|
on Econometric Time Series |
| By: | Joann Jasiak; Aryan Manafi Neyazi |
| Abstract: | We examine finite sample performance of the Generalized Covariance (GCov) residual-based specification test for semiparametric models with i.i.d. errors. The residual-based multivariate portmanteau test statistic follows asymptotically a $\chi^2$ distribution when the model is estimated by the GCov estimator. The test is shown to perform well in application to the univariate mixed causal-noncausal MAR, double autoregressive (DAR) and multivariate Vector Autoregressive (VAR) models. We also introduce a bootstrap procedure that provides the limiting distribution of the test statistic when the specification test is applied to a model estimated by the maximum likelihood, or the approximate or quasi-maximum likelihood under a parametric assumption on the error distribution. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.05373&r=ets |
| By: | Peter Knaus; Sylvia Fr\"uhwirth-Schnatter |
| Abstract: | Many current approaches to shrinkage within the time-varying parameter framework assume that each state is equipped with only one innovation variance for all time points. Sparsity is then induced by shrinking this variance towards zero. We argue that this is not sufficient if the states display large jumps or structural changes, something which is often the case in time series analysis. To remedy this, we propose the dynamic triple gamma prior, a stochastic process that has a well-known triple gamma marginal form, while still allowing for autocorrelation. Crucially, the triple gamma has many interesting limiting and special cases (including the horseshoe shrinkage prior) which can also be chosen as the marginal distribution. Not only is the marginal form well understood, we further derive many interesting properties of the dynamic triple gamma, which showcase its dynamic shrinkage characteristics. We develop an efficient Markov chain Monte Carlo algorithm to sample from the posterior and demonstrate the performance through sparse covariance modeling and forecasting of the returns of the components of the EURO STOXX 50 index. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.10487&r=ets |
| By: | Lajos Horv\'ath; Lorenzo Trapani |
| Abstract: | We propose a family of weighted statistics based on the CUSUM process of the WLS residuals for the online detection of changepoints in a Random Coefficient Autoregressive model, using both the standard CUSUM and the Page-CUSUM process. We derive the asymptotics under the null of no changepoint for all possible weighing schemes, including the case of the standardised CUSUM, for which we derive a Darling-Erdos-type limit theorem; our results guarantee the procedure-wise size control under both an open-ended and a closed-ended monitoring. In addition to considering the standard RCA model with no covariates, we also extend our results to the case of exogenous regressors. Our results can be applied irrespective of (and with no prior knowledge required as to) whether the observations are stationary or not, and irrespective of whether they change into a stationary or nonstationary regime. Hence, our methodology is particularly suited to detect the onset, or the collapse, of a bubble or an epidemic. Our simulations show that our procedures, especially when standardising the CUSUM process, can ensure very good size control and short detection delays. We complement our theory by studying the online detection of breaks in epidemiological and housing prices series. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.11710&r=ets |
| By: | Demetrescu, Matei; Rodrigues, Paulo MM; Taylor, AM Robert |
| Abstract: | We develop new tests for predictability, based on the Lagrange Multiplier [LM] principle, in the context of quantile regression [QR] models which allow for persistent and endogenous predictors driven by conditionally and/or unconditionally heteroskedastic errors. Of the extant predictive QR tests in the literature, only the moving blocks bootstrap implementation, due to Fan and Lee (2019), of theWald-type test of Lee (2016) can allow for conditionally heteroskedastic errors in the context of a QR model with persistent predictors. In common with all other tests in the literature it cannot, however, allow for any form of unconditionally heteroskedastic behaviour in the errors. The LM-based approach we adopt in this paper is obtained from a simple auxiliary linear test regression which facilitates inference based on established instrumental variable methods. We demonstrate that, as a result, the tests we develop, based on either conventional or heteroskedasticity-consistent standard errors in the auxiliary regression, are robust under the null hypothesis of no predictability to conditional heteroskedasticity and to unconditional heteroskedasticity in the errors driving the predictors, with no need for bootstrap implementation. Tests are developed both for predictability at a single quantile, and also jointly over a set of quantiles. Simulation results highlight the superior finite sample size and power properties of our proposed LM tests over the tests of Lee (2016) and Fan and Lee (2019) for both conditionally and unconditionally heteroskedastic errors. An empirical application to the equity premium for the S&P 500 highlights the practical usefulness of our proposed tests, uncovering significant evidence of predictability in the left and right tails of the returns distribution for a number of predictors containing information on market or firm risk. |
| Keywords: | Predictive regression, Conditional quantile, Unknown persistence, Endogeneity, Time-varying volatility |
| Date: | 2024–01–03 |
| URL: | http://d.repec.org/n?u=RePEc:esy:uefcwp:37486&r=ets |
| By: | Agathe Sadeghi; Achintya Gopal; Mohammad Fesanghary |
| Abstract: | A deeper comprehension of financial markets necessitates understanding not only the statistical dependencies among various entities but also the causal dependencies. This paper extends the Constraint-based Causal Discovery from Heterogeneous Data algorithm to account for lagged relationships in time-series data (an algorithm we call CD-NOTS), shedding light on the complex causal relations between different financial assets and variables. We compare the performance of different algorithmic choices, such as the choice of conditional independence test, to give general advice on the effective way to use CD-NOTS. Using the results from the simulated data, we apply CD-NOTS to a broad range of indices and factors in order to identify causal connections among the entities, thereby showing how causal discovery can serve as a valuable tool for factor-based investing, portfolio diversification, and comprehension of market dynamics. Further, we show our algorithm is a more effective alternative to other causal discovery algorithms since the assumptions of our algorithm are more realistic in terms of financial data, a conclusion we find is statistically significant. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.17375&r=ets |