nep-ets New Economics Papers
on Econometric Time Series
Issue of 2019‒12‒02
nine papers chosen by
Jaqueson K. Galimberti
KOF Swiss Economic Institute

  1. Structural stability of infinite-order regression By Abhimanyu Gupta; Myung Hwan Seo
  2. Instrument-free inference under confined regressor endogeneity; derivations and applications By Kiviet, Jan
  3. Modeling, Forecasting, and Nowcasting U.S. CO2 Emissions Using Many Macroeconomic Predictors By Mikkel Bennedsen; Eric Hillebrand; Siem Jan Koopman
  4. Predictive properties of forecast combination, ensemble methods, and Bayesian predictive synthesis By Kosaku Takanashi; Kenichiro McAlinn
  5. Mean Reversion in Asia-Pacific Stock Prices: New Evidence from Quantile Unit Root Tests By Gilbert V. Nartea; Harold Glenn A. Valera; Maria Luisa G. Valera
  6. A Moving Average Heterogeneous Autoregressive Model for Forecasting the Realized Volatility of the US Stock Market: Evidence from Over a Century of Data By Afees A. Salisu; Rangan Gupta; Ahamuefula E. Ogbonna
  7. An ARIMA analysis of the Indian Rupee/USD exchange rate in India By NYONI, THABANI
  8. Forecasting inflation in the euro area: countries matter! By Angela Capolongo; Claudia Pacella
  9. Lithuanian house price index: modelling and forecasting By Laurynas Narusevicius; Tomas Ramanauskas; Laura Gudauskaitė; Tomas Reichenbachas

  1. By: Abhimanyu Gupta; Myung Hwan Seo
    Abstract: We develop a class of tests for the structural stability of infinite-order models such as the infinite-order autoregressive model and the nonparametric sieve regression. When the number $ p $ of restrictions diverges, the traditional tests based on the suprema of Wald, LM and LR statistics or their exponentially weighted averages diverge as well. We introduce a suitable transformation of these tests and obtain proper weak limits under the condition that $p $ grows to infinity as the sample size $n $ goes to infinity. In general, this limit distribution is different from the sequential limit, which can be obtained by increasing the order of the standardized tied-down Bessel process in Andrews (1993). In particular, our joint asymptotic analysis discovers a nonlinear high order serial correlation, for which we provide a consistent estimator. Our Monte Carlo simulation illustrates the importance of robustifying the structural break test against the nonlinear serial correlation even when $ p $ is moderate. Furthermore, we also establish a weighted power optimality property of our tests under some regularity conditions. We examine finite-sample performance in a Monte Carlo study and illustrate the test with a number of empirical examples.
    Date: 2019–11
  2. By: Kiviet, Jan
    Abstract: A fully-fledged alternative to Two-Stage Least-Squares (TSLS) inference is developed for general linear models with endogenous regressors. This alternative approach does not require the adoption of external instrumental variables. It generalizes earlier results which basically assumed all variables in the model to be normally distributed and their observational units to be stochastically independent. Now the chosen underlying framework corresponds completely to that of most empirical cross-section or time-series studies using TSLS. This enables revealing empirically relevant replication studies, also because the new technique allows testing the earlier untestable exclusion restrictions adopted when applying TSLS. For three illustrative case studies a new perspective on their empirical findings results. The new technique is computationally not very demanding. It involves scanning least-squares-based results over all compatible values of the nuisance parameters established by the correlations between regressors and disturbances.
    Keywords: endogeneity robust inference, instrument validity tests, replication studies, sensitivity analysis, two-stage least-squares.
    JEL: C12 C13 C21 C22 C26
    Date: 2019–11–06
  3. By: Mikkel Bennedsen (Aarhus University and CREATES); Eric Hillebrand (Aarhus University and CREATES); Siem Jan Koopman (Vrije Universiteit Amsterdam and CREATES)
    Abstract: We propose a structural augmented dynamic factor model for U.S. CO2 emissions. Variable selection techniques applied to a large set of annual macroeconomic time series indicate that CO2 emissions are best explained by industrial production indices covering manufacturing and residential utilities sectors. We employ a dynamic factor structure to explain, forecast, and nowcast the industrial production indices and thus, by way of the structural equation, emissions. We show that our model has good in-sample properties and out-of-sample performance in comparison with univariate and multivariate competitor models. Based on data through September 2019, our model nowcasts a reduction of about 2.6% in U.S. CO2 emissions in 2019 compared to 2018 as the result of a reduction in industrial production in residential utilities.
    Keywords: CO2 emissions, macroeconomic variables, dynamic factor model, variable selection, forecasting, nowcasting
    JEL: C01 C13 C32 C51 C52 C53 C55 C82 Q43 Q47
    Date: 2019–11–27
  4. By: Kosaku Takanashi; Kenichiro McAlinn
    Abstract: This paper studies the theoretical predictive properties of classes of forecast combination methods. The study is motivated by the recently developed Bayesian framework for synthesizing predictive densities: Bayesian predictive synthesis. A novel strategy based on continuous time stochastic processes is proposed and developed, where the combined predictive error processes are expressed as stochastic differential equations, evaluated using Ito's lemma. We show that a subclass of synthesis functions under Bayesian predictive synthesis, which we categorize as non-linear synthesis, entails an extra term that "corrects" the bias from misspecification and dependence in the predictive error process, effectively improving forecasts. Theoretical properties are examined and shown that this subclass improves the expected squared forecast error over any and all linear combination, averaging, and ensemble of forecasts, under mild conditions. We discuss the conditions for which this subclass outperforms others, and its implications for developing forecast combination methods. A finite sample simulation study is presented to illustrate our results.
    Date: 2019–11
  5. By: Gilbert V. Nartea (University of Canterbury); Harold Glenn A. Valera; Maria Luisa G. Valera
    Abstract: We investigate the stationarity of real stock prices among 12 Asia-Pacific countries over the period 1991–2018. The methodology employed is driven by the need to address three key concerns: (i) the identification of which positive or negative shocks are linked to stationarity; (ii) the identification of different speeds of adjustment towards long-run equilibrium; and (iii) the identification of mean reversion and potential asymmetric speed of adjustment before and after the 2008-2009 global financial crisis. To meet these concerns, we examine the time series properties of the data within a quantile unit root testing framework. Our results generally indicate that real stock prices are stationary at the upper quantiles only. There is also evidence of a varied speed of adjustment process across the quantiles where stationarity is present. Further analysis indicates that real stock prices became much more reverting and with a faster speed of adjustment after the global financial crisis, except for Japan and New Zealand.
    Keywords: Stock prices, Mean reversion, Quantile unit root regression
    JEL: C1 C5 G1
    Date: 2019–11–01
  6. By: Afees A. Salisu (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam and Faculty of Business Administration, Ton Duc Thang University, Ho Chi Minh City, Vietnam); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Ahamuefula E. Ogbonna (Centre for Econometric & Allied Research, University of Ibadan and Department of Statistics, University of Ibadan)
    Abstract: This study forecasts the monthly realized volatility of the US stock market covering the period of February, 1885 to September, 2019 using a recently developed novel approach – a moving average heterogeneous autoregressive (MAT-HAR) model, which treats threshold as a moving average generated time varying parameter rather than as a fixed or unknown parameter. The significance of asymmetric information in realized volatility of stock market forecasting is also considered by examining the case of good and bad realized volatility. The Clark and West (2007) forecast evaluation approach is employed to evaluate the forecast performance of the proposed predictive model vis-à-vis the conventional HAR and threshold HAR (T-HAR) models. We find evidence in favour of the MAT-HAR model relative to the HAR and T-HAR models. Also observed is the significant role of asymmetry in modeling the realized volatility as good realized volatility and bad realized volatility yield dissimilar predictability results. Our results are not sensitive to the choice of sample periods and realized volatility measures.
    Keywords: Realized volatility, US stock market, Forecast evaluation, HAR models
    JEL: C22 C53 G12
    Date: 2019–11
    Abstract: This study uses annual time series data on the Indian Rupee / USD exchange rate from 1960 to 2017, to model and forecast exchange rates using the Box-Jenkins ARIMA technique. Diagnostic tests indicate that R is an I (1) variable. Based on Theil’s U, the study presents the ARIMA (0, 1, 6) model, the diagnostic tests further show that this model is quite stable and hence acceptable for forecasting the Indian Rupee / USD exchange rates. The selected optimal model the ARIMA (0, 1, 6) model shows that the Indian Rupee / USD exchange rate will appreciate over the period 2018 – 2022, after which it will depreciate slightly until 2027. The main policy prescription emanating from this study is that the Reserve Bank of India (RBI) should devalue the Rupee, firstly to restore the much needed exchange rate stability, secondly to encourage local manufacturing and thirdly to promote foreign capital inflows.
    Keywords: ARIMA; exchange rate; forecasting; India; Indian Rupee/USD
    JEL: C53 E37 E47 F37 O24
    Date: 2019–11–03
  8. By: Angela Capolongo (ECARES, Université Libre de Bruxelles); Claudia Pacella (Bank of Italy)
    Abstract: We construct a Bayesian vector autoregressive model with three layers of information: the key drivers of inflation, cross-country dynamic interactions, and country-specific variables. The model provides good forecasting accuracy with respect to the popular benchmarks used in the literature. We perform a step-by-step analysis to shed light on which layer of information is more crucial for accurately forecasting euro area inflation. Our empirical analysis reveals the importance of including the key drivers of inflation and taking into account the multi-country dimension of the euro area. The results show that the complete model performs better overall in forecasting inflation excluding energy and unprocessed food, while a model based only on aggregate euro area variables works better for headline inflation.
    Keywords: inflation, forecasting, euro area, Bayesian estimation
    JEL: C32 C53 E31 E37
    Date: 2019–06
  9. By: Laurynas Narusevicius (Bank of Lithuania); Tomas Ramanauskas; Laura Gudauskaitė (Bank of Lithuania); Tomas Reichenbachas (Bank of Lithuania)
    Abstract: Timely monitoring of the housing market developments in Lithuania is one of the key elements in the analysis framework of the macroprudential authority aiming to contribute to financial stability in Lithuania. In this paper, we addressed three important questions related to Lithuanian house prices, namely, whether house prices are under- or over valuated, which explanatory variables have the biggest impact on the growth of house prices and what the future development of the Lithuanian house price index could be. Three separate modelling and forecasting exercises were performed in order to tackle these questions. The first exercise employs the vector error correction modelling (VECM) approach to assess under- or overvaluation of the house prices. We then use an autoregressive distributed lag model (ARDL) to evaluate which explanatory variables have the biggest impact on house price growth. As the last exercise, we develop a suite of models that are used to forecast future development of the house price index. The analysis presented in this paper may be viewed as a further step towards more formalised modelling and forecasting of the Lithuanian house price index.
    Keywords: House price index, fundamental value, time series models, forecasting, forecast combination
    JEL: C22 C32 C53 E37 R30
    Date: 2019–11–19

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