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Real-Time Causal Inference
Opoku-Agyemang, Kweku A. - 2018
The paper highlights causal inference based on econometric measurement in real-time data environments. Each state has a probability of being realized in real-time. We define state selection bias as arising when real-time environments are ignored. We model indicator variables as measurements that...
Persistent link: https://www.econbiz.de/10012934379
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Obtaining and Predicting the Bounds of Realized Correlations
Grossmass, Lidan - 2013
The problem of estimation of realized correlation, which is analogous to realized covariance, is compounded by effects …
Persistent link: https://www.econbiz.de/10013082359
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Reliable real-time output gap estimates based on a modified Hamilton filter
Quast, Josefine; Wolters, Maik H. - 2020
We propose a simple modification of Hamilton’s (2018) time series filter that yields reliable and economically meaningful real-time output gap estimates. The original filter relies on 8 quarter ahead forecast errors of a simple autoregression of real GDP. While this approach yields a cyclical...
Persistent link: https://www.econbiz.de/10012233667
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A Frequency Domain Method for the Estimation and Forecasting of Spatial Big Data
Fernandez-Macho, Javier - 2019
This paper presents a method for estimating and forecasting global data, based on a novel space-time extension of a …
Persistent link: https://www.econbiz.de/10012868604
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Weighted-average least squares (WALS) : confidence and prediction intervals
De Luca, Giuseppe; Magnus, Jan R.; Peracchi, Franco - 2021
We extend the results of De Luca et al. (2021) to inference for linear regression models based on weighted … the performance of WALS with that of several competing estimators, including the unrestricted least-squares estimator … (with all auxiliary regressors) and the restricted least-squares estimator (with no auxiliary regressors), two post …
Persistent link: https://www.econbiz.de/10012510747
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Estimating and forecasting generalized fractional long memory stochastic volatility models
Peiris, Shelton; Asai, Manabu; McAleer, Michael - In: Journal of risk and financial management : JRFM 10 (2017) 4, pp. 1-16
This paper considers a flexible class of time series models generated by Gegenbauer polynomials incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the corresponding statistical properties of this model,...
Persistent link: https://www.econbiz.de/10011854876
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The geometric meaning of the notion of joint unpredictability of a bivariate VAR(1) stochastic process
Triacca, Umberto - In: Econometrics : open access journal 1 (2013) 3, pp. 207-216
This paper investigates, in a particular parametric framework, the geometric meaning of joint unpredictability for a bivariate discrete process. In particular, the paper provides a characterization of the joint unpredictability in terms of distance between information sets in an Hilbert space.
Persistent link: https://www.econbiz.de/10010237098
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A fast algorithm for finding the confidence set of large collections of models
Barde, Sylvain - 2015
The paper proposes a new algorithm for finding the confidence set of a collection of forecasts or prediction models. Existing numerical implementations for finding the confidence set use an elimination approach where one starts with the full collection of models and successively eliminates the...
Persistent link: https://www.econbiz.de/10011342917
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Weighted-average Least Squares (WALS) : Confidence and Prediction Intervals
De Luca, Giuseppe; Magnus, Jan R.; Peracchi, Franco - 2021
We extend the results of De Luca et al. (2021) to inference for linear regression models based on weighted … the performance of WALS with that of several competing estimators, including the unrestricted least-squares estimator … (with all auxiliary regressors) and the restricted least-squares estimator (with no auxiliary regressors), two post …
Persistent link: https://www.econbiz.de/10013228440
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Modified profile likelihood inference and interval forecast of the burst of financial bubbles
Filimonov, Vladimir; Demos, Guilherme; Heinimann, Hans … - 2016
We present a detailed methodological study of the application of the modified profile likelihood method for the calibration of nonlinear financial models characterised by a large number of parameters. We apply the general approach to the Log-Periodic Power Law Singularity (LPPLS) model of...
Persistent link: https://www.econbiz.de/10011514498