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Johansen’s Cointegration Test (JCT) performs remarkably well in finding stable bivariate cointegration relationships. Nonetheless, the JCT is not necessarily designed to detect such relationships in presence of non-linear patterns such as structural breaks or cycles that fall in the low frequency portion of the spectrum. Seasonal adjustment procedures might not detect such non-linear patterns, and thus, we expose the difficulty in identifying cointegrating relations under the traditional use of JCT. Within several Monte Carlo experiments, we show that wavelets can empower more the JCT framework than the traditional seasonal adjustment methodologies, allowing for identification of hidden cointegrating relationships. Moreover, we confirm these results using seasonally adjusted time series as US consumption and income, gross national product (GNP) and money supply M1 and GNP and M2.

This paper extends the buffered autoregressive model to the buffered vector error-correction model (VECM). Least squares estimation and a reduced-rank estimation are discussed, and the consistency of the estimators on the delay parameter and threshold parameters is derived. We also propose a supWald test for the presence of buffer-type threshold effect. Under the null hypothesis of no threshold, the supWald test statistic converges to a function of Gaussian process. A bootstrap method is proposed to obtain the p -value for the supWald test. We investigate the effectiveness of our methods by simulation studies. We apply our model to study the monthly Federal bond rates of United States. We find the evidences of buffering regimes and the asymmetric error-correction effect.

We report the results of applying several long-memory models to the historical monthly U.S. inflation rate series and analyze their out-of-sample forecasting performance over different horizons. We find that the time-varying approach to estimating inflation persistence outperforms the models that assume a constant long-memory process. In addition, we examine the link between inflation persistence and exchange rate regimes. Our results support the hypothesis that floating exchange rates associate with increased inflation persistence. This finding, however, is less pronounced during the era of the Great Moderation and the Federal Reserve System’s commitment to inflation targeting.

We propose a new volatility process in which parameters vary over time according to an artificial neural network (ANN). We prove the process’s stationarity as well as the global identification of the parameters. Since ANNs require economic series as input variables, we develop a shrinkage approach to select which explanatory variables are relevant to forecast volatility. Empirically, the proposed model favorably compares with other flexible processes in terms of in-sample fit on six financial returns. It also delivers accurate short-term volatility predictions in terms of root mean squared errors and the predictive likelihood criterion. For long-term forecasts, it can be competitive with the Markov-switching generalized autoregressive conditional heteroskedastic (MS-GARCH) model if appropriate exogenous variables are used. Since our new type of time-varying parameter (TVP) process is based on a universal approximator, the approach can readily revisit and potentially improve many standard TVP applications.

We study the effect of factor substitutability in the neoclassical growth model with variable elasticity of substitution. We consider two otherwise identical economies differing uniquely in their initial factor substitutability with Variable-Elasticity-of-Substitution (VES), Sobelow or Sigmoidal technologies. If the initial capital per capita is below its steady-state value, the economy with the higher initial elasticity of substitution will feature a higher steady-state income and capital per capita irrespective of whether the production technology is VES, Sobelow or Sigmoidal. Numerical results are provided to compare the effect of a higher elasticity of substitution in the Constant-Elasticity-of-Substitution (CES) model versus the models with variable-elasticity-of-substitution technology.

We build a small open economy RBC model with financial frictions to analyse spending and tax based fiscal consolidations in emerging market economies (EMEs). We show that if government spending is a substitute to private consumption in household utility, a spending based fiscal consolidation has an expansionary effect on output. In contrast, tax based consolidations are always contractionary irrespective of the strength of substitutability between government and private consumption. Our findings support the results in the World Economic Outlook (2010), USA: International Monetary Fund, that tax based consolidation measures are more costly (in terms of GDP losses) than spending based consolidations. We calibrate the model to India and calculate the fiscal multipliers associated with spending and tax based fiscal consolidations. Our paper identifies new mechanisms that underlie the dynamics of fiscal reforms and their implications for successful fiscal consolidations.

We propose a model selection criterion to detect purely causal from purely noncausal models in the framework of quantile autoregressions (QAR). We also present asymptotics for the i.i.d. case with regularly varying distributed innovations in QAR. This new modelling perspective is appealing for investigating the presence of bubbles in economic and financial time series, and is an alternative to approximate maximum likelihood methods. We illustrate our analysis using hyperinflation episodes of Latin American countries.