nep-ets New Economics Papers
on Econometric Time Series
Issue of 2019‒03‒18
sixteen papers chosen by
Jaqueson K. Galimberti
KOF Swiss Economic Institute

  1. Nonparametric estimation and bootstrap inference on trends in atmospheric time series: an application to ethane By Marina Friedrich; Eric Beutner; Hanno Reuvers; Stephan Smeekes; Jean-Pierre Urbain; Whitney Bader; Bruno Franco; Bernard Lejeune; Emmanuel Mahieu
  2. Some Dynamic and Steady-State Properties of Threshold Autoregressions with Applications to Stationarity and Local Explosivity By Ahmed, M. F..; Satchell, S
  3. Forecasting bubbles with mixed causal-noncausal autoregressive models By Voisin, Elisa; Hecq, Alain
  4. Forecasting the Realized Variance in the Presence of Intraday Periodicity By Dumitru, Ana-Maria; Hizmeri, Rodrigo; Izzeldin, Marwan
  5. Time-varying Fiscal Multipliers Identified by Systematic Component: A Bayesian Approach to TVP-SVAR model By Iiboshi, Hirokuni; Iwata, Yasuharu; Kajita, Yuto; Soma, Naoto
  6. Forecasting Volatility in Cryptocurrency Markets By Mawuli Segnon; Stelios Bekiros
  7. Asymptotic F Tests under Possibly Weak Identification By Julian Martinez-Iriarte; Yixiao Sun; Xuexin Wang
  8. A cointegration model of money and wealth By Assenmacher-Wesche, Katrin; Beyer, Andreas
  9. Sovereign Spread Volatility and Banking Sector By Vivek Sharma; Edgar Silgado-Gómez
  10. Stock market linkages between the ASEAN countries, China and the US: a fractional cointegration approach By Guglielmo Maria Caporale; Luis A. Gil-Alana; Kefei You
  11. An application of dynamic factor models to nowcast regional economic activity in Spain By María Gil; Danilo Leiva-Leon; Javier J. Pérez; Alberto Urtasun
  12. Stock Market Volatility Clustering and Asymmetry in Africa: A Post Global Financial Crisis Evidence By Emenike, Kalu O.
  13. The nonlinear dynamics of corporate bond spreads: Regime-dependent effects of their determinants By Fischer, Henning; Stolper, Oscar
  14. Shapley regressions: a framework for statistical inference on machine learning models By Joseph, Andreas
  15. Nowcasting Recessions using the SVM Machine Learning Algorithm By Alexander James; Yaser S. Abu-Mostafa; Xiao Qiao
  16. High-dimensional sparse financial networks through a regularised regression model By Bernardi, Mauro; Costola, Michele

  1. By: Marina Friedrich; Eric Beutner; Hanno Reuvers; Stephan Smeekes; Jean-Pierre Urbain; Whitney Bader; Bruno Franco; Bernard Lejeune; Emmanuel Mahieu
    Abstract: Understanding the development of trends and identifying trend reversals in decadal time series is becoming more and more important. Many climatological and atmospheric time series are characterized by autocorrelation, heteroskedasticity and seasonal effects. Additionally, missing observations due to instrument failure or unfavorable measurement conditions are common in such series. This is why it is crucial to apply methods which work reliably under these circumstances. The goal of this paper is to provide a toolbox which can be used to determine the presence and form of changes in trend functions using parametric as well as nonparametric techniques. We consider bootstrap inference on broken linear trends and smoothly varying nonlinear trends. In particular, for the broken trend model, we propose a bootstrap method for inference on the break location and the corresponding changes in slope. For the smooth trend model we construct simultaneous confidence bands around the nonparametrically estimated trend. Our autoregressive wild bootstrap approach combined with a seasonal filter, is able to handle all issues mentioned above. We apply our methods to a set of atmospheric ethane series with a focus on the measurements obtained above the Jungfraujoch in the Swiss Alps. Ethane is the most abundant non-methane hydrocarbon in the Earth's atmosphere, an important precursor of tropospheric ozone and a good indicator of oil and gas production as well as transport. Its monitoring is therefore crucial for the characterization of air quality and of the transport of tropospheric pollution.
    Date: 2019–03
  2. By: Ahmed, M. F..; Satchell, S
    Abstract: The purpose of this paper is to investigate the dynamics and steady-state properties of threshold autoregressive models with exogenous states that follow Markovian processes; these processes are widely used in applied economics although their statistical properties have not been explored in detail. We use characteristic functions to carry out the analysis and this allows us to describe limiting distributions for processes not considered in the literature previously. We also calculate analytical expressions for some moments. Furthermore, we see that we can have locally explosive processes that are explosive in one regime whilst being strongly stationary overall. This is explored through simulation analysis where we also show how the distribution changes when the explosive state become more frequent although the overall process remains stationary. In doing so, we are able to relate our analysis to asset prices which exhibit similar distributional properties.
    Keywords: Threshold Auto-regression, Markov process
    JEL: C22 C32 C53
    Date: 2019–03–06
  3. By: Voisin, Elisa; Hecq, Alain
    Abstract: This paper investigates one-step ahead density forecasts of mixed causal-noncausal models. We compare the sample-based and the simulations-based approaches respectively developed by Gouriéroux and Jasiak (2016) and Lanne, Luoto, and Saikkonen (2012). We focus on explosive episodes and therefore on predicting turning points of bubbles bursts. We suggest the use of both methods to construct investment strategies based on how much probabilities are induced by the assumed model and by past behaviours. We illustrate our analysis on Nickel prices series.
    Keywords: Noncausal models, forecasting, predictive densities, bubbles, simulations-based forecasts
    JEL: C22 C53 C58
    Date: 2019–03–13
  4. By: Dumitru, Ana-Maria; Hizmeri, Rodrigo; Izzeldin, Marwan
    Abstract: This paper examines the impact of intraday periodicity on forecasting realized volatility using a heterogeneous autoregressive model (HAR) framework. We show that periodicity inflates the variance of the realized volatility and biases jump estimators. This combined effect adversely affects forecasting. To account for this, we propose a periodicity-adjusted model, HARP, where predictors are built from the periodicity-filtered data. We demonstrate empirically (using 30 stocks from various business sectors and the SPY for the period 2000--2016) and via Monte Carlo simulations that the HARP models produce significantly better forecasts, especially at the 1-day and 5-days ahead horizons.
    Keywords: realized volatility,forecast,intraday periodicity,heterogeneous autoregressive models
    JEL: C14 C22 C58 G17
    Date: 2019
  5. By: Iiboshi, Hirokuni; Iwata, Yasuharu; Kajita, Yuto; Soma, Naoto
    Abstract: Abstract This study estimates time varying fiscal multipliers from the aspect of fiscal policy rules derived from the systematic component along the line of “Agnostic Identification Procedure” proposed by Caldara and Kamps (2017) for the US economy between 1952:Q1-2018:Q1. To do so, we adopt time-varying parameter structural vector autoregressive (TVP-SVAR) with MCMC procedure by a Bayesian approach, and identify both of government spending and tax cut shocks using the zero and sign restrictions method proposed by Arias, Rubio-Ramirez and Waggoner (2018). And we compare those values with time varying version identified by standard sign restriction along the line of Mountford and Uhlig (2009). Our estimation reports that time-varying fiscal multipliers of output by government spending rule could be nearly double for one year but decline to unity after eight years, and seem to have been very stable for long terms such as sixty years. By contrast, those of tax cut rule are more fluctuate and negative for long run except the 1990’s.
    Keywords: Bayesian estimation, time-varying-parameter Structual VAR, Sign and Zero Restrictions
    JEL: C32 E32 E62
    Date: 2019–03
  6. By: Mawuli Segnon; Stelios Bekiros
    Abstract: In this paper, we revisit the stylized facts of cryptocurrency markets and propose various approaches for modeling the dynamics governing the mean and variance processes. We first provide the statistical properties of our proposed models and study in detail their forecasting performance and adequacy by means of point and density forecasts. We adopt two loss functions and the model confidence set (MSC) test to evaluate the predictive ability of the models and the likelihood ratio test to assess their adequacy. Our results confirm that cryptocurrency markets are characterized by regime shifting, long memory and multifractality. We find that the Markov switching multifractal (MSM) and FIGARCH models outperform other GARCH-type models in forecasting bitcoin returns volatility. Furthermore, combined forecasts improve upon forecasts from individual models.
    Keywords: Bitcoin, Multifractal processes, GARCH processes, Model confidence set, Likelihood ratio test
    JEL: C22 C53 C58
    Date: 2019–03
  7. By: Julian Martinez-Iriarte; Yixiao Sun; Xuexin Wang
    Abstract: This paper develops asymptotic F tests robust to weak identification and temporal dependence. The test statistics are modified versions of the S statistic of Stock and Wright (2000) and the K statistic of Kleibergen (2005), both of which are based on the continuous updating generalized method of moments. In the former case, the modification involves only a multiplicative degree-of-freedom adjustment. In the latter case, the modification involves an additional multiplicative adjustment that uses a J statistic for testing overidentification. By adopting fixed-smoothing asymptotics, we show that both the modified S statistic and the modified K statistic are asymptotically F-distributed. The asymptotic F theory accounts for the estimation errors in the underlying heteroskedasticity and autocorrelation robust variance estimators, which the asymptotic chi-squared theory ignores. Monte Carlo simulations show that the F approximations are much more accurate than the corresponding chi-squared approximations in finite samples.
    Keywords: Heteroskedasticity and autocorrelation robust variance, continuous updating GMM, F distribution, fixed-smoothing asymptotics, weak identification
    JEL: C12 C14 C32 C36
    Date: 2019–03–12
  8. By: Assenmacher-Wesche, Katrin; Beyer, Andreas
    Abstract: Extending the data set used in Beyer (2009) to 2017, we estimate I(1) and I(2) money demand models for euro area M3. After including two broken trends and a few dummies to account for shifts in the variables following the global financial crisis and the ECB's non-standard monetary policy measures, we find that the money demand and the real wealth relations identified in Beyer (2009) have remained remarkably stable throughout the extended sample period. Testing for price homogeneity in the I(2) model we find that the nominal-to-real transformation is not rejected for the money relation whereas the wealth relation cannot be expressed in real terms.
    Keywords: money demand,wealth,cointegration,vector error correction model,I(2) analysis
    JEL: E41 C32 C22
    Date: 2019
  9. By: Vivek Sharma (LUISS Guido Carli, Department of Economics and Finance); Edgar Silgado-Gómez (University of Rome "Tor Vergata" & European Central Bank)
    Abstract: Using structural vector autoregression augmented with stochastic volatility (SVAR-SV), we document that in late 2000s there were large spikes in volatility of spreads on peripheral eurozone government bonds. This increased volatility entailed a significant decline in bank credit to nonfinancial sector and real economic activity. We rationalize these results in a New Keynesian dynamic stochastic general equilibrium (DSGE) model with financial intermediation. In our framework, a rise in spread volatility erodes banks’ net worth and constrains their balance sheets. The banks respond by slashing their lending to real sector, dampening the economy as a whole. Results from the model match our empirical findings.
    Keywords: Sovereign Spread Volatility, Banks, SVAR-SV, NK-DSGE
    JEL: E32 E44 F30
    Date: 2019–03–08
  10. By: Guglielmo Maria Caporale; Luis A. Gil-Alana; Kefei You
    Abstract: This paper examines stock market integration between the ASEAN five and the US and China, respectively, over the period from November 2002 to March 2018. The linkages between both aggregate and financial sector stock indices (both weekly and monthly) are analysed using fractional integration and fractional cointegration methods. Further, recursive cointegration analysis is carried out for the weekly series to study the impact of the 2007-8 global financial crisis and the 2015 China stock market crash on the pattern of stock market co-movement. The main findings are the following. All stock indices exhibit long memory. There is cointegration between the ASEAN five and the US but almost none between the former and China, except between Indonesia and China in the case of the financial sector. The 2007-8 global financial crisis and the 2015 Chinese stock market plunge weakened the linkages between the ASEAN five and both China and the US. The implications of these results for market participants and policy makers are discussed.
    Keywords: Asian stock markets, financial integration, fractional integration, fractional cointegration
    JEL: C22 C32 G11 G15
    Date: 2019
  11. By: María Gil (Banco de España); Danilo Leiva-Leon (Banco de España); Javier J. Pérez (Banco de España); Alberto Urtasun (Banco de España)
    Abstract: The goal of this paper is to propose a model to produce nowcasts of GDP growth of Spanish regions, by means of dynamic factor models. This framework is capable to incorporate in a parsimonious way the relevant information available at the time that each forecast is made. We employ a Bayesian perspective to provide robust estimation of all the ingredients involved in the model. Accordingly, we introduce the Bayesian Factor model for Regions (BayFaR), which allows for the inclusion of missing data and combines quarterly data on regional real output growth (taken from the database of the AIReF and from the individual regional statistics institutes, when available) and monthly information associated to indicators of regional real activity. We apply the BayFaR to nowcast the GDP growth of the four largest regions of Spain, and illustrate the real-time nowcasting performance of the proposed framework for each case. We also apply the model to nowcast Spanish GDP in order to be able to assess the relative growth of each region.
    Keywords: regional activity, nowcasting, dynamic factor model
    JEL: C32 E37 R13
    Date: 2019–03
  12. By: Emenike, Kalu O.
    Abstract: This paper evaluates the nature of stock market volatility in Africa after the global financial crisis. Specifically, the paper examines volatility clustering and volatility asymmetry in aftermath of the global financial crisis for Botswana, Régionale des Valeurs Mobilières (BRVM), Egypt, Ghana, Kenya, Malawi, Mauritius, Morocco, Namibia, Nigeria, Rwanda, South Africa, Tunisia, Uganda, and Zambia. The paper employs autoregressive asymmetric generalized autoregressive conditional heteroscedasticity (AR(i)-GJR-GARCH(1,1)) model. The major findings are as follows: (i) there is evidence of volatility clustering in Africa stock markets returns after the global financial crisis, although with varying degrees; (ii) there is existence of volatility persistence in the African stock market returns after the global financial crisis except for few countries, which are not very persistent; (iii) after the global financial crisis, Africa stock markets returns are asymmetric, with negative shocks producing higher volatility in the immediate future than positive shocks of the same magnitude in some countries, and positive shocks producing higher volatility in other countries. The findings provide comparative basis for assessing market patterns, predicting market risk, and gauging market sentiment in Africa stock markets, as well as provide foreign portfolio managers required evidence for harvesting volatility through portfolio rebalancing for optimal performance.
    Keywords: stock market returns, volatility clustering, asymmetry, GARCH models, Africa
    JEL: C22 G0 N27
    Date: 2018–10–07
  13. By: Fischer, Henning; Stolper, Oscar
    Abstract: This paper studies the behavior of corporate bond spreads during different market regimes between 2004 and 2016. Applying a Markov-switching vector autoregressive (MS-VAR) model, we document that the dynamic impact of spread determinants varies substantially with market conditions. In periods of high volatility, systematic credit risk - rather than interest rate movements - contributes to driving up spreads. Moreover, while market-wide liquidity risk is not priced when volatility is low, it becomes a crucial factor during stress periods. Our results challenge the notion that spreads predominantly capture credit risk and suggest it must be reassessed during periods of financial distress.
    Keywords: corporate bond spreads,regime dependency,Markov switching,vector autoregression,credit spread puzzle
    JEL: C32 C34 C58 G12
    Date: 2019
  14. By: Joseph, Andreas (Bank of England)
    Abstract: Machine learning models often excel in the accuracy of their predictions but are opaque due to their non-linear and non-parametric structure. This makes statistical inference challenging and disqualifies them from many applications where model interpretability is crucial. This paper proposes the Shapley regression framework as an approach for statistical inference on non-linear or non-parametric models. Inference is performed based on the Shapley value decomposition of a model, a pay-off concept from cooperative game theory. I show that universal approximators from machine learning are estimation consistent and introduce hypothesis tests for individual variable contributions, model bias and parametric functional forms. The inference properties of state-of-the-art machine learning models — like artificial neural networks, support vector machines and random forests — are investigated using numerical simulations and real-world data. The proposed framework is unique in the sense that it is identical to the conventional case of statistical inference on a linear model if the model is linear in parameters. This makes it a well-motivated extension to more general models and strengthens the case for the use of machine learning to inform decisions.
    Keywords: Machine learning; statistical inference; Shapley values; numerical simulations; macroeconomics; time series
    JEL: C45 C52 C71 E47
    Date: 2019–03–08
  15. By: Alexander James; Yaser S. Abu-Mostafa; Xiao Qiao
    Abstract: We introduce a novel application of Support Vector Machines (SVM), an important Machine Learning algorithm, to determine the beginning and end of recessions in real time. Nowcasting, "forecasting" a condition about the present time because the full information about it is not available until later, is key for recessions, which are only determined months after the fact. We show that SVM has excellent predictive performance for this task, and we provide implementation details to facilitate its use in similar problems in economics and finance.
    Date: 2019–02
  16. By: Bernardi, Mauro; Costola, Michele
    Abstract: We propose a shrinkage and selection methodology specifically designed for network inference using high dimensional data through a regularised linear regression model with Spike-and-Slab prior on the parameters. The approach extends the case where the error terms are heteroscedastic, by adding an ARCH-type equation through an approximate Expectation-Maximisation algorithm. The proposed model accounts for two sets of covariates. The first set contains predetermined variables which are not penalised in the model (i.e., the autoregressive component and common factors) while the second set of variables contains all the (lagged) financial institutions in the system, included with a given probability. The financial linkages are expressed in terms of inclusion probabilities resulting in a weighted directed network where the adjacency matrix is built "row by row". In the empirical application, we estimate the network over time using a rolling window approach on 1248 world financial firms (banks, insurances, brokers and other financial services) both active and dead from 29 December 2000 to 6 October 2017 at a weekly frequency. Findings show that over time the shape of the out degree distribution exhibits the typical behavior of financial stress indicators and represents a significant predictor of market returns at the first lag (one week) and the fourth lag (one month).
    Keywords: VAR estimation,Financial Networks,Bayesian inference,Sparsity,Spike-and-Slab prior,Stochastic Search Variable Selection,Expectation-Maximisation
    Date: 2019

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