
14 |
0 |
2 |
12 |
12 |
| Bayesian nonparametric methods for macroeconomic forecasting |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
| Bayesian nonparametric methods for macroeconomic forecasting |
1 |
3 |
25 |
25 |
3 |
7 |
57 |
57 |
| Big Data Econometrics: Now Casting and Early Estimates |
1 |
4 |
6 |
207 |
4 |
9 |
17 |
279 |
| Blended Identification in Structural VARs |
0 |
1 |
6 |
65 |
0 |
1 |
13 |
49 |
| Blended Identification in Structural VARs |
0 |
0 |
5 |
8 |
1 |
2 |
13 |
22 |
| Boosting the Forecasting Power of Conditional Heteroskedasticity Models to Account for Covid-19 Outbreaks |
0 |
1 |
1 |
87 |
0 |
1 |
4 |
56 |
| Can Machine Learning Catch the COVID-19 Recession? |
0 |
0 |
1 |
44 |
2 |
3 |
7 |
71 |
| Can Machine Learning Catch the COVID-19 Recession? |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
13 |
| Can Machine Learning Catch the COVID-19 Recession? |
0 |
0 |
0 |
25 |
0 |
0 |
2 |
103 |
| Can Machine Learning Catch the COVID-19 Recession? |
0 |
0 |
0 |
8 |
0 |
0 |
2 |
29 |
| Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions |
0 |
2 |
8 |
15 |
0 |
5 |
26 |
48 |
| Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions |
0 |
1 |
5 |
213 |
0 |
4 |
13 |
324 |
| Characterising the Business Cycle for Accession Countries |
0 |
0 |
0 |
196 |
0 |
0 |
1 |
532 |
| Characterising the Business Cycle for Accession Countries |
0 |
0 |
0 |
312 |
0 |
0 |
2 |
700 |
| Characterizing the Business Cycle for Accession Countries |
0 |
0 |
1 |
175 |
0 |
0 |
1 |
543 |
| Classical time-varying FAVAR models - Estimation, forecasting and structural analysis |
0 |
0 |
1 |
114 |
0 |
1 |
2 |
353 |
| Classical time-varying FAVAR models - estimation, forecasting and structural analysis |
1 |
2 |
4 |
663 |
2 |
4 |
24 |
1,560 |
| Coarsened Bayesian VARs -- Correcting BVARs for Incorrect Specification |
1 |
1 |
3 |
35 |
1 |
1 |
5 |
26 |
| Common Drifting Volatility in Large Bayesian VARs |
0 |
0 |
0 |
40 |
0 |
1 |
2 |
146 |
| Common Drifting Volatility in Large Bayesian VARs |
0 |
0 |
1 |
116 |
0 |
1 |
5 |
266 |
| Common drifting volatility in large Bayesian VARs |
0 |
0 |
1 |
97 |
0 |
3 |
7 |
278 |
| Cross-sectional Averaging and Instrumental Variable Estimation with Many Weak Instruments |
0 |
0 |
0 |
6 |
0 |
0 |
1 |
26 |
| Dating the Euro Area Business Cycle |
0 |
0 |
3 |
313 |
0 |
1 |
9 |
1,073 |
| Dating the Euro Area Business Cycle |
0 |
0 |
2 |
427 |
0 |
0 |
3 |
1,343 |
| Dating the Euro Area Business Cycle |
0 |
0 |
4 |
347 |
0 |
0 |
5 |
1,134 |
| Econometric analyses with backdated data: unified Germany and the euro area |
0 |
0 |
0 |
63 |
0 |
0 |
3 |
272 |
| Empirical Simultaneous Confidence Regions for Path-Forecasts |
0 |
0 |
0 |
8 |
0 |
0 |
2 |
61 |
| Empirical simultaneous confidence regions for path-forecasts |
0 |
0 |
0 |
46 |
0 |
0 |
0 |
149 |
| Empirical simultaneous prediction regions for path-forecasts |
0 |
0 |
1 |
58 |
0 |
0 |
2 |
142 |
| Endogenous Monetary Policy Regimes and the Great Moderation |
0 |
0 |
0 |
90 |
0 |
0 |
0 |
192 |
| Endogenous Monetary Policy Regimes and the Great Moderation |
0 |
0 |
0 |
35 |
0 |
0 |
1 |
193 |
| Endogenous Uncertainty |
0 |
0 |
0 |
166 |
0 |
0 |
4 |
401 |
| EuroMInd-C: a Disaggregate Monthly Indicator of Economic Activity for the Euro |
0 |
0 |
2 |
62 |
0 |
0 |
2 |
121 |
| EuroMInd-C: a Disaggregate Monthly Indicator of Economic Activity for the Euro Area and member countries |
0 |
0 |
1 |
68 |
1 |
1 |
2 |
148 |
| Ex Post and Ex Ante Analysis of Provisional Data |
0 |
0 |
0 |
240 |
0 |
0 |
0 |
2,045 |
| Explaining the Time-varying Effects Of Oil Market Shocks On U.S. Stock Returns |
0 |
0 |
3 |
77 |
0 |
0 |
9 |
187 |
| Factor Analysis in a Model with Rational Expectations |
0 |
1 |
1 |
119 |
0 |
1 |
2 |
350 |
| Factor Analysis in a New-Keynesian Model |
0 |
0 |
0 |
116 |
0 |
0 |
2 |
477 |
| Factor Based Index Tracking |
0 |
0 |
0 |
538 |
0 |
1 |
4 |
1,317 |
| Factor Based Index Trading |
0 |
0 |
1 |
453 |
0 |
0 |
3 |
1,344 |
| Factor Forecasts for the UK |
0 |
0 |
0 |
161 |
0 |
0 |
0 |
497 |
| Factor Forecasts for the UK |
0 |
0 |
1 |
191 |
0 |
0 |
1 |
530 |
| Factor analysis in a New-Keynesian model |
0 |
0 |
0 |
198 |
0 |
0 |
1 |
557 |
| Factor based identification-robust inference in IV regressions |
0 |
0 |
0 |
47 |
1 |
1 |
4 |
93 |
| Factor forecasts for the UK |
0 |
0 |
2 |
176 |
0 |
1 |
4 |
586 |
| Factor-GMM Estimation with Large Sets of Possibly Weak Instruments |
0 |
0 |
0 |
23 |
0 |
0 |
3 |
108 |
| Factor-GMM Estimation with Large Sets of Possibly Weak Instruments |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
22 |
| Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP |
0 |
0 |
1 |
144 |
1 |
1 |
5 |
419 |
| Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1 |
0 |
0 |
1 |
93 |
0 |
0 |
4 |
397 |
| Factor-MIDAS for now- and forecasting with ragged-edge data: A model comparison for German GDP |
0 |
0 |
2 |
218 |
0 |
0 |
4 |
713 |
| Factor-MIDAS for now- and forecasting with ragged-edge data: a model comparison for German GDP |
0 |
0 |
2 |
199 |
0 |
0 |
5 |
702 |
| Factor-augmented Error Correction Models |
0 |
0 |
4 |
362 |
1 |
1 |
11 |
922 |
| Factor-augmented Error Correction Models |
0 |
0 |
1 |
199 |
0 |
0 |
3 |
519 |
| Factor-augmented Error Correction Models |
0 |
0 |
1 |
175 |
0 |
0 |
1 |
353 |
| Firm Heterogeneity and Macroeconomic Fluctuations: a Functional VAR model |
0 |
2 |
25 |
25 |
0 |
5 |
45 |
45 |
| Fiscal Forecasting: the Track Record of the IMF, OECD and EC |
0 |
0 |
0 |
1 |
1 |
1 |
2 |
436 |
| Fiscal Forecasting: the Track Record of the IMF, OECD, and EC |
1 |
1 |
1 |
172 |
1 |
1 |
4 |
589 |
| Fiscal Solvency and Fiscal Forecasting in Europe |
0 |
0 |
0 |
1 |
0 |
1 |
3 |
347 |
| Fiscal Solvency and Fiscal Forecasting in Europe |
0 |
0 |
0 |
289 |
0 |
1 |
2 |
804 |
| Fiscal Solvency and Fiscal Forecasting in Europe |
0 |
0 |
1 |
200 |
0 |
0 |
2 |
646 |
| Forecast Pooling for Short Time Series of Macroeconomic Variables |
0 |
0 |
0 |
274 |
0 |
0 |
3 |
878 |
| Forecast pooling for short time series of macroeconomic variables |
0 |
0 |
2 |
421 |
1 |
5 |
9 |
1,572 |
| Forecasting EMU Macroeconomic Variables |
0 |
0 |
0 |
302 |
1 |
1 |
1 |
1,848 |
| Forecasting EMU macroeconomic variables |
1 |
1 |
1 |
325 |
1 |
1 |
5 |
1,810 |
| Forecasting Euro-Area Variables with German Pre-EMU Data |
0 |
0 |
0 |
54 |
0 |
0 |
0 |
300 |
| Forecasting Exchange Rates with a Large Bayesian VAR |
1 |
1 |
2 |
174 |
2 |
4 |
8 |
419 |
| Forecasting Exchange Rates with a Large Bayesian VAR |
0 |
1 |
1 |
11 |
0 |
2 |
6 |
38 |
| Forecasting Exchange Rates with a Large Bayesian VAR |
0 |
1 |
3 |
75 |
0 |
1 |
4 |
267 |
| Forecasting Government Bond Yields with Large Bayesian VARs |
0 |
0 |
0 |
9 |
0 |
0 |
2 |
42 |
| Forecasting Government Bond Yields with Large Bayesian VARs |
1 |
1 |
1 |
37 |
1 |
1 |
3 |
139 |
| Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models |
0 |
0 |
0 |
62 |
0 |
2 |
3 |
212 |
| Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models |
0 |
0 |
0 |
144 |
0 |
0 |
0 |
308 |
| Forecasting Large Datasets with Reduced Rank Multivariate Models |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
11 |
| Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change |
0 |
1 |
1 |
127 |
1 |
4 |
5 |
687 |
| Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change |
0 |
0 |
1 |
219 |
0 |
0 |
3 |
636 |
| Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change |
0 |
0 |
1 |
141 |
0 |
0 |
5 |
554 |
| Forecasting Macroeconomic Variables for the Acceding Countries |
0 |
0 |
1 |
120 |
0 |
0 |
2 |
578 |
| Forecasting US Inflation Using Bayesian Nonparametric Models |
0 |
0 |
2 |
122 |
0 |
0 |
4 |
109 |
| Forecasting US Inflation Using Bayesian Nonparametric Models |
0 |
1 |
4 |
30 |
1 |
2 |
9 |
57 |
| Forecasting US Inflation Using Bayesian Nonparametric Models |
0 |
0 |
0 |
30 |
0 |
3 |
5 |
89 |
| Forecasting economic activity with higher frequency targeted predictors |
0 |
0 |
0 |
151 |
0 |
1 |
3 |
252 |
| Forecasting euro-area variables with German pre-EMU data |
0 |
0 |
0 |
55 |
0 |
0 |
2 |
195 |
| Forecasting macroeconomic variables for the new member states of the European Union |
0 |
0 |
0 |
192 |
0 |
1 |
5 |
706 |
| Forecasting the COVID-19 recession and recovery: Lessons from the financial crisis |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
4 |
| Forecasting the Covid-19 Recession and Recovery: Lessons from the Financial Crisis |
0 |
0 |
0 |
127 |
0 |
0 |
2 |
274 |
| Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis |
0 |
0 |
0 |
24 |
0 |
2 |
2 |
62 |
| Forecasting the Covid-19 recession and recovery: lessons from the financial crisis |
0 |
0 |
0 |
30 |
0 |
0 |
0 |
54 |
| Forecasting with Dynamic Models using Shrinkage-based Estimation |
0 |
0 |
2 |
3 |
0 |
0 |
2 |
17 |
| Forecasting with Factor-Augmented Error Correction Models |
0 |
0 |
3 |
204 |
0 |
2 |
8 |
366 |
| Forecasting with Factor-augmented Error Correction Models |
0 |
0 |
0 |
101 |
0 |
0 |
0 |
233 |
| Forecasting with Factor-augmented Error Correction Models |
1 |
1 |
2 |
61 |
2 |
2 |
3 |
230 |
| Forecasting with Large Unbalanced Datasets: The Mixed-Frequency Three-Pass Regression Filter |
0 |
1 |
3 |
169 |
1 |
2 |
10 |
306 |
| Forecasting with Shadow-Rate VARs |
0 |
0 |
0 |
48 |
0 |
1 |
3 |
90 |
| Further Results on MSFE Encompassing |
0 |
0 |
0 |
62 |
0 |
0 |
0 |
474 |
| Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty |
0 |
0 |
1 |
152 |
0 |
0 |
6 |
97 |
| Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty |
0 |
0 |
2 |
3 |
0 |
0 |
4 |
7 |
| Have Standard VARs Remained Stable Since the Crisis? |
0 |
0 |
0 |
43 |
0 |
0 |
0 |
77 |
| Have Standard VARs Remained Stable since the Crisis? |
0 |
0 |
0 |
91 |
0 |
0 |
1 |
210 |
| Have standard VARs remained stable since the crisis? |
0 |
0 |
0 |
114 |
1 |
1 |
13 |
252 |
| Impulse Response Functions from Structural Dynamic Factor Models: A Monte Carlo Evaluation |
0 |
0 |
0 |
144 |
0 |
0 |
1 |
529 |
| Impulse Response Functions from Structural Dynamic Factor Models:A Monte Carlo Evaluation |
0 |
0 |
0 |
348 |
0 |
1 |
2 |
1,051 |
| Inflation, Attention and Expectations |
0 |
1 |
15 |
15 |
5 |
8 |
25 |
25 |
| Inflation, Attention and Expectations |
2 |
3 |
7 |
7 |
3 |
7 |
14 |
14 |
| Instability and Non-Linearity in the EMU |
0 |
0 |
0 |
100 |
0 |
0 |
1 |
330 |
| Instability and non-linearity in the EMU |
0 |
0 |
0 |
122 |
0 |
0 |
0 |
536 |
| Interpolation and Backdating with A Large Information Set |
0 |
0 |
1 |
97 |
0 |
0 |
4 |
346 |
| Interpolation and backdating with a large information set |
0 |
0 |
0 |
129 |
0 |
0 |
2 |
426 |
| Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model |
0 |
0 |
2 |
36 |
0 |
0 |
5 |
51 |
| Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model |
0 |
0 |
0 |
1 |
0 |
0 |
3 |
8 |
| Investigating Growth-at-Risk Using a Multicountry Non-parametric Quantile Factor Model |
0 |
1 |
1 |
1 |
0 |
1 |
4 |
4 |
| LSM: A DSGE Model for Luxembourg |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
17 |
| LSM: A DSGE Model for Luxembourg |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
75 |
| LSM: A DSGE Model for Luxembourg |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
13 |
| Large Datasets, Small Models and Monetary Policy in Europe |
0 |
0 |
2 |
112 |
0 |
0 |
6 |
635 |
| Large Datasets, Small Models and Monetary Policy in Europe |
0 |
0 |
0 |
152 |
0 |
0 |
3 |
991 |
| Large Time-Varying Parameter VARs: A Non-Parametric Approach |
0 |
1 |
2 |
86 |
1 |
2 |
6 |
125 |
| Large Vector Autoregressions with Asymmetric Priors |
0 |
0 |
0 |
6 |
0 |
1 |
1 |
38 |
| Large Vector Autoregressions with Stochastic Volatility and Flexible Priors |
0 |
0 |
1 |
206 |
0 |
2 |
7 |
376 |
| Large time-varying parameter VARs: a non-parametric approach |
1 |
1 |
1 |
122 |
2 |
2 |
9 |
184 |
| Leading Indicators for Euro Area Inflation and GDP Growth |
0 |
0 |
1 |
343 |
1 |
2 |
5 |
1,057 |
| Leading Indicators for Euro-area Inflation and GDP Growth |
2 |
2 |
4 |
670 |
2 |
3 |
10 |
1,847 |
| Leading Indicators: What Have We Learned? |
0 |
0 |
1 |
234 |
0 |
0 |
1 |
474 |
| Leading Indicators: What Have We Learned? |
0 |
0 |
0 |
385 |
0 |
0 |
2 |
628 |
| Linear Aggregation with Common Trends and Cycles |
0 |
0 |
0 |
63 |
2 |
2 |
3 |
222 |
| MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area |
1 |
2 |
7 |
453 |
1 |
4 |
17 |
1,121 |
| MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area |
0 |
0 |
1 |
120 |
2 |
2 |
8 |
484 |
| MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area |
0 |
0 |
0 |
133 |
0 |
0 |
1 |
414 |
| Machine Learning the Macroeconomic Effects of Financial Shocks |
0 |
3 |
27 |
27 |
3 |
6 |
24 |
24 |
| Macro Uncertainty in the Long Run |
0 |
0 |
1 |
4 |
1 |
1 |
3 |
12 |
| Macroeconomic Forecasting in a Multi-country Context |
0 |
0 |
0 |
6 |
1 |
2 |
6 |
22 |
| Macroeconomic Forecasting in a Multi-country Context |
0 |
0 |
1 |
67 |
0 |
0 |
5 |
59 |
| Macroeconomic Forecasting in the Euro Area: Country Specific versus Area-Wide Information |
1 |
2 |
5 |
683 |
1 |
2 |
9 |
1,836 |
| Macroeconomic activity and risk indicators: an unstable relationship |
0 |
0 |
0 |
55 |
0 |
0 |
0 |
52 |
| Macroeconomic forecasting during the Great Recession: The return of non-linearity? |
0 |
0 |
0 |
178 |
0 |
1 |
4 |
456 |
| Macroeconomic forecasting during the Great Recession: The return of non-linearity? |
0 |
0 |
0 |
58 |
0 |
0 |
0 |
145 |
| Macroeconomic forecasting during the Great Recession: the return of non-linearity? |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
31 |
| Markov-Switching Mixed-Frequency VAR Models |
1 |
2 |
2 |
127 |
1 |
4 |
8 |
285 |
| Markov-Switching Three-Pass Regression Filter |
0 |
0 |
0 |
26 |
0 |
0 |
2 |
117 |
| Markov-switching MIDAS models |
0 |
0 |
2 |
115 |
0 |
0 |
11 |
466 |
| Markov-switching three-pass regression filter |
0 |
0 |
0 |
33 |
0 |
1 |
1 |
98 |
| Mean Group Instrumental Variable Estimation of Time-Varying Large Heterogeneous Panels with Endogenous Regressors |
0 |
0 |
1 |
17 |
0 |
0 |
6 |
18 |
| Measuring Uncertainty and Its Effects in the COVID-19 Era |
0 |
0 |
1 |
57 |
2 |
3 |
6 |
126 |
| Measuring Uncertainty and Its Effects in the COVID-19 Era |
0 |
0 |
1 |
11 |
0 |
0 |
2 |
31 |
| Measuring Uncertainty and Its Impact on the Economy |
0 |
1 |
1 |
201 |
0 |
2 |
6 |
358 |
| Measuring Uncertainty and Its Impact on the Economy |
1 |
1 |
2 |
75 |
1 |
4 |
14 |
146 |
| Mixed frequency models with MA components |
0 |
1 |
1 |
34 |
0 |
3 |
10 |
108 |
| Mixed frequency models with MA components |
0 |
0 |
0 |
79 |
0 |
0 |
4 |
119 |
| Mixed frequency structural VARs |
0 |
0 |
1 |
196 |
0 |
0 |
3 |
337 |
| Mixed frequency structural models: estimation, and policy analysis |
0 |
0 |
0 |
124 |
1 |
2 |
3 |
198 |
| Model Selection for Non-Linear Dynamic Models |
0 |
0 |
1 |
236 |
0 |
0 |
2 |
666 |
| Modelling and Forecasting Fiscal Variables for the Euro Area |
0 |
0 |
5 |
305 |
0 |
0 |
8 |
642 |
| Modelling and Forecasting Fiscal Variables for the euro Area |
0 |
0 |
0 |
134 |
0 |
1 |
6 |
388 |
| Modelling shifts in the wage-price and unemployment-inflation relationships in Italy, Poland, and the UK |
0 |
0 |
0 |
333 |
0 |
0 |
2 |
1,477 |
| Monetary, Fiscal and Oil Shocks: Evidence based on Mixed Frequency Structural FAVARs |
0 |
0 |
0 |
122 |
0 |
0 |
2 |
211 |
| Monitoring the Economy of the Euro Area: A Comparison of Composite Coincident Indexes |
0 |
0 |
0 |
101 |
0 |
0 |
1 |
402 |
| No Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates |
0 |
0 |
0 |
72 |
1 |
1 |
2 |
142 |
| No-Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates |
0 |
0 |
0 |
44 |
0 |
0 |
3 |
42 |
| Nonparametric Time Varying IV-SVARs: Estimation and Inference |
0 |
1 |
17 |
17 |
0 |
4 |
14 |
14 |
| Nowcasting Tail Risk to Economic Activity at a Weekly Frequency |
0 |
2 |
3 |
39 |
1 |
4 |
9 |
98 |
| Nowcasting Tail Risks to Economic Activity with Many Indicators |
0 |
0 |
0 |
96 |
0 |
1 |
9 |
229 |
| Nowcasting distributions: a functional MIDAS model |
0 |
0 |
44 |
44 |
1 |
4 |
63 |
63 |
| Nowcasting with Mixed Frequency Data Using Gaussian Processes |
1 |
2 |
6 |
37 |
2 |
6 |
25 |
43 |
| On the importance of sectoral and regional shocks for price setting |
0 |
0 |
0 |
18 |
0 |
0 |
1 |
71 |
| On the importance of sectoral and regional shocks for price-setting |
0 |
0 |
0 |
36 |
0 |
0 |
1 |
129 |
| On the importance of sectoral and regional shocks for price-setting |
0 |
0 |
1 |
71 |
0 |
0 |
1 |
227 |
| On the importance of sectoral shocks for price-setting |
0 |
0 |
0 |
7 |
0 |
0 |
0 |
54 |
| Path Forecast Evaluation |
1 |
1 |
3 |
14 |
1 |
2 |
6 |
83 |
| Path Forecast Evaluation |
1 |
1 |
1 |
75 |
1 |
1 |
1 |
182 |
| Path Forecast Evaluation |
0 |
0 |
1 |
33 |
0 |
0 |
3 |
88 |
| Point, interval and density forecasts of exchange rates with time-varying parameter models |
0 |
1 |
1 |
84 |
1 |
2 |
10 |
185 |
| Point, interval and density forecasts of exchange rates with time-varying parameter models |
0 |
0 |
0 |
38 |
0 |
0 |
1 |
65 |
| Pooling versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP |
0 |
0 |
2 |
122 |
0 |
0 |
4 |
318 |
| Pooling versus model selection for nowcasting with many predictors: An application to German GDP |
0 |
1 |
1 |
83 |
0 |
1 |
1 |
285 |
| Pooling versus model selection for nowcasting with many predictors: an application to German GDP |
0 |
0 |
0 |
86 |
0 |
1 |
1 |
260 |
| Pooling-based Data Interpolation and Backdating |
0 |
0 |
0 |
80 |
0 |
1 |
2 |
301 |
| Pooling-based data interpolation and backdating |
0 |
0 |
0 |
64 |
0 |
0 |
0 |
325 |
| Principal components at work: The empirical analysis of monetary policy with large datasets |
0 |
0 |
1 |
792 |
0 |
1 |
4 |
2,351 |
| Public Capital and Economic Performance: Evidence from Italy |
0 |
0 |
1 |
459 |
0 |
0 |
2 |
1,227 |
| Real time estimates of the euro area output gap: reliability and forecasting performance |
0 |
2 |
3 |
153 |
0 |
2 |
10 |
462 |
| Real-Time Nowcasting with a Bayesian Mixed Frequency Model with Stochastic Volatility |
0 |
0 |
0 |
74 |
0 |
0 |
3 |
245 |
| Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility |
0 |
1 |
3 |
231 |
0 |
2 |
8 |
462 |
| Regime Switches in the Risk-Return Trade-Off |
0 |
0 |
0 |
39 |
0 |
1 |
2 |
164 |
| Regime Switches in the Risk-Return Trade-off |
0 |
0 |
0 |
46 |
0 |
1 |
2 |
53 |
| Regional Inflation Dynamics within and across Euro Area Countries and a Comparison with the US |
0 |
1 |
1 |
6 |
0 |
1 |
3 |
58 |
| Regional Inflation Dynamics within and across Euro Area and a Comparison with the US |
0 |
0 |
0 |
125 |
0 |
0 |
1 |
332 |
| Regional inflation dynamics within and across euro area countries and a comparison with the US |
0 |
0 |
1 |
202 |
1 |
1 |
5 |
713 |
| Regional inflation dynamics within and across euro area countries and a comparison with the US |
0 |
1 |
1 |
13 |
0 |
1 |
1 |
83 |
| Risky Oil: It's All in the Tails |
0 |
0 |
1 |
1 |
1 |
1 |
4 |
4 |
| Risky Oil: It's All in the Tails |
0 |
1 |
12 |
12 |
0 |
1 |
29 |
29 |
| STOCHASTIC PROCESSES SUBJECT TO TIME SCALE TRANSFORMATIONS: AN APPLICATION TO HIGH-FREQUENCY FX DATA |
0 |
0 |
0 |
102 |
0 |
0 |
0 |
539 |
| STOCHASTIC PROCESSES SUBJECT TO TIME SCALE TRANSFORMATIONS: AN APPLICATION TO HIGH-FREQUENCY FX DATA |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
11 |
| Sectoral Survey-based Confidence Indicators for Europe |
1 |
1 |
1 |
52 |
1 |
2 |
4 |
251 |
| Selecting predictors by using Bayesian model averaging in bridge models |
0 |
0 |
1 |
71 |
0 |
0 |
2 |
187 |
| Shadow-rate VARs |
0 |
3 |
10 |
34 |
0 |
6 |
27 |
70 |
| Short-term GDP forecasting with a mixed frequency dynamic factor model with stochastic volatility |
0 |
0 |
0 |
172 |
0 |
0 |
3 |
420 |
| Short-term GDP forecasting with a mixed frequency dynamic factor model with stochastic volatility |
0 |
0 |
6 |
407 |
0 |
2 |
11 |
875 |
| Small system modelling of real wages, inflation, unemployment and output per capita in Italy 1970-1994 |
0 |
0 |
0 |
251 |
0 |
0 |
0 |
994 |
| Some Cautions on the Use of Panel Methods for Integrated Series of Macro-Economic Data |
0 |
0 |
2 |
438 |
0 |
0 |
3 |
1,056 |
| Some Cautions on the Use of Panel Methods for Integrated Series of Macro-economic Data |
0 |
0 |
0 |
0 |
0 |
0 |
5 |
478 |
| Some Stylized Facts on Non-Systematic Fiscal Policy in the Euro Area |
0 |
0 |
0 |
115 |
0 |
1 |
1 |
435 |
| Some stylized facts on non-systematic fiscal policy in the Euro area |
0 |
1 |
2 |
357 |
0 |
1 |
2 |
961 |
| Specification Choices in Quantile Regression for Empirical Macroeconomics |
1 |
2 |
12 |
12 |
3 |
7 |
27 |
27 |
| Specification Choices in Quantile Regression for Empirical Macroeconomics |
0 |
1 |
7 |
80 |
0 |
1 |
17 |
78 |
| Stochastic Processes Subject to Time-Scale Transformations: An Application to High-Frequency FX Data |
0 |
0 |
0 |
164 |
2 |
2 |
4 |
774 |
| Structural Analysis with Multivariate Autoregressive Index Models |
0 |
0 |
3 |
87 |
0 |
1 |
4 |
121 |
| Structural FECM: Cointegration in large-scale structural FAVAR models |
0 |
0 |
1 |
90 |
0 |
0 |
2 |
187 |
| Survey Data as Coicident or Leading Indicators |
1 |
1 |
1 |
72 |
1 |
1 |
2 |
202 |
| Survey Data as Coincident or Leading Indicators |
1 |
1 |
2 |
38 |
1 |
1 |
3 |
166 |
| TFP, Costs, and Public Infrastructure: An Equivocal Relationship |
0 |
1 |
3 |
396 |
0 |
1 |
3 |
1,032 |
| Tail Forecasting with Multivariate Bayesian Additive Regression Trees |
0 |
0 |
0 |
78 |
0 |
1 |
3 |
90 |
| Tail Forecasting with Multivariate Bayesian Additive Regression Trees |
1 |
1 |
1 |
5 |
1 |
3 |
10 |
18 |
| Tax shocks with high and low uncertainty |
0 |
0 |
0 |
122 |
1 |
1 |
3 |
133 |
| Temporal Disaggregation, Missing Observations, Outliers, and Forecasting: A Unifying Non-Model Based Procedures |
0 |
0 |
0 |
0 |
1 |
1 |
5 |
320 |
| Testing for PPP: Should We Use Panel Methods? |
0 |
0 |
0 |
471 |
0 |
1 |
1 |
1,587 |
| Testing for PPP: Should We Use Panel Methods? |
0 |
0 |
0 |
309 |
0 |
0 |
0 |
615 |
| The Changing International Transmission of Financial Shocks: Evidence from a Classical Time-Varying FAVAR |
0 |
0 |
1 |
76 |
0 |
1 |
4 |
256 |
| The Distributional Effects of Economic Uncertainty |
0 |
2 |
5 |
5 |
0 |
4 |
12 |
12 |
| The Forecasting Performance of Real Time Estimates of the Euro Area Output Gap |
0 |
0 |
0 |
17 |
0 |
1 |
1 |
85 |
| The Global Component of Inflation Volatility |
0 |
0 |
0 |
41 |
0 |
3 |
8 |
157 |
| The Multiscale Causal Dynamics of Foreign Exchange Markets |
0 |
1 |
1 |
65 |
1 |
2 |
3 |
166 |
| The Reliability of Real Time Estimates of the Euro Area Output Gap |
0 |
0 |
0 |
32 |
0 |
0 |
1 |
167 |
| The Role of Search Frictions and Bargaining for Inflation Dynamics |
0 |
0 |
0 |
41 |
0 |
0 |
2 |
175 |
| The Transmission Mechanism in a Changing World |
0 |
0 |
0 |
134 |
0 |
0 |
0 |
499 |
| The banking and distribution sectors in a small open economy DSGE Model |
0 |
0 |
0 |
178 |
1 |
1 |
5 |
351 |
| The banking and distribution sectors in a small open economy DSGE Model |
0 |
0 |
0 |
29 |
0 |
1 |
1 |
89 |
| The changing international transmission of financial shocks: evidence from a classical time-varying FAVAR |
0 |
0 |
2 |
280 |
1 |
1 |
10 |
675 |
| The demand and supply of information about inflation |
0 |
0 |
1 |
20 |
0 |
1 |
4 |
30 |
| The demand and supply of information about inflation |
0 |
0 |
2 |
30 |
1 |
3 |
22 |
69 |
| The economic drivers of volatility and uncertainty |
0 |
0 |
0 |
68 |
0 |
0 |
5 |
116 |
| The financial accelerator mechanism: does frequency matter? |
0 |
0 |
0 |
25 |
0 |
0 |
1 |
62 |
| The financial accelerator mechanism: does frequency matter? |
0 |
0 |
0 |
12 |
0 |
1 |
2 |
13 |
| The global component of inflation volatility |
0 |
0 |
1 |
148 |
1 |
1 |
5 |
375 |
| The transmission mechanism in a changing world |
0 |
0 |
0 |
214 |
0 |
1 |
2 |
527 |
| Time Variation in Macro-Financial Linkages |
0 |
0 |
0 |
59 |
0 |
1 |
3 |
185 |
| Time Varying Three Pass Regression Filter |
4 |
6 |
10 |
10 |
4 |
13 |
20 |
20 |
| Time variation in macro-financial linkages |
0 |
0 |
0 |
172 |
0 |
0 |
3 |
431 |
| Time-Scale Transformations of Discrete-Time Processes |
0 |
0 |
0 |
2 |
0 |
0 |
5 |
18 |
| Time-Varying Instrumental Variable Estimation |
0 |
0 |
0 |
40 |
0 |
0 |
1 |
97 |
| Time-Varying Instrumental Variable Estimation |
0 |
0 |
1 |
50 |
1 |
1 |
8 |
71 |
| U-MIDAS: MIDAS regressions with unrestricted lag polynomials |
0 |
0 |
4 |
99 |
1 |
1 |
15 |
327 |
| U-MIDAS: MIDAS regressions with unrestricted lag polynomials |
2 |
4 |
20 |
584 |
6 |
12 |
59 |
2,021 |
| Uncertainty Through the Lenses of A Mixed-Frequency Bayesian Panel Markov Switching Model |
0 |
0 |
0 |
53 |
1 |
1 |
5 |
108 |
| Using Time-Varying Volatility for Identification in Vector Autoregressions: An Application to Endogenous Uncertainty |
1 |
1 |
1 |
23 |
1 |
1 |
2 |
55 |
| Using low frequency information for predicting high frequency variables |
0 |
0 |
1 |
141 |
0 |
2 |
16 |
230 |
| Wages, Prices, Productivity, Inflation and Unemployment in Italy 1970-1994 |
0 |
0 |
1 |
702 |
1 |
1 |
3 |
2,893 |
| interpolation with a large information set |
0 |
0 |
0 |
55 |
0 |
0 |
1 |
253 |
| the Reliability of Real Time Estimates of the EURO Area Output Gap |
0 |
0 |
0 |
53 |
1 |
1 |
1 |
158 |
| Total Working Papers |
41 |
114 |
557 |
36,365 |
130 |
361 |
1,688 |
106,456 |
| Journal Article |
File Downloads |
Abstract Views |
| Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
| A Credibility Proxy: Tracking US Monetary Developments |
0 |
0 |
2 |
102 |
1 |
3 |
7 |
273 |
| A Markov-switching vector equilibrium correction model of the UK labour market |
0 |
0 |
0 |
309 |
0 |
0 |
1 |
915 |
| A SHRINKAGE INSTRUMENTAL VARIABLE ESTIMATOR FOR LARGE DATASETS |
0 |
0 |
0 |
1 |
0 |
1 |
1 |
48 |
| A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series |
2 |
4 |
12 |
421 |
6 |
13 |
107 |
1,217 |
| A comparison of methods for the construction of composite coincident and leading indexes for the UK |
0 |
0 |
1 |
57 |
1 |
1 |
3 |
159 |
| A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates |
2 |
3 |
10 |
180 |
4 |
5 |
28 |
378 |
| A daily indicator of economic growth for the euro area |
0 |
0 |
2 |
44 |
1 |
2 |
6 |
112 |
| A linear benchmark for forecasting GDP growth and inflation? |
0 |
1 |
2 |
195 |
0 |
3 |
9 |
529 |
| A macroeconometric model for the Euro economy |
0 |
0 |
1 |
139 |
0 |
1 |
4 |
361 |
| A parametric estimation method for dynamic factor models of large dimensions |
0 |
0 |
0 |
64 |
0 |
0 |
2 |
152 |
| A similarity‐based approach for macroeconomic forecasting |
0 |
0 |
0 |
28 |
0 |
2 |
5 |
103 |
| Addressing COVID-19 Outliers in BVARs with Stochastic Volatility |
0 |
0 |
3 |
3 |
5 |
9 |
52 |
52 |
| Are there any reliable leading indicators for US inflation and GDP growth? |
0 |
0 |
4 |
204 |
0 |
0 |
9 |
550 |
| Assessing international commonality in macroeconomic uncertainty and its effects |
0 |
2 |
2 |
28 |
0 |
2 |
5 |
81 |
| Bayesian VARs: Specification Choices and Forecast Accuracy |
1 |
4 |
9 |
124 |
2 |
6 |
16 |
332 |
| Bayesian neural networks for macroeconomic analysis |
0 |
0 |
0 |
0 |
2 |
2 |
2 |
2 |
| Blended identification in structural VARs |
0 |
0 |
7 |
7 |
2 |
4 |
29 |
29 |
| Business Cycles in the New EU Member Countries and their Conformity with the Euro Area |
0 |
0 |
0 |
80 |
1 |
2 |
4 |
216 |
| CAN MACHINE LEARNING CATCH THE COVID-19 RECESSION? |
1 |
1 |
1 |
15 |
1 |
1 |
2 |
30 |
| Capturing Macro‐Economic Tail Risks with Bayesian Vector Autoregressions |
0 |
2 |
7 |
7 |
3 |
9 |
27 |
27 |
| Classical time varying factor-augmented vector auto-regressive models—estimation, forecasting and structural analysis |
0 |
1 |
2 |
76 |
0 |
1 |
3 |
163 |
| Common Drifting Volatility in Large Bayesian VARs |
0 |
0 |
5 |
56 |
0 |
0 |
15 |
158 |
| Cross-sectional averaging and instrumental variable estimation with many weak instruments |
0 |
0 |
0 |
33 |
0 |
0 |
1 |
97 |
| Dating Business Cycles: A Methodological Contribution with an Application to the Euro Area |
1 |
1 |
3 |
195 |
1 |
1 |
7 |
564 |
| EUROMIND: a monthly indicator of the euro area economic conditions |
0 |
0 |
0 |
0 |
1 |
2 |
16 |
224 |
| Econometric analyses with backdated data: Unified Germany and the euro area |
0 |
0 |
0 |
19 |
0 |
0 |
2 |
123 |
| Empirical simultaneous prediction regions for path-forecasts |
0 |
0 |
1 |
27 |
0 |
0 |
1 |
99 |
| EuroMInd-C: A disaggregate monthly indicator of economic activity for the Euro area and member countries |
0 |
0 |
0 |
11 |
0 |
0 |
1 |
67 |
| Explaining the time-varying effects of oil market shocks on US stock returns |
0 |
0 |
2 |
42 |
0 |
0 |
3 |
131 |
| Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP |
0 |
1 |
11 |
207 |
2 |
8 |
41 |
508 |
| Factor analysis in a model with rational expectations |
0 |
0 |
0 |
79 |
0 |
1 |
3 |
427 |
| Factor based index tracking |
0 |
0 |
5 |
158 |
0 |
0 |
13 |
401 |
| Factor-GMM estimation with large sets of possibly weak instruments |
0 |
0 |
4 |
104 |
0 |
1 |
9 |
235 |
| Factor‐Based Identification‐Robust Interference in IV Regressions |
1 |
1 |
1 |
11 |
2 |
2 |
2 |
39 |
| Fiscal forecasting: The track record of the IMF, OECD and EC |
0 |
0 |
0 |
12 |
1 |
1 |
2 |
680 |
| Forecast Bias and MSFE Encompassing |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
9 |
| Forecast Pooling for European Macroeconomic Variables |
0 |
0 |
0 |
32 |
1 |
1 |
3 |
171 |
| Forecasting EMU macroeconomic variables |
0 |
0 |
1 |
141 |
0 |
1 |
2 |
555 |
| Forecasting economic activity by Bayesian bridge model averaging |
0 |
0 |
4 |
39 |
0 |
1 |
7 |
107 |
| Forecasting economic activity with targeted predictors |
0 |
1 |
2 |
72 |
0 |
1 |
4 |
159 |
| Forecasting euro area variables with German pre-EMU data |
0 |
0 |
0 |
44 |
1 |
1 |
3 |
150 |
| Forecasting exchange rates with a large Bayesian VAR |
0 |
1 |
6 |
287 |
0 |
3 |
10 |
785 |
| Forecasting government bond yields with large Bayesian vector autoregressions |
0 |
1 |
2 |
139 |
0 |
1 |
5 |
349 |
| Forecasting gross domestic product growth with large unbalanced data sets: the mixed frequency three‐pass regression filter |
0 |
1 |
3 |
47 |
0 |
2 |
10 |
105 |
| Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods |
0 |
0 |
0 |
23 |
0 |
0 |
2 |
71 |
| Forecasting large datasets with Bayesian reduced rank multivariate models |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
144 |
| Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis |
0 |
0 |
1 |
10 |
0 |
0 |
2 |
28 |
| Forecasting with a DSGE Model of a Small Open Economy within the Monetary Union |
0 |
0 |
1 |
33 |
0 |
2 |
13 |
110 |
| Forecasting with factor-augmented error correction models |
0 |
0 |
3 |
89 |
3 |
4 |
14 |
234 |
| Foreword |
0 |
0 |
0 |
6 |
0 |
0 |
1 |
42 |
| Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty |
0 |
0 |
0 |
0 |
1 |
3 |
6 |
6 |
| Guest Editors’ Introduction to Special Issue on Encompassing |
0 |
0 |
0 |
14 |
0 |
0 |
1 |
61 |
| Have Standard VARS Remained Stable Since the Crisis? |
0 |
0 |
0 |
15 |
1 |
1 |
4 |
104 |
| Interpolation and backdating with a large information set |
0 |
0 |
1 |
77 |
0 |
0 |
2 |
222 |
| Introduction to advances in business cycle analysis and forecasting |
0 |
0 |
0 |
43 |
0 |
0 |
2 |
117 |
| Investigating Growth-at-Risk Using a Multicountry Nonparametric Quantile Factor Model |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
2 |
| LSM: A DSGE model for Luxembourg |
0 |
0 |
0 |
49 |
1 |
3 |
6 |
194 |
| Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors |
1 |
2 |
15 |
161 |
3 |
6 |
44 |
461 |
| Large time‐varying parameter VARs: A nonparametric approach |
0 |
0 |
2 |
17 |
1 |
3 |
8 |
84 |
| Leading Indicators for Euro‐area Inflation and GDP Growth* |
0 |
0 |
1 |
250 |
2 |
3 |
9 |
885 |
| Linear aggregation with common trends and cycles |
0 |
0 |
0 |
14 |
0 |
1 |
2 |
82 |
| MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area |
0 |
1 |
4 |
70 |
0 |
2 |
10 |
328 |
| MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area |
1 |
3 |
12 |
190 |
1 |
7 |
31 |
704 |
| MIXED‐FREQUENCY STRUCTURAL MODELS: IDENTIFICATION, ESTIMATION, AND POLICY ANALYSIS |
0 |
0 |
1 |
26 |
0 |
0 |
3 |
72 |
| MODELING HIGH-FREQUENCY FOREIGN EXCHANGE DATA DYNAMICS |
0 |
0 |
1 |
19 |
0 |
0 |
2 |
75 |
| Machine learning the macroeconomic effects of financial shocks |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
| Macro uncertainty in the long run |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
3 |
| Macroeconomic forecasting during the Great Recession: The return of non-linearity? |
0 |
1 |
3 |
40 |
1 |
3 |
7 |
153 |
| Macroeconomic forecasting in a multi‐country context |
1 |
1 |
3 |
15 |
2 |
2 |
6 |
34 |
| Macroeconomic forecasting in the Euro area: Country specific versus area-wide information |
0 |
0 |
1 |
335 |
2 |
2 |
5 |
834 |
| Markov-Switching MIDAS Models |
0 |
1 |
3 |
209 |
1 |
2 |
17 |
718 |
| Markov-Switching Three-Pass Regression Filter |
1 |
1 |
2 |
36 |
1 |
2 |
7 |
109 |
| Markov-switching mixed-frequency VAR models |
0 |
0 |
1 |
86 |
0 |
0 |
8 |
315 |
| Measuring Uncertainty and Its Impact on the Economy |
1 |
6 |
16 |
195 |
2 |
12 |
57 |
598 |
| Mixed frequency structural vector auto-regressive models |
0 |
0 |
1 |
47 |
0 |
0 |
3 |
109 |
| Mixed‐frequency models with moving‐average components |
0 |
0 |
1 |
13 |
0 |
0 |
4 |
56 |
| Model Selection for Nested and Overlapping Nonlinear, Dynamic and Possibly Mis‐specified Models* |
0 |
0 |
1 |
31 |
0 |
1 |
3 |
130 |
| Modelling and Forecasting Fiscal Variables for the Euro Area* |
0 |
0 |
0 |
86 |
1 |
1 |
3 |
279 |
| Modelling shifts in the wage-price and unemployment-inflation relationships in Italy, Poland and the UK |
0 |
0 |
0 |
60 |
0 |
0 |
0 |
203 |
| Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs |
0 |
0 |
1 |
70 |
0 |
0 |
10 |
227 |
| NOWCASTING GDP GROWTH IN A SMALL OPEN ECONOMY |
1 |
1 |
6 |
26 |
1 |
1 |
16 |
75 |
| Nowcasting tail risk to economic activity at a weekly frequency |
0 |
0 |
8 |
30 |
0 |
2 |
17 |
75 |
| No‐arbitrage priors, drifting volatilities, and the term structure of interest rates |
0 |
0 |
0 |
7 |
0 |
0 |
1 |
29 |
| On the Importance of Sectoral and Regional Shocks for Price‐Setting |
0 |
0 |
0 |
20 |
0 |
0 |
2 |
118 |
| POOLING VERSUS MODEL SELECTION FOR NOWCASTING GDP WITH MANY PREDICTORS: EMPIRICAL EVIDENCE FOR SIX INDUSTRIALIZED COUNTRIES |
0 |
0 |
0 |
0 |
0 |
0 |
7 |
182 |
| Path forecast evaluation |
0 |
1 |
2 |
65 |
0 |
1 |
7 |
263 |
| Point, interval and density forecasts of exchange rates with time varying parameter models |
0 |
0 |
0 |
14 |
0 |
1 |
1 |
61 |
| Pooling‐Based Data Interpolation and Backdating |
0 |
0 |
0 |
12 |
0 |
0 |
0 |
87 |
| Predicting Tail-Risks for the Italian Economy |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Principal components at work: the empirical analysis of monetary policy with large data sets |
0 |
0 |
1 |
456 |
0 |
1 |
10 |
1,343 |
| Public Capital and Economic Performance: Evidence from Italy |
0 |
0 |
0 |
0 |
0 |
4 |
11 |
305 |
| ROBUST DECISION THEORY AND THE LUCAS CRITIQUE |
0 |
0 |
0 |
11 |
0 |
0 |
4 |
69 |
| Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility |
1 |
1 |
1 |
49 |
1 |
3 |
9 |
213 |
| Regime switches in the risk–return trade-off |
0 |
1 |
1 |
33 |
0 |
3 |
5 |
117 |
| Regional inflation dynamics within and across euro area countries and a comparison with the United States |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
8 |
| Sectoral Survey‐based Confidence Indicators for Europe |
0 |
0 |
0 |
0 |
1 |
2 |
3 |
82 |
| Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility |
0 |
0 |
4 |
47 |
1 |
2 |
14 |
152 |
| Small-system modelling of real wages, inflation, unemployment and output per capita in Italy 1970-1994 |
0 |
1 |
1 |
165 |
0 |
1 |
1 |
848 |
| Some Consequences of Temporal Aggregation in Empirical Analysis |
0 |
0 |
0 |
0 |
2 |
4 |
11 |
542 |
| Some cautions on the use of panel methods for integrated series of macroeconomic data |
0 |
0 |
0 |
285 |
1 |
1 |
5 |
794 |
| Some stylized facts on non-systematic fiscal policy in the Euro area |
0 |
0 |
0 |
103 |
1 |
1 |
2 |
260 |
| Specification Choices in Quantile Regression for Empirical Macroeconomics |
0 |
0 |
0 |
0 |
0 |
0 |
5 |
5 |
| Structural FECM: Cointegration in large‐scale structural FAVAR models |
0 |
0 |
2 |
25 |
1 |
1 |
4 |
84 |
| Structural analysis with Multivariate Autoregressive Index models |
0 |
1 |
1 |
44 |
0 |
2 |
5 |
201 |
| Survey data as coincident or leading indicators |
0 |
0 |
0 |
58 |
0 |
0 |
3 |
176 |
| TAIL FORECASTING WITH MULTIVARIATE BAYESIAN ADDITIVE REGRESSION TREES |
0 |
1 |
3 |
8 |
1 |
2 |
8 |
26 |
| Tax shocks with high and low uncertainty |
1 |
1 |
3 |
19 |
1 |
1 |
4 |
77 |
| Testing for PPP: Should we use panel methods? |
0 |
0 |
1 |
364 |
1 |
1 |
2 |
1,056 |
| The Changing International Transmission of Financial Shocks: Evidence from a Classical Time‐Varying FAVAR |
0 |
1 |
5 |
72 |
1 |
4 |
14 |
213 |
| The effects of the monetary policy stance on the transmission mechanism |
1 |
1 |
11 |
122 |
4 |
4 |
21 |
259 |
| The global component of inflation volatility |
0 |
1 |
1 |
10 |
2 |
3 |
5 |
33 |
| The multiscale causal dynamics of foreign exchange markets |
0 |
0 |
1 |
51 |
0 |
0 |
3 |
180 |
| The reliability of real-time estimates of the euro area output gap |
0 |
0 |
1 |
89 |
0 |
1 |
5 |
345 |
| The transmission mechanism in a changing world |
0 |
0 |
1 |
175 |
0 |
2 |
6 |
534 |
| Time Variation in Macro‐Financial Linkages |
0 |
0 |
2 |
28 |
0 |
0 |
5 |
115 |
| Time-varying instrumental variable estimation |
0 |
0 |
2 |
18 |
0 |
0 |
3 |
57 |
| Time‐scale transformations of discrete time processes |
0 |
1 |
2 |
32 |
0 |
11 |
13 |
258 |
| Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials |
0 |
1 |
8 |
156 |
4 |
6 |
23 |
405 |
| Using low frequency information for predicting high frequency variables |
0 |
3 |
8 |
104 |
1 |
4 |
14 |
400 |
| Using time-varying volatility for identification in Vector Autoregressions: An application to endogenous uncertainty |
0 |
1 |
3 |
29 |
1 |
4 |
10 |
74 |
| Total Journal Articles |
18 |
59 |
270 |
8,900 |
88 |
229 |
1,058 |
29,788 |
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