arXiv 1 Jul 2020 · Econometrics · 41 citations (OpenAlex)
arXiv:2007.00273 · PDF · DOI · OpenAlex · Extracted main text
Alternative data sets are widely used for macroeconomic nowcasting together with machine learning--based tools. The latter are often applied without a complete picture of their theoretical nowcasting properties. Against this background, this paper proposes a theoretically grounded nowcasting methodology that allows researchers to incorporate alternative Google Search Data (GSD) among the predictors and that combines targeted preselection, Ridge regularization, and Generalized Cross Validation. Breaking with most existing literature, which focuses on asymptotic in-sample theoretical properties, we establish the theoretical out-of-sample properties of our methodology and support them by Monte-Carlo simulations. We apply our methodology to GSD to nowcast GDP growth rate of several countries during various economic periods. Our empirical findings support the idea that GSD tend to increase nowcasting accuracy, even after controlling for official variables, but that the gain differs between periods of recessions and of macroeconomic stability.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | M. Carrasco and B. Rossi (2016) In-sample inference and forecasting in misspecified factor models | 1.000 | 7 | 3 | 100% |
| 2 | K.-C. Li (1986) Asymptotic optimality of $C_L$ and generalized cross-validation in ridge regression with application to spline smoothing | 0.928 | 4 | 3 | 100% |
| 3 | E. Barut, J. Fan, and A. Verhasselt (2016) Conditional sure independence screening | 0.874 | 6 | 2 | 100% |
| 4 | J. Bai and S. Ng (2008) Forecasting economic time series using targeted predictors | 0.874 | 5 | 2 | 100% |
| 5 | J. Boivin and S. Ng (2006) Are more data always better for factor analysis? | 0.644 | 2 | 2 | 100% |
| 6 | C. De Mol, D. Giannone, and L. Reichlin (2008) Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components? | 0.644 | 2 | 2 | 100% |
| 7 | E. Angelini, G. Camba-Mendez, D. Giannone, L. Reichlin, and G. Ruens… (2011) Short-term forecasts of euro area GDP growth | 0.511 | 2 | 1 | 100% |
| 8 | D. J. Lewis, K. Mertens, and J. H. Stock (2020) Monitoring Real Activity in Real Time: The Weekly Economic Index | 0.511 | 2 | 1 | 100% |
| 9 | D. W. Andrews (1991) Asymptotic optimality of generalized CL, cross-validation, and generalized cross-validation in regression with heteroskedastic e… | 0.405 | 1 | 1 | 100% |
| 10 | K. Aastveit and T. Trovik (2012) Nowcasting Norwegian GDP: the role of asset prices in a small open economy | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 33 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model | 0.585 | 3 | 1 |
| 2 | Nowcasting R&D Expenditures: A Machine Learning Approach | 0.405 | 1 | 1 |