nep-ets New Economics Papers
on Econometric Time Series
Issue of 2023‒03‒27
eleven papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Detecting Common Bubbles in Multivariate Mixed Causal-noncausal Models By Gianluca Cubadda; Alain Hecq; Elisa Voisin
  2. SVARs in the Frequency Domain using a Continuum of Restrictions By Alain Guay; Florian Pelgrin
  3. Dynamic Identification in VARs By Paul Beaudry; Fabrice Collard; Patrick Feve; Alain Guay; Franck Portier
  4. Improved Tests for Stock Return Predictability By Harvey, David I; Leybourne, Stephen J; Taylor, AM Robert
  5. Detecting Rough Volatility: A Filtering Approach By Camilla Damian; R\"udiger Frey
  6. Sequential Estimation of Multivariate Factor Stochastic Volatility Models By Giorgio Calzolari; Roxana Halbleib; Christian M\"ucher
  7. Anchoring Long-term VAR Forecasts Based On Survey Data and State-space Models By Marta Baltar Moreira Areosa; Wagner Piazza Gaglianone
  8. Fat Tailed DSGE Models: A Survey and New Results By Dave, Chetan; Sorge, Marco
  9. State-Dependent Local Projections: Understanding Impulse Response Heterogeneity By James Cloyne; Òscar Jordà; Alan M. Taylor
  10. The performance of time series forecasting based on classical and machine learning methods for S&P 500 index By Maudud Hassan Uzzal; Robert Ślepaczuk
  11. Common Drivers of Commodity Futures? By Tom Dudda; Tony Klein; Duc Khuong Nguyen; Thomas Walther

  1. By: Gianluca Cubadda (CEIS, Università di Roma ‘Tor Vergata’); Alain Hecq (Maastricht University); Elisa Voisin (Maastricht University)
    Abstract: This paper proposes concepts and methods to investigate whether the bubble patterns observed in individual time series are common among them. Having established the conditions under which common bubbles are present within the class of mixed causal-noncausal vector autoregressive models, we suggest statistical tools to detect the common locally explosive dynamics in a Student-t distribution maximum likelihood framework. The performances of both likelihood ratio tests and information criteria are investigated in a Monte Carlo study. Finally, we evaluate the practical value of our approach by an empirical application on three commodity prices.
    Keywords: Forward-looking models, bubbles, co-movements
    JEL: C32
    Date: 2023–02–27
  2. By: Alain Guay (University of Quebec in Montreal); Florian Pelgrin (EDHEC Business School)
    Abstract: This paper proposes a joint methodology for the identification and inference of structural vector autoregressive models in the frequency domain. We show that identifying restrictions can be written naturally as an asymptotic least squares problem (Gourieroux, Monfort and Trognon, 1985) in which there is a continuum of nonlinear estimating equations. Following Carrasco and Florens (2000), we then develop a continuum asymptotic least squares estimator (C-ALS) that exploits efficiently the continuum of estimating equations thereby allowing to obtain optimal consistent estimates of impulse responses and reliable confidence intervals. Moreover the identifying restrictions can be formally tested using an appropriate J-stat and the frequency band can be selected with a data-driven procedure. Finally, we provide some new results using Monte Carlo simulations and applications regarding the hours-productivity debate and the impact of news shocks.
    Keywords: SVARs, Frequency domain, Asymptotic least squares, Continuum of identifying restrictions.
    JEL: C12 C32 C51
    Date: 2021–08
  3. By: Paul Beaudry (Bank of Canada); Fabrice Collard (Toulouse School of Economics); Patrick Feve (Toulouse School of Economics); Alain Guay (University of Quebec in Montreal); Franck Portier (University College London)
    Abstract: Most macroeconomic models, both fully structural models as well as SVAR models, view economic outcomes as the product of a combination of endogenous and exogenous dynamic forces. In particular, the exogenous forces are generally modeled as a set of linearly independent dynamics processes. In this paper we begin by showing that this dual dynamic structure is sufficient to identify the entire set of structural impulse responses inherent to any such model. No extra restrictions are necessary. We then use this observation to suggest how it can be used to evaluate common SVAR restrictions (impact restrictions, long-run restrictions and proxy-VAR), as well as help transpire the role of cross-equation restrictions inherent to more structural models.
    Keywords: Structural Shocks, Dynamic Identification, SVARs, DSGE models.
    JEL: C32 E32
    Date: 2022–11
  4. By: Harvey, David I; Leybourne, Stephen J; Taylor, AM Robert
    Abstract: Predictive regression methods are widely used to examine the predictability of (excess) stock returns by lagged financial variables characterised by unknown degrees of persistence and endogeneity. We develop a new hybrid test for predictability in these circumstances based on simple regression t-statistics. Where the predictor is endogenous, the optimal, but infeasible, test for predictability is based on the t-statistic on the lagged predictor in the basic predictive regression augmented with the current period innovation driving the predictor. We propose a feasible version of this augmented test, designed for the case where the predictor is an endogenous near-unit root process, using a GLS-based estimate of the innovation used in the infeasible test regression. The limiting null distribution of this statistic depends on both the endogeneity correlation parameter and the local-to-unity parameter characterising the predictor. A method for obtaining asymptotic critical values is discussed and response surfaces are provided. We compare the asymptotic power properties of the feasible augmented test with those of a (non-augmented) t-test recently considered in Harvey et al. (2021) and show that the augmented test is more powerful in the strongly persistent predictor case. We then propose using a weighted combination of the augmented statistic and the t-statistic of Harvey et al. (2021), where the weights are obtained using the p-values from a unit root test on the predictor. We find this can further improve asymptotic power in cases where the predictor has persistence at or close to that of a unit root process. Our final hybrid testing procedure then embeds the weighted statistic within a switching-based procedure which makes use of a standard predictive regression t-test, compared with standard normal critical values, when there is evidence for the predictor being weakly persistent. Monte Carlo simulations suggest that overall our new hybrid test displays superior finite sample performance to comparable extant tests.
    Keywords: predictive regression; augmented regression; persistence; endogeneity; weighted statistics
    Date: 2023–03–08
  5. By: Camilla Damian; R\"udiger Frey
    Abstract: In this paper, we focus on the estimation of historical volatility of asset prices from high-frequency data. Stochastic volatility models pose a major statistical challenge: since in reality historical volatility is not observable, its current level and, possibly, the parameters governing its dynamics have to be estimated from the observable time series of asset prices. To complicate matters further, recent research has analyzed the rough behavior of volatility time series to challenge the common assumption that the volatility process is a Brownian semimartingale. In order to tackle the arising inferential task efficiently in this setting, we use the fact that a fractional Brownian motion can be represented as a superposition of Markovian semimartingales (Ornstein-Uhlenbeck processes) and we solve the filtering (and parameter estimation) problem by resorting to more standard techniques, such as particle methods.
    Date: 2023–02
  6. By: Giorgio Calzolari; Roxana Halbleib; Christian M\"ucher
    Abstract: We provide a simple method to estimate the parameters of multivariate stochastic volatility models with latent factor structures. These models are very useful as they alleviate the standard curse of dimensionality, allowing the number of parameters to increase only linearly with the number of the return series. Although theoretically very appealing, these models have only found limited practical application due to huge computational burdens. Our estimation method is simple in implementation as it consists of two steps: first, we estimate the loadings and the unconditional variances by maximum likelihood, and then we use the efficient method of moments to estimate the parameters of the stochastic volatility structure with GARCH as an auxiliary model. In a comprehensive Monte Carlo study we show the good performance of our method to estimate the parameters of interest accurately. The simulation study and an application to real vectors of daily returns of dimensions up to 148 show the method's computation advantage over the existing estimation procedures.
    Date: 2023–02
  7. By: Marta Baltar Moreira Areosa; Wagner Piazza Gaglianone
    Abstract: The objective of this paper is to forecast Brazilian inflation using a hybrid approach that combines a standard Vector Autoregression (VAR) model with expectations from surveys of consumers or professional forecasters. We cast a VAR model with parameter restriction into a state-space setup, where the long-run forecast from the model matches the long-run survey prediction. The proposed method also allows for exogenous variables in the system of equations as a way to enlarge the information set, and is designed to quickly adapt the multi-step-ahead forecasts in response to new survey information. An empirical exercise with Brazilian data illustrates the usefulness of the method. The results using a pre-COVID-19 sample indicate forecasts obtained from the proposed model prevail over traditional methods at longer horizons, thus confirming the benefits of using forward-looking information from survey in the forecasting process. The main reason is that the method incorporates relevant transformations observed in the Brazilian economy in recent years, such as monetary policy credibility gains and lower inflation targets. In turn, the results based on the full sample, up to August 2022, show larger forecast errors after the pandemic, which caused huge outliers in macroeconomic variables world-wide. Altogether, these findings offer a valuable contribution to applied macroeconomics, especially with regard to forecasting inflation in Brazil using VARs and survey data.
    Date: 2023–02
  8. By: Dave, Chetan (University of Alberta, Department of Economics); Sorge, Marco (University of Salerno)
    Abstract: We review recent advances in dynamic stochastic general equilibrium theory concerned with the emergence of fat tailed time series distributions. Focusing on mechanisms that are firmly grounded in structural equilibrium models, we provide a common reference framework to organize existing contributions according to whether they entail extreme business cycle swings as an endogenous response to small and short-lived shocks ('thin in, fat out'), or rather as an automatic consequence of large and/or heteroskedastic exogenous impulses ('fat in, fat out'). Within the former class, non-Gaussian features of equilibrium patterns can endogenously emerge in fully rational, Gaussian environments. Using an empirically plausible real business cycle framework, we also report novel simulation-based evidence that helps reconcile theoretical predictions with the documented higher-order properties of time-series data for output measures.
    Keywords: non-Gaussian distributions; fat tails; DSGE models; minimum distance estimation
    JEL: E30 E40 E70
    Date: 2023–02–28
  9. By: James Cloyne; Òscar Jordà; Alan M. Taylor
    Abstract: An impulse response is the dynamic average effect of an intervention across horizons. We use the well-known Kitagawa-Blinder-Oaxaca decomposition to explore a response’s heterogeneity over time and over states of the economy. This can be implemented with a simple extension to the usual local projection specification that nevertheless keeps the model linear in parameters. Using our new decomposition-based approach, we show how to unpack heterogeneity in the fiscal multiplier, an object that at any point in time may depend on a number of potentially correlated factors, including existing economic conditions and the monetary response. In our application, the fiscal multiplier varies considerably with monetary policy: it can be as small as zero, or as large as 2, depending on the degree of monetary offset.
    JEL: C54 C99 E32 E62 H20 H5
    Date: 2023–02
  10. By: Maudud Hassan Uzzal (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group); Robert Ślepaczuk (University of Warsaw, Faculty of Economic Sciences, Department of Quantitative Finance, Quantitative Finance Research Group)
    Abstract: Based on one step ahead forecasts, this study compares the forecasting abilities of the traditional technique (ARIMA) with recurrent neural network (LSTM). In order to check the possible use of these forecasts in different asset management methods, these forecasts are afterwards included into trading signals of investment strategies. As a benchmark method, the Random Walk model producing naive forecasts has been utilized. This research examines daily data from the S&P 500 index for 20 years, from 2000 to 2020, and it includes information on some significant market turbulence. The methods were tested in terms of robustness to changes in parameters and hyperparameters and evaluated based on various error metrics (MAE, MAPE, RMSE MSE). The results show that ARIMA outperforms LSTM in terms of one step ahead forecasts. Finally, LSTM model with a variety of hyperparameters - including a number of epochs, a loss function, an optimizer, activation functions, a number of units, a batch size, and a learning rate - was tested in order to check its robustness.
    Keywords: deep learning, recurrent neural networks, ARIMA, algorithmic investment strategies, trading systems, LSTM, walk-forward process, optimization
    JEL: C4 C14 C45 C53 C58 G13
    Date: 2023
  11. By: Tom Dudda; Tony Klein; Duc Khuong Nguyen; Thomas Walther
    Abstract: We study potential drivers for a large cross-section of commodity futures. Unlike previous studies, we examine the effect of monthly drivers on daily returns using mixed- frequency Granger causality tests. We find real economic activity as a main driver on a monthly basis, whereas financial variables seem to affect returns at daily frequency. The linkages are time-varying for various stages of the financialization of commodity markets with an overall dissipating impact in the recent period of de-financialization. As our results strongly differ from traditional low-frequency Granger causality tests under the temporal aggregation of futures returns, we show the economic value of accessing infor- mation at a higher frequency in an out-of-sample trading study. Our findings emphasize the importance of using mixed-frequency techniques to uncover relationships between monthly-published macroeconomic variables and commodity prices.
    Keywords: Commodity futures, VAR, Granger causality, Mixed data sampling
    Date: 2022

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