nep-ets New Economics Papers
on Econometric Time Series
Issue of 2019‒04‒08
eleven papers chosen by
Jaqueson K. Galimberti
KOF Swiss Economic Institute

  1. Predictability, Real Time Estimation, and the Formulation of Unobserved Components Models By Tommaso Proietti
  2. The uniform validity of impulse response inference in autoregressions By Atsushi Inoue; Lutz Kilian
  3. Quantilograms under Strong Dependence By Lee, L.; Linton, O.; Whang, Y-J.;
  4. Estimation and Inference in Semiparametric Quantile Factor Models By Ma, S.; Linton, O.; Gao, J.
  5. Wild Bootstrap and Asymptotic Inference with Multiway Clustering By James G. MacKinnon; Morten Ø. Nielsen; Matthew D. Webb
  6. Does U.K.’s Real GDP have a Unit Root? Evidence from a Multi-Century Perspective By Giorgio Canarella; Rangan Gupta; Stephen M. Miller; Tolga Omay
  7. Measuring real and financial cycles in Luxembourg: An unobserved components approach By Paolo Guarda; Alban Moura
  8. Exchange Rate Pass-through to Prices : Bayesian VAR Evidence for Ghana By Asafo, Shuffield Seyram
  9. Forecasting the Volatilities of Philippine Stock Exchange Composite Index Using the Generalized Autoregressive Conditional Heteroskedasticity Modeling By Novy Ann M. Etac; Roel F. Ceballos
  10. Parametric identification of the dynamics of inter-sectoral balance: modelling and forecasting By Olena Kostylenko; Helena Sofia Rodrigues; Delfim F. M. Torres
  11. Global financial cycles since 1880 By Potjagailo, Galina; Wolters, Maik H.

  1. By: Tommaso Proietti (CEIS & DEF, University of Rome "Tor Vergata")
    Abstract: The formulation of unobserved components models raises some relevant interpretative issues, owing to the existence of alternative observationally equivalent specifications, differing for the timing of the disturbances and their covariance matrix. We illustrate them with reference to unobserved components models with ARMA(m;m) reduced form, performing the decomposition of the series into an ARMA(m; q) signal, q m, and a noise component. We provide a characterization of the set of covariance structures that are observationally equivalent, when the models are formulated both in the future and the contemporaneous forms. Hence, we show that, while the point predictions and the contemporaneous real time estimates are invariant to the specification of the disturbances covariance matrix, the reliability cannot be identified, except for special cases requiring q
    Keywords: ARMA models,Steady State Kalman filter,Correlated Components,Nonfundamentalness
    JEL: C22 C51 C53
    Date: 2019–03–22
  2. By: Atsushi Inoue (Vanderbilt University); Lutz Kilian (University of Michigan)
    Abstract: Existing proofs of the asymptotic validity of conventional methods of impulse response inference based on higher-order autoregressions are pointwise only. In this paper, we establish the uniform asymptotic validity of conventional asymptotic and bootstrap inference about individual impulse responses and vectors of impulse responses at fixed horizons. For inference about vectors of impulse responses based on Wald test statistics to be uniformly valid, lag-augmented autoregressions are required, whereas inference about individual impulse responses is uniformly valid under weak conditions even without lag augmentation. We introduce a new rank condition that ensures the uniform validity of inference on impulse responses and show that this condition holds under weak conditions. Simulations show that the highest finite-sample accuracy is achieved when bootstrapping the lag-augmented autoregression using the bias adjustments of Kilian (1999). The resulting confidence intervals remain accurate even at long horizons. We provide a formal asymptotic justification for this result.
    Keywords: Impulse response, autoregression, lag augmentation, asymptotic normality, bootstrap, uniform inference
    JEL: C22 C52
    Date: 2019–03–25
  3. By: Lee, L.; Linton, O.; Whang, Y-J.;
    Abstract: We develop the limit theory of the quantilogram and cross-quantilogram under long memory. We establish the sub-root-n central limit theorems for quantilograms that depend on nuisance parameters. We propose a moving block bootstrap (MBB) procedure for inference and we establish its consistency thereby enabling a consistent confidence interval construction for the quantilograms. The newly developed reduction principles for the quantilograms serve as the main technical devices used to derive the asymptotics and establish the validity of MBB. We report some simulation evidence that our methods work satisfactorily. We apply our method to quantile predictive relations between financial returns and long-memory predictors.
    Keywords: Long Memory, Moving Block Bootstrap, Nonlinear Dependence, Quantilogram and Cross-Quantilgoram, Reduction Principle
    JEL: C22
  4. By: Ma, S.; Linton, O.; Gao, J.
    Abstract: We consider a semiparametric quantile factor panel model that allows observed stock-specific characteristics to affect stock returns in a nonlinear time-varying way, extending Connor, Hagmann,and Linton (2012) to the quantile restriction case. We propose a sieve-based estimation methodology that is easy to implement. We provide tools for inference that are robust to the existence of moments and to the form of weak cross-sectional dependence in the idiosyncratic error term. We apply our method to daily stock return data where we find significant evidence of nonlinearity in many of the characteristic exposure curves.
    Keywords: Cross-Sectional Dependence, Fama-French Model, Inference, Quantile, Sieve Estimation
    JEL: C12
    Date: 2019–04–01
  5. By: James G. MacKinnon (Queen's University); Morten Ø. Nielsen (Queen's University and CREATES); Matthew D. Webb (Carleton University)
    Abstract: We study two cluster-robust variance estimators (CRVEs) for regression models with clustering in two dimensions and give conditions under which t-statistics based on each of them yield asymptotically valid inferences. In particular, one of the CRVEs requires stronger assumptions about the nature of the intra-cluster correlations. We then propose several wild bootstrap procedures and state conditions under which they are asymptotically valid for each type of t-statistic. Extensive simulations suggest that using certain bootstrap procedures with one of the t-statistics generally performs very well. An empirical example confirms that bootstrap inferences can differ substantially from conventional ones
    Keywords: CRVE, grouped data, clustered data, cluster-robust variance estimator, two-way clustering, wild cluster bootstrap, robust inference
    JEL: C15 C21 C23
    Date: 2019–03
  6. By: Giorgio Canarella (Department of Economics, University of Nevada, Las Vegas, 89154-6005, United States); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); Stephen M. Miller (Department of Economics, University of Nevada, Las Vegas, 89154-6005, United States); Tolga Omay (Department of Economics, Atilim University, 06830 Kızılçaşar, Gölbaşı Ankara, Turkey)
    Abstract: We employ the nonlinear unit-root test recently developed by Omay et al. (2018), as well as other linear and nonlinear tests, to examine the stationarity of five multi-century historical U.K. series of real output compiled by the Bank of England (Thomas and Dimsdale, 2017). Three series span 1270 to 2016 and two series span 1700 to 2016. These datasets represent the longest span of historical real output data available and, thus, provide the environment for which unit-root tests are most powerful. A key feature of the Omay et al. (2018) test is its simulataneous allowance for two types of nonlinearity: time-dependent (structural breaks) nonlinearity and state-dependent (asymmetric adjustment) nonlinearity. The key finding of the test, contrary to what other more popular nonlinear unit-root tests suggest, provides strong evidence that the main structure of the five series is stationary with a sharp trend break and an asymmetric nonlinear adjustment. This finding is highly significant from the perspective of current macroeconomic debate because it refutes, for the historical U.K. series at least, the most stylized fact that real output follows a non-stationary process.
    Keywords: Unit Root, Structural Break, Smooth Transition, Fourier Approximation, State-Dependent Nonlinearity
    JEL: C12 C22
    Date: 2019–03
  7. By: Paolo Guarda; Alban Moura
    Abstract: We use unobserved components time series models to extract real and financial cycles for Luxembourg over the period 1980Q1-2018Q2. We find that financial cycles are longer and have larger amplitude compared to standard business cycles. Furthermore, financial cycles are highly correlated with cycles in GDP. We compare our results to other approaches to measure financial cycles and show how unobserved components models can serve to evaluate uncertainty and to monitor cyclical developments in real time. Overall, our estimates indicate that in mid 2018 both real and financial cycles in Luxembourg were close to zero, with financial conditions near their long-run trend.
    Keywords: Financial cycles, unobserved component time series models, Luxembourg.
    JEL: C22 C32 E30 E50 G01
    Date: 2019–03
  8. By: Asafo, Shuffield Seyram
    Abstract: Using quarterly data from 2006q3 to 2017q4, this paper employed sign restrictions with rejection method in a Vector Autoregression to estimate the pass-through of exchange rate dynamics to domestic prices in Ghana. The priors of the model belongs to the flat Normal inverted-Wishart family. Markov Chain Monte Carlo (MCMC) is used to collect 1000 draws from the posterior distribution of the SVAR parameters that satisfy the sign restrictions. The model specification included some idiosyncratic features of the Ghanaian economy such as the dependence on primary export commodities for foreign exchange revenue and the dependence on foreign aid. Impulse response functions was used to analyze exchange rate pass-through whilst variance decomposition was used to explain the most dominant source of inflation in the study sample. The impulse response showed a fairly large but not unitary pass-through of exchange rate dynamics to domestic prices. The implication herein is that exchange rate depreciation led to upsurge in prices in Ghana albeit, the impact is incomplete. Results from the variance decomposition indicated a monetary expansion was most dominant in explaining inflationary pressures in Ghana. For inflation to be lowered, policy directives should be geared towards exchange rate stability as well as ensuring a stable interest rate environment.
    Keywords: Exchange Rate Pass-through, Monetary policy, Inflation, Structural Vector Autoregressive, Bayesian Analysis
    JEL: C1 C11 C13 C32 E30 E37 E5 E52 F3
    Date: 2019–03–12
  9. By: Novy Ann M. Etac; Roel F. Ceballos
    Abstract: This study was conducted to find an appropriate statistical model to forecast the volatilities of PSEi using the model Generalized Autoregressive Conditional Heteroskedasticity (GARCH). Using the R software, the log returns of PSEi is modeled using various ARIMA models and with the presence of heteroskedasticity, the log returns was modeled using GARCH. Based on the analysis, GARCH models are the most appropriate to use for the log returns of PSEi. Among the selected GARCH models, GARCH (1,2) has the lowest AIC value and also has the highest LL value implying that GARCH (1,2) is the best model for the log returns of PSEi.
    Date: 2019–02
  10. By: Olena Kostylenko; Helena Sofia Rodrigues; Delfim F. M. Torres
    Abstract: This work is devoted to modelling and identification of the dynamics of the inter-sectoral balance of a macroeconomic system. An approach to the problem of specification and identification of a weakly formalized dynamical system is developed. A matching procedure for parameters of a linear stationary Cauchy problem with a decomposition of its upshot trend and a periodic component, is proposed. Moreover, an approach for detection of significant harmonic waves, which are inherent to real macroeconomic dynamical systems, is developed.
    Date: 2019–03
  11. By: Potjagailo, Galina; Wolters, Maik H.
    Abstract: We analyze cyclical co-movement in credit, house prices, equity prices, and long-term interest rates across 17 advanced economies. Using a time-varying multi-level dynamic factor model and more than 130 years of data, we analyze the dynamics of co-movement at different levels of aggregation and compare recent developments to earlier episodes such as the early era of financial globalization from 1880 to 1913 and the Great Depression. We find that joint global dynamics across various financial quantities and prices as well as variable-specific global co-movements are important to explain fluctuations in the data. From a historical perspective, global co-movement in financial variables is not a new phenomenon, but its importance has increased for some variables since the 1980s. For equity prices, global cycles play currently a historically unprecedented role, explaining more than half of the fluctuations in the data. Global cycles in credit and housing have become much more pronounced and longer, but their importance in explaining dynamics has only increased for some economies including the US, the UK and Nordic European countries. We also include GDP in the analysis and find an increasing role for a global business cycle.
    Keywords: financial cycles,global co-movement,dynamic factor models,time-varying parameters,macro-finance
    JEL: C32 C38 E44 F44 G15 N10 N20
    Date: 2019

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