Sparse structures with LASSO through Principal Components: forecasting GDP components in the short-run
Saulius Jokubaitis, Dmitrij Celov, Remigijus Leipus
备注：
Major update of the previous version: updated results, more benchmark models, inclusion of EU countries. Keywords: nowcasting, LASSO, adaptive LASSO, relaxed LASSO, principal components analysis, variable selection, GDP components; JEL Codes: C13, C38, C52, C53, C55
This paper aims to examine the use of sparse methods to forecast the real, in the chain-linked volume sense, expenditure components of the US and EU GDP in the short-run sooner than the national institutions of statistics officially release the data. We estimate current quarter nowcasts along with 1- and 2-quarter forecasts by bridging quarterly data with available monthly information announced with a much smaller delay. We solve the high-dimensionality problem of the monthly dataset by assuming sparse structures of leading indicators, capable of adequately explaining the dynamics of analyzed data. For variable selection and estimation of the forecasts, we use the sparse methods - LASSO together with its recent modifications. We propose an adjustment that combines LASSO cases with principal components analysis that deemed to improve the forecasting performance. We evaluate forecasting performance conducting pseudo-real-time experiments for gross fixed capital formation, private consumption, imports and exports over the sample of 2005-2019, compared with benchmark ARMA and factor models. The main results suggest that sparse methods can outperform the benchmarks and to identify reasonable subsets of explanatory variables. The proposed LASSO-PC modification show further improvement in forecast accuracy.
UK GDP data is published with a lag time of more than a month and it is often adjusted for prior periods. This paper contemplates breaking away from the historic GDP measure to a more dynamic method using Bank Account, Cheque and Credit Card payment transactions as possible predictors for faster and real time measure of GDP value. Historic timeseries data available from various public domain for various payment types, values, volume and nominal UK GDP was used for this analysis. Low Value Payments was selected for simple Ordinary Least Square Simple Linear Regression with mixed results around …
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Gross domestic product (GDP) is an important economic indicator that aggregates useful information to assist economic agents and policymakers in their decision-making process. In this context, GDP forecasting becomes a powerful decision optimization tool in several areas. In order to contribute in this direction, we investigated the efficiency of classical time series models, the state-space models, and the neural network models, applied to Brazilian gross domestic product. The models used were: a Seasonal Autoregressive Integrated Moving Average (SARIMA) and a Holt-Winters method, which are c…
This paper introduces structured machine learning regressions for high-dimensional time series data potentially sampled at different frequencies. The sparse-group LASSO estimator can take advantage of such time series data structures and outperforms the unstructured LASSO. We establish oracle inequalities for the sparse-group LASSO estimator within a framework that allows for the mixing processes and recognizes that the financial and the macroeconomic data may have heavier than exponential tails. An empirical application to nowcasting US GDP growth indicates that the estimator performs favorab…
Duke University（杜克大学）；State Farm（州立农业保险公司）；The University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Insper（Insper 商学院）
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