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on Econometric Time Series |
| By: | Matteo Barigozzi; Marc Hallin |
| Abstract: | Dynamic factor models have been developed out of the need of analyzing and forecasting time series in increasingly high dimensions. While mathematical statisticians faced with inference problems in high-dimensional observation spaces were focusing on the so-called spiked-model-asymptotics, econometricians adopted an entirely and considerably more effective asymptotic approach, rooted in the factor models originally considered in psychometrics. The so-called dynamic factor model methods, in two decades, has grown into a wide and successful body of techniques that are widely used in central banks, financial institutions, economic and statistical institutes. The objective of this chapter is not an extensive survey of the topic but a sketch of its historical growth, with emphasis on the various assumptions and interpretations, and a family tree of its main variants. |
| Keywords: | High-dimensional time series, factor models, panel data, forecasting |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:eca:wpaper:2013/364359&r=ets |
| By: | Sahil Teymurzade (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group); Robert Ślepaczuk (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance) |
| Abstract: | This paper implements automated trading strategies with buy/sell signals based on Autoregressive Integrated Moving Average (ARIMA) and Vector autoregression (VAR) models. ARIMA and VAR models are compared based on several forecast error measures and investment performance statistics. The data used in this thesis are daily closing prices of Dow Jones Industrial Average, NASDAQ Composite and NYSE Composite indices. The trading period covers 20 years of data from 2000-11-30 to 2020-11-30. The sensitivity analysis is made by changing the initial parameters to test how robust the methods are to these changes. Results show that although ARIMA model performed remarkably well during the volatile periods, VAR based strategy had better investment performance and was less robust to the changes compared to the ARIMA based strategy. Additionally, we have found that error metrics might be insufficient to evaluate performance of forecasting models, as VAR with higher forecast errors outperformed ARIMA model in algorithmic trading strategies. |
| Keywords: | ARIMA model, VAR model, time series analysis, algorithmic trading strategies, investment systems, statistical models, forecasting stock prices |
| JEL: | C4 C14 C45 C53 C58 G13 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:war:wpaper:2023-27&r=ets |
| By: | Joshua Chan |
| Abstract: | Bayesian vector autoregressions (BVARs) are the workhorse in macroeconomic forecasting. Research in the last decade has established the importance of allowing time-varying volatility to capture both secular and cyclical variations in macroeconomic uncertainty. This recognition, together with the growing availability of large datasets, has propelled a surge in recent research in building stochastic volatility models suitable for large BVARs. Some of these new models are also equipped with additional features that are especially desirable for large systems, such as order invariance -- i.e., estimates are not dependent on how the variables are ordered in the BVAR -- and robustness against COVID-19 outliers. Estimation of these large, flexible models is made possible by the recently developed equation-by-equation approach that drastically reduces the computational cost of estimating large systems. Despite these recent advances, there remains much ongoing work, such as the development of parsimonious approaches for time-varying coefficients and other types of nonlinearities in large BVARs. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.14438&r=ets |
| By: | Martin Magris; Alexandros Iosifidis |
| Abstract: | The Bayesian estimation of GARCH-family models has been typically addressed through Monte Carlo sampling. Variational Inference is gaining popularity and attention as a robust approach for Bayesian inference in complex machine learning models; however, its adoption in econometrics and finance is limited. This paper discusses the extent to which Variational Inference constitutes a reliable and feasible alternative to Monte Carlo sampling for Bayesian inference in GARCH-like models. Through a large-scale experiment involving the constituents of the S&P 500 index, several Variational Inference optimizers, a variety of volatility models, and a case study, we show that Variational Inference is an attractive, remarkably well-calibrated, and competitive method for Bayesian learning. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.03435&r=ets |
| By: | Puwasala Gamakumara; Edgar Santos-Fernandez; Priyanga Dilini Talagala; Rob J Hyndman; Kerrie Mengersen; Catherine Leigh |
| Abstract: | Collections of time series that are formed via aggregation are prevalent in many fields. These are commonly referred to as hierarchical time series and may be constructed cross-sectionally across different variables, temporally by aggregating a single series at different frequencies, or may even be generalised beyond aggregation as time series that respect linear constraints. When forecasting such time series, a desirable condition is for forecasts to be coherent, that is to respect the constraints. The past decades have seen substantial growth in this field with the development of reconciliation methods that not only ensure coherent forecasts but can also improve forecast accuracy. This paper serves as both an encyclopaedic review of forecast reconciliation and an entry point for researchers and practitioners dealing with hierarchical time series. The scope of the article includes perspectives on forecast reconciliation from machine learning, Bayesian statistics and probabilistic forecasting as well as applications in economics, energy, tourism, retail demand and demography. |
| Keywords: | aggregation, coherence, cross-temporal, hierarchical time series, grouped time series, temporal aggregation |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2023-10&r=ets |
| By: | George Athanasopoulos; Rob J Hyndman; Nikolaos Kourentzes; Anastasios Panagiotelis |
| Abstract: | Collections of time series that are formed via aggregation are prevalent in many fields. These are commonly referred to as hierarchical time series and may be constructed cross-sectionally across different variables, temporally by aggregating a single series at different frequencies, or may even be generalised beyond aggregation as time series that respect linear constraints. When forecasting such time series, a desirable condition is for forecasts to be coherent, that is to respect the constraints. The past decades have seen substantial growth in this field with the development of reconciliation methods that not only ensure coherent forecasts but can also improve forecast accuracy. This paper serves as both an encyclopaedic review of forecast reconciliation and an entry point for researchers and practitioners dealing with hierarchical time series. The scope of the article includes perspectives on forecast reconciliation from machine learning, Bayesian statistics and probabilistic forecasting as well as applications in economics, energy, tourism, retail demand and demography. |
| Keywords: | aggregation, coherence, cross-temporal, hierarchical time series, grouped time series, temporal aggregation |
| JEL: | C10 C14 C53 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2023-8&r=ets |
| By: | Dimitris Christopoulos; Peter McAdam; Elias Tzavalis |
| Abstract: | A new method refines the threshold vector autoregressive model used to study the effects of monetary policy. We contribute a new method for dealing with the problem of endogeneity of the threshold variable in threshold vector auto-regression (TVAR) models. Drawing on copula theory enables us to capture the dependence structure between the threshold variable and the vector of TVAR innovations, independently of the marginal distribution of the threshold variable. A Monte Carlo demonstrates that our method works well, and that ignoring threshold endogeneity leads to biased estimates of the threshold parameter and the variance-covariance error structure, thus invalidating dynamic analysis. As an application, we assess the effects of interest rate shocks on output and inflation: when “expected” inflation exceeds 3.6 percent, the effects of monetary policy are faster and stronger than otherwise. |
| Keywords: | VAR models; threshold models; monetary policy |
| JEL: | E40 E50 C32 |
| Date: | 2023–07–28 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedkrw:96762&r=ets |
| By: | Juan Tenorio; Wilder Pérez |
| Abstract: | In a context of ongoing change, “nowcasting” models based on Machine Learning (ML) algorithms deliver a noteworthy advantage for decision-making in both the public and private sectors due to its flexibility and ability to drive large amounts of data. This document presents projection models for the monthly GDP rate growth of Peru, which incorporate structured macroeconomic indicators with high-frequency unstructured sentiment variables. The window sampling comes from January 2007 to May 2023, including a total of 91 variables. By assessing six ML algorithms, the best predictors for each model were identified. The results reveal the high capacity of each ML model with unstructured data to provide more accurate and anticipated predictions than traditional time series models, where the outstanding models were Gradient Boosting Machine, LASSO, and Elastic Net, which achieved a prediction error reduction of 20% to 25% compared to the AR and Dynamic Factor Models (DFM) models. These results could be influenced by the analysis period, which includes crisis events featured by high uncertainty, where ML models with unstructured data improve significance. |
| Keywords: | nowcasting, machine learning, GDP growth |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:apc:wpaper:197&r=ets |
| By: | Xin Du; Kai Moriyama; Kumiko Tanaka-Ishii |
| Abstract: | This paper shows a novel machine learning model for realized volatility (RV) prediction using a normalizing flow, an invertible neural network. Since RV is known to be skewed and have a fat tail, previous methods transform RV into values that follow a latent distribution with an explicit shape and then apply a prediction model. However, knowing that shape is non-trivial, and the transformation result influences the prediction model. This paper proposes to jointly train the transformation and the prediction model. The training process follows a maximum-likelihood objective function that is derived from the assumption that the prediction residuals on the transformed RV time series are homogeneously Gaussian. The objective function is further approximated using an expectation-maximum algorithm. On a dataset of 100 stocks, our method significantly outperforms other methods using analytical or naive neural-network transformations. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.14536&r=ets |