nep-ets New Economics Papers
on Econometric Time Series
Issue of 2018‒09‒03
thirteen papers chosen by
Jaqueson K. Galimberti
KOF Swiss Economic Institute

  1. Robust inference in structural VARs with long-run restrictions By Chevillon, Guillaume; Mavroeidis, Sophocles; Zhan, Zhaoguo
  2. Structural Estimation of Dynamic Macroeconomic Models using Higher-Frequency Financial Data By Max Ole Liemen; Michel van der Wel; Olaf Posch
  3. Inference for nonlinear state space models: A comparison of different methods applied to Markov-switching multifractal models By Lux, Thomas
  4. Maximum Likelihood Estimation in Possibly Misspeci ed Dynamic Models with Time-Inhomogeneous Markov Regimes By Demian Pouzo; Zacharias Psaradakis; Martin Sola
  5. About Local Projection Impulse Responde Function Reliability By Luca Brugnolini
  6. Forecasting with Dynamic Panel Data Models By Laura Liu; Hyungsik Moon; Frank Schorfheide
  7. Estimates of Potential Output and the Neutral Rate for the U.S. Economy By Ali Alichi; Rania A. Al-Mashat; Hayk Avetisyan; Jaromir Benes; Olivier Bizimana; Aram Butavyan; Robert Ford; Narek Ghazaryan; Vahagn Grigoryan; Mane Harutyunyan; Anahit Hovhannisyan; Edgar Hovhannisyan; Hayk Karapetyan; Mariam Kharaishvili; Douglas Laxton; Akaki Liqokeli; Karolina Matikyan; Gevorg Minasyan; Shalva Mkhatrishvili; Armen Nurbekyan; Andrei Orlov; Babken Pashinyan; Garik Petrosyan; Yekaterina Rezepina; Aleksandr Shirkhanyan; Tamta Sopromadze; Lusine Torosyan; Erik Vardanyan; Hou Wang; Jiaxiong Yao
  8. Is the Cycle the Trend? Evidence From the Views of International Forecasters By John C Bluedorn; Daniel Leigh
  9. Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models By Bal\'azs Csan\'ad Cs\'aji
  10. LASSO-Driven Inference in Time and Space By Chernozhukov, V.; Härdle, W.K.; Huang, C.; Wang, W.
  11. Efficient forecasting of electricity spot prices with expert and LASSO models By Bartosz Uniejewski; Rafal Weron
  12. Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts? By Grzegorz Marcjasz; Bartosz Uniejewski; Rafal Weron
  13. The transmission of international shocks to CIS economies : A Global VAR approach By Faryna, Oleksandr; Simola, Heli

  1. By: Chevillon, Guillaume (ESSEC Research Center, ESSEC Business School); Mavroeidis, Sophocles (Department of Economics and Institute for New Economic Thinking at the Oxford Martin School, Oxford University); Zhan, Zhaoguo (Department of Economics, Finance and Quantitative Analysis, Kennesaw State University)
    Abstract: Long-run restrictions are a very popular method for identifying structural vector autoregressions, but they suffer from weak identification when the data is very persistent, i.e., when the highest autoregressive roots are near unity. Near unit roots introduce additional nuisance parameters and make standard weak-instrument-robust methods of inference inapplicable. We develop a method of inference that is robust to both weak identi fication and strong persistence. The method is based on a combination of the Anderson-Rubin test with instruments derived by fi ltering potentially non-stationary variables to make them near stationary. We apply our method to obtain robust con fidence bands on impulse responses in two leading applications in the literature.
    Keywords: weak instruments; identification; SVARs; near unit roots; IVX
    JEL: C12 C32 E32
    Date: 2016–11–22
  2. By: Max Ole Liemen (Universität Hamburg); Michel van der Wel (Erasmus University Rotterdam); Olaf Posch (Universität Hamburg)
    Abstract: In this paper we show how high-frequency financial data can be used in a combined macro-finance framework to estimate the underlying structural parameters. Our formulation of the model allows for substituting macro variables by asset prices in a way that enables casting the relevant estimation equations partly (or completely) in terms of financial data. We show that using only financial data allows for identification of the majority of the relevant parameters. Adding macro data allows for identification of all parameters. In our simulation study, we find that it also improves the accuracy of the parameter estimates. In the empirical application we use interest rate, macro, and S&P500 stock index data, and compare the results using different combinations of macro and financial variables.
    Date: 2018
  3. By: Lux, Thomas
    Abstract: Nonlinear, non-Gaussian state space models have found wide applications in many areas. Since such models usually do not allow for an analytical representation of their likelihood function, sequential Monte Carlo or particle filter methods are mostly applied to estimate their parameters. Since such stochastic approximations lead to non-smooth likelihood functions, finding the best-fitting parameters of a model is a non-trivial task. In this paper, we compare recently proposed iterative filtering algorithms developed for this purpose with simpler online filters and more traditional methods of inference. We use a highly nonlinear class of Markov-switching models, the so called Markov-switching multifractal model (MSM), as our workhorse in the comparison of different optimisation routines. Besides the well-established univariate discrete-time MSM, we introduce univariate and multivariate continuous-time versions of MSM. Monte Carlo simulation experiments indicate that across a variety of MSM specifications, the classical Nelder-Mead or simplex algorithm appears still as more efficient and robust compared to a number of online and iterated filters. A very close competitor is the iterated filter recently proposed by Ionides et al. (2006) while other alternatives are mostly dominated by these two algorithms. An empirical application of both discrete and continuous-time MSM to seven financial time series shows that both models dominate GARCH and FIGARCH models in terms of in-sample goodness-of-fit. Out-of-sample forecast comparisons show in the majority of cases a clear dominance of the continuous-time MSM under a mean absolute error criterion, and less conclusive results under a mean squared error criterion.
    Keywords: partially observed Markov processes,state space models,Markov-switching mulitfracted model,nonlinear filtering,forecasting of volatility
    JEL: C20 G15
    Date: 2018
  4. By: Demian Pouzo; Zacharias Psaradakis; Martin Sola
    Abstract: This paper considers maximum likelihood (ML) estimation in a large class of models with hidden Markov regimes. We investigate consistency and local asymptotic normality of the ML estimator under general conditions which allow for autoregressive dynamics in the observable process, time-inhomogeneous Markov regime sequences, and possible model misspeci cation. A Monte Carlo study examines the nite-sample properties of the ML estimator. An empirical application is also discussed. Key words and phrases: Autoregressive model; consistency; hidden Markov model; Markov regimes; maximum likelihood; local asymptotic normality; misspeci ed models; time-inhomogenous Markov chain.
    Keywords: Autoregressive model; consistency; hidden Markov model; Markov regimes; maximum likelihood; local asymptotic normality; misspeci ed models; time-inhomogenous Markov chain.
    Date: 2016–12
  5. By: Luca Brugnolini (Central Bank of Malta's Research Department)
    Abstract: I compare the performance of the vector autoregressive (VAR) model impulse response function estimator with the Jordà (2005) local projection (LP) methodology. In a Monte Carlo experiment, I demonstrate that when the data generating process is a well-specified VAR, the standard impulse response function estimator is the best option. However, when the sample size is small, and the model lag-length is misspecified, I prove that the local projection estimator is a competitive alternative. Finally, I show how to improve the local projection performance by fixing the lag-length at each horizon.
    Keywords: VAR,information criteria,lag-length,Monte Carlo
    JEL: C32 C52 C53 E52
    Date: 2018–06–09
  6. By: Laura Liu (Federal Reserve Bank); Hyungsik Moon (Department of Economics, USC); Frank Schorfheide (Department of Economics, University of Pennsylvania)
    Abstract: This paper considers the problem of forecasting a collection of short time series using cross sectional information in panel data. We construct point predictors using Tweedie's formula for the posterior mean of heterogeneous coefficients under a correlated random effects distribution. This formula utilizes cross-sectional information to transform the unit-specific (quasi) maximum likelihood estimator into an approximation of the posterior mean under a prior distribution that equals the population distribution of the random coefficients. We show that the risk of a predictor based on a non-parametric estimate of the Tweedie correction is asymptotically equivalent to the risk of a predictor that treats the correlated-random-effects distribution as known (ratio-optimality). Our empirical Bayes predictor performs well compared to various competitors in a Monte Carlo study. In an empirical application we use the predictor to forecast revenues for a large panel of bank holding companies and compare forecasts that condition on actual and severely adverse macroeconomic conditions.
    Keywords: Bank Stress Tests, Empirical Bayes, Forecasting, Panel Data, Ratio Optimality, Tweedies Formula
    JEL: C11 C14 C23 C53 G21
    Date: 2016–12–21
  7. By: Ali Alichi; Rania A. Al-Mashat; Hayk Avetisyan; Jaromir Benes; Olivier Bizimana; Aram Butavyan; Robert Ford; Narek Ghazaryan; Vahagn Grigoryan; Mane Harutyunyan; Anahit Hovhannisyan; Edgar Hovhannisyan; Hayk Karapetyan; Mariam Kharaishvili; Douglas Laxton; Akaki Liqokeli; Karolina Matikyan; Gevorg Minasyan; Shalva Mkhatrishvili; Armen Nurbekyan; Andrei Orlov; Babken Pashinyan; Garik Petrosyan; Yekaterina Rezepina; Aleksandr Shirkhanyan; Tamta Sopromadze; Lusine Torosyan; Erik Vardanyan; Hou Wang; Jiaxiong Yao
    Abstract: Estimates of potential output and the neutral short-term interest rate play important roles in policy making. However, such estimates are associated with significant uncertainty and subject to significant revisions. This paper extends the structural multivariate filter methodology by adding a monetary policy block, which allows estimating the neutral rate of interest for the U.S. economy. The addition of the monetary policy block further improves the reliability of the structural multivariate filter.
    Keywords: United States;Western Hemisphere;Potential output;Macroeconomic Modeling, Neutral Rate, Model Construction and Estimation, Monetary Policy (Targets, Instruments, and Effects)
    Date: 2018–07–06
  8. By: John C Bluedorn; Daniel Leigh
    Abstract: We revisit the conventional view that output fluctuates around a stable trend by analyzing professional long-term forecasts for 38 advanced and emerging market economies. If transitory deviations around a trend dominate output fluctuations, then forecasters should not change their long-term output level forecasts following an unexpected change in current period output. By contrast, an analysis of Consensus Economics forecasts since 1989 suggest that output forecasts are super-persistent—an unexpected 1 percent upward revision in current period output typically translates into a revision of ten year-ahead forecasted output by about 2 percent in both advanced and emerging markets. Drawing upon evidence from the behavior of forecast errors, the persistence of actual output is typically weaker than forecasters expect, but still consistent with output shocks normally having large and permanent level effects.
    Date: 2018–07–13
  9. By: Bal\'azs Csan\'ad Cs\'aji
    Abstract: A standard model of (conditional) heteroscedasticity, i.e., the phenomenon that the variance of a process changes over time, is the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model, which is especially important for economics and finance. GARCH models are typically estimated by the Quasi-Maximum Likelihood (QML) method, which works under mild statistical assumptions. Here, we suggest a finite sample approach, called ScoPe, to construct distribution-free confidence regions around the QML estimate, which have exact coverage probabilities, despite no additional assumptions about moments are made. ScoPe is inspired by the recently developed Sign-Perturbed Sums (SPS) method, which however cannot be applied in the GARCH case. ScoPe works by perturbing the score function using randomly permuted residuals. This produces alternative samples which lead to exact confidence regions. Experiments on simulated and stock market data are also presented, and ScoPe is compared with the asymptotic theory and bootstrap approaches.
    Date: 2018–07
  10. By: Chernozhukov, V.; Härdle, W.K.; Huang, C.; Wang, W.
    Abstract: We consider the estimation and inference in a system of high-dimensional regression equations allowing for temporal and cross-sectional dependency in covariates and error processes, covering rather general forms of weak dependence. A sequence of large-scale regressions with LASSO is applied to reduce the dimensionality, and an overall penalty level is carefully chosen by a block multiplier bootstrap procedure to account for multiplicity of the equations and dependencies in the data. Correspondingly, oracle properties with a jointly selected tuning parameter are derived. We further provide high-quality de-biased simultaneous inference on the many target parameters of the system. We provide bootstrap consistency results of the test procedure, which are based on a general Bahadur representation for the Z-estimators with dependent data. Simulations demonstrate good performance of the proposed inference procedure. Finally, we apply the method to quantify spillover effects of textual sentiment indices in a financial market and to test the connectedness among sectors.
    Keywords: LASSO; time series; simultaneous inference; system of equations; Z-estimation; Bahadur representation; martingale decomposition
    Date: 2018
  11. By: Bartosz Uniejewski; Rafal Weron
    Abstract: Recent electricity price forecasting (EPF) studies suggest that the least absolute shrinkage and selection operator (LASSO) leads to well performing models, generally better than obtained from other variable selection schemes. Conducting an empirical study involving three expert models, two multi-parameter regression (called baseline) models and four variance stabilizing transformations, we discuss the optimal way of implementing the LASSO. We show that using a complex baseline model and a well chosen variance stabilizing transformation indeed leads to significant accuracy gains compared to the typically used EPF models.
    Keywords: Electricity spot price; Day-ahead market; Long-term seasonal component; LASSO; Automated variable selection; Variance stabilizing transformation
    JEL: C14 C22 C51 C53 Q47
    Date: 2018–06–29
  12. By: Grzegorz Marcjasz; Bartosz Uniejewski; Rafal Weron
    Abstract: A recent electricity price forecasting (EPF) study has shown that the Seasonal Component Artificial Neural Network (SCANN) modeling framework, which consists of decomposing a series of spot prices into a trend-seasonal and a stochastic component, modeling them independently and then combining their forecasts, can yield more accurate point predictions than an approach in which the same non-linear autoregressive NARX-type neural network is calibrated to the prices themselves. Here, considering two novel extensions of the SCANN concept to probabilistic forecasting, we find that (i) efficiently calibrated NARX networks can outperform their autoregressive counterparts, even without combining forecasts from many runs, and that (ii) in terms of accuracy it is better to construct probabilistic forecasts directly from point predictions, however, if speed is a critical issue, running quantile regression on combined point forecasts (i.e., committee machines) may be an option worth considering. Moreover, we confirm an earlier observation that averaging probabilities outperforms averaging quantiles when combining predictive distributions in EPF.
    Keywords: Electricity spot price; Probabilistic forecast; Combining forecasts; Long-term seasonal component; NARX neural network; Quantile regression
    JEL: C14 C22 C45 C51 C53 Q47
    Date: 2018–07–13
  13. By: Faryna, Oleksandr; Simola, Heli
    Abstract: This paper employs a Global Vector Auto Regressive (GVAR) model to study the evolution of the response of the Commonwealth of Independent States (CIS) to foreign output and oil price shocks. During a two-decade observation period, cross-country trade and financial linkages experience no-table changes. We find CIS countries highly sensitive to global and regional shocks, with that sensitivity increasing after the global financial crisis. CIS countries show strongest responses to output shocks originating in the US, Russia and within the region itself, but their sensitivity to euro area shocks also increases substantially. Despite growing trade relations with China, the responses of CIS countries to output shocks originating in China are still relatively moderate.
    JEL: C32 F42 F43 E32
    Date: 2018–08–30

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