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Although many macroeconomic time series are assumed to follow nonlinear processes, nonlinear models often do not provide better predictions than their linear counterparts. Furthermore, such models easily become very complex and difficult to estimate. The aim of this study is to investigate...
Persistent link: https://www.econbiz.de/10010434848
We introduce and investigate some properties of a class of nonlinear time series models based on the moving sample quantiles in the autoregressive data generating process. We derive a test fit to detect this type of nonlinearity. Using the daily realized volatility data of Standard & Poor's 500...
Persistent link: https://www.econbiz.de/10010478989
that the LMACP nicely captures salient features of bid-ask spreads like the strong autocorrelation and discreteness of …
Persistent link: https://www.econbiz.de/10009229669
estimation of the state vector and of the time-varying parameters. We use this method to study the timevarying relationship …
Persistent link: https://www.econbiz.de/10012156426
This study focuses on the question whether nonlinear transformation of lagged time series values and residuals are able to systematically improve the average forecasting performance of simple Autoregressive models. Furthermore it investigates the potential superior forecasting results of a...
Persistent link: https://www.econbiz.de/10009310287
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
We develop a new targeted maximum likelihood estimation method that provides improved forecasting for misspecified …-validation procedure. In a set of Monte Carlo experiments we reveal that the estimation method can significantly improve the forecasting …
Persistent link: https://www.econbiz.de/10012416341
The paper compares one-period ahead forecasting performance of linear vector-autoregressive (VAR) models and single-equation Markov-switching (MS) models for two cases: when leading information is available and when it is not. The results show that single-equation MS models tend to perform...
Persistent link: https://www.econbiz.de/10013147524
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
In this paper, I apply univariate and vector autoregressive (VAR) models to forecast inflation in Vietnam. To investigate the forecasting performance of the models, two naive benchmark models (one is a variant of a random walk and the other is an autoregressive model) are first built based on...
Persistent link: https://www.econbiz.de/10011606109