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Chapter 62 A Practitioner's Approach to Estimating Intertemporal Relationships Using Longitudinal Data: Lessons from Applications in Wage Dynamics
MaCurdy, Thomas - In: Handbook of econometrics : volume 6A, (pp. 4057-4167). 2007
sampling inherent in survey longitudinal data, (3) incorporation of predetermined variables in estimation, and (4 …
Persistent link: https://www.econbiz.de/10014024953
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Sequential Monte Carlo Sampling for State Space Models
Wuthrich, Mario V. - 2016
The aim of these notes is to revisit sequential Monte Carlo (SMC) sampling. SMC sampling is a powerful simulation tool …
Persistent link: https://www.econbiz.de/10012993836
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Precision-based sampling for state space models that have no measurement error
Mertens, Elmar - 2023
instance of precision-based sampling methods that operate on the inverse variance-covariance matrix of the states (also known … other instances of precision-based sampling, computational gains are considerable. Relevant applications include trend …
Persistent link: https://www.econbiz.de/10014336195
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Model-based measurement of actual volatility in high-frequency data
Jungbacker, Borus; Koopman, Siem Jan - 2004
are based on importance sampling techniques. It is shown that such Monte Carlo techniques can be employed successfully for …
Persistent link: https://www.econbiz.de/10011342558
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Small sample properties of forecasts from autoregressive models under structural breaks
Timmermann, Allan; Pesaran, M. Hashem - 2003
Autoregressive models are used routinely in forecasting and often lead to better performance than more complicated models. However, empirical evidence is also suggesting that the autoregressive representations of many macroeconomic and financial time series are likely to be subject to structural...
Persistent link: https://www.econbiz.de/10011508088
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Estimating Low Sampling Frequency Risk Measure by High-Frequency Data
Wesselhöfft, Niels - 2020
Weekly, quarterly and yearly risk measures are crucial for risk reporting according to Basel III and Solvency II. For the respective data frequencies, the authors show in a simulation and back-test study that available data series are not sufficient in order to estimate Value at Risk and...
Persistent link: https://www.econbiz.de/10012827639
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The Gibbs Sampler with Particle Efficient Importance Sampling for State-Space Models
Grothe, Oliver - 2017
Monte Carlo (MC) approximation of the standard Gibbs procedure which uses sequential MC (SMC) importance sampling inside the … generic and easily implementable SMC approach known as Particle Efficient Importance Sampling (PEIS). By using SMC importance … sampling densities which are approximately fully globally adapted to the targeted density of the states, PEIS can substantially …
Persistent link: https://www.econbiz.de/10012970355
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The Algebra of Two Scales Estimation, and the S-TSRV : High Frequency Estimation That Is Robust to Sampling Times
Mykland, Per A. - 2018
effect of sampling times is cancelled to high order. This is a particular robustness property of the two scales construction …
Persistent link: https://www.econbiz.de/10012914838
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Efficient Importance Sampling Maximum Likelihood Estimation of Stochastic Differential Equations
Rossi, Eduardo; Pastorello, Sergio - 2011
based on efficient importance sampling (EIS) is detailed. Monte Carlo experiments, based on widely used diffusion processes …, evaluate its performance against an alternative importance sampling (IS) strategy, showing that EIS is at least equivalent, if …
Persistent link: https://www.econbiz.de/10014183458
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Assessing Point Forecast Bias Across Multiple Time Series : Measures and Visual Tools
Davydenko, Andrey; Goodwin, Paul - 2021
Measuring bias is important as it helps identify flaws in quantitative forecasting methods or judgmental forecasts. It can, therefore, potentially help improve forecasts. Despite this, bias tends to be under represented in the literature: many studies focus solely on measuring accuracy. Methods...
Persistent link: https://www.econbiz.de/10013314570