| Working Paper |
File Downloads |
Abstract Views |
| Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
| A Neural Phillips Curve and a Deep Output Gap |
0 |
0 |
0 |
17 |
0 |
2 |
6 |
28 |
| A Neural Phillips Curve and a Deep Output Gap |
0 |
1 |
5 |
43 |
2 |
3 |
22 |
91 |
| An Adaptive Moving Average for Macroeconomic Monitoring |
0 |
18 |
18 |
18 |
0 |
12 |
13 |
13 |
| Arctic Amplification of Anthropogenic Forcing: A Vector Autoregressive Analysis |
0 |
0 |
0 |
36 |
0 |
1 |
3 |
5 |
| Arctic Amplification of Anthropogenic Forcing: A Vector Autoregressive Analysis |
0 |
0 |
0 |
16 |
0 |
0 |
0 |
32 |
| Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice: Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models |
0 |
0 |
0 |
42 |
0 |
1 |
4 |
15 |
| Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice: Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
8 |
| Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice:Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models |
0 |
0 |
0 |
46 |
0 |
0 |
3 |
8 |
| Can Machine Learning Catch the COVID-19 Recession? |
0 |
0 |
0 |
8 |
0 |
1 |
3 |
29 |
| Can Machine Learning Catch the COVID-19 Recession? |
0 |
0 |
1 |
44 |
0 |
1 |
6 |
69 |
| Can Machine Learning Catch the COVID-19 Recession? |
0 |
0 |
0 |
0 |
0 |
0 |
5 |
13 |
| Can Machine Learning Catch the COVID-19 Recession? |
0 |
0 |
0 |
25 |
0 |
0 |
3 |
103 |
| Dual Interpretation of Machine Learning Forecasts |
0 |
1 |
1 |
1 |
1 |
1 |
8 |
8 |
| From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks |
2 |
2 |
3 |
24 |
4 |
8 |
15 |
21 |
| From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks |
0 |
0 |
1 |
14 |
1 |
3 |
8 |
14 |
| How is Machine Learning Useful for Macroeconomic Forecasting? |
0 |
1 |
3 |
15 |
2 |
5 |
9 |
47 |
| How is Machine Learning Useful for Macroeconomic Forecasting? |
0 |
1 |
5 |
340 |
2 |
4 |
16 |
968 |
| How is Machine Learning Useful for Macroeconomic Forecasting? |
0 |
1 |
2 |
57 |
0 |
2 |
6 |
100 |
| Macroeconomic Data Transformations Matter |
0 |
0 |
0 |
34 |
0 |
0 |
0 |
36 |
| Macroeconomic Data Transformations Matter |
0 |
0 |
0 |
18 |
0 |
0 |
0 |
47 |
| Macroeconomic Data Transformations Matter |
0 |
0 |
1 |
3 |
0 |
1 |
3 |
11 |
| Maximally Forward-Looking Core Inflation |
0 |
0 |
62 |
62 |
1 |
4 |
172 |
173 |
| Maximally Machine-Learnable Portfolios |
0 |
0 |
1 |
5 |
0 |
2 |
14 |
21 |
| Maximally Machine-Learnable Portfolios |
0 |
0 |
1 |
29 |
0 |
2 |
9 |
33 |
| On Spurious Causality, CO2, and Global Temperature |
0 |
0 |
0 |
7 |
0 |
0 |
4 |
23 |
| Optimal Combination of Arctic Sea Ice Extent Measures: A Dynamic Factor Modeling Approach |
0 |
0 |
0 |
18 |
0 |
2 |
2 |
33 |
| Optimal Combination of Arctic Sea Ice Extent Measures: A Dynamic Factor Modeling Approach |
0 |
0 |
0 |
5 |
0 |
0 |
1 |
25 |
| Prévision de l’activité économique au Québec et au Canada à l’aide des méthodes Machine Learning |
1 |
3 |
11 |
85 |
2 |
5 |
27 |
210 |
| Slow-Growing Trees |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
5 |
| Slow-Growing Trees |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
17 |
| The Anatomy of Out-of-Sample Forecasting Accuracy |
0 |
1 |
4 |
50 |
1 |
5 |
16 |
44 |
| The Anatomy of Out-of-Sample Forecasting Accuracy |
0 |
0 |
3 |
94 |
1 |
1 |
5 |
47 |
| The Macroeconomy as a Random Forest |
1 |
3 |
4 |
25 |
1 |
4 |
15 |
63 |
| The Macroeconomy as a Random Forest |
0 |
0 |
1 |
55 |
0 |
1 |
4 |
180 |
| Time-Varying Parameters as Ridge Regressions |
1 |
2 |
18 |
102 |
1 |
5 |
36 |
104 |
| To Bag is to Prune |
0 |
0 |
0 |
70 |
0 |
2 |
3 |
35 |
| To Bag is to Prune |
0 |
0 |
0 |
1 |
0 |
1 |
6 |
13 |
| When Will Arctic Sea Ice Disappear? Projections of Area, Extent, Thickness, and Volume |
0 |
0 |
0 |
1 |
0 |
1 |
3 |
12 |
| When Will Arctic Sea Ice Disappear? Projections of Area, Extent, Thickness, and Volume |
0 |
0 |
0 |
48 |
0 |
0 |
0 |
26 |
| When Will Arctic Sea Ice Disappear? Projections of Area, Extent, Thickness, and Volume |
0 |
0 |
0 |
11 |
1 |
2 |
5 |
56 |
| Total Working Papers |
5 |
34 |
145 |
1,474 |
20 |
82 |
458 |
2,786 |
LogEc is hosted by the
Örebro University School of Business.