Do exchange rate changes improve the trade balance: An asymmetric nonlinear cointegration approach
TL;DR: In this paper, the authors examined the impact of real effective exchange rate on the trade balance of eight countries in the context of several nonlinear techniques, especially the nonlinear auto-regressive distributed lag model (NARDL).
About: This article is published in International Review of Economics & Finance.The article was published on 2017-05-01. It has received 186 citations till now. The article focuses on the topics: Distributed lag & Cointegration.
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TL;DR: In this article, the sensitivity of Bitcoin returns to changes in gold price returns and some other international risk factors such as US stock market returns, interest rates, crude oil prices, the volatility index of the American stock market (VIX) and the Saint Louis financial stress index (STLFSI) was analyzed.
92 citations
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TL;DR: In this article, the authors consider the relationship between the phase angles between the components of the two series and conclude that the changes in the terms of trade should be durable enough to influence the balance of trade.
Abstract: The right conclusion could be that there is a strong connection between the spectral components with specified periods. A possible economic interpretation could identify a strong long-term (in the sense of the specified periods) connection between both series. But that would still not allow us to say anything concerning the size and direction of the time delay between the two series. For such an analysis we would have to, at least, consider the phase angles between the components. Because of the ambivalent meaning of the phase angle (in relation to the direction and number of periods) the final answer could come only from the time domain analysis. However, the fact that only spectral components with longer periods, i.e., smaller frequencies, show coherency suggests something else: In order to influence the balance of trade the changes in the terms of trade should be durable enough. Mathematical formalization of this concept of durability would therefore be helpful. I would propose that further research be done in this respect.
64 citations
TL;DR: In this article, the authors investigated the link between the U.S. housing market and its stock market and found short-run symmetric causality from stock prices to house prices in 10 states and from house prices to stock prices in 20 states.
Abstract: Previous research that investigated the link between the U.S. housing market and its stock market concentrated on the wealth effect by assuming the stock prices to be another determinant of house prices. However, the meltdown of housing market and its subsequent impact on the stock market and the U.S. economy in 2008 pointed to causal relation that could run from housing market to stock market. When we tested this hypothesis using state level data, we found short-run symmetric causality from stock prices to house prices in 10 states and from house prices to stock prices in 20 states. However, when we engaged in asymmetric causality, these numbers increased to 25 and 41 respectively. Surprisingly, in 39 out of 41 states in which house prices caused stock prices, it was decline in house prices that caused stock prices, supporting the events of 2008 as well as asymmetric impacts. We also found cointegration of the two markets in more than half of the states.
52 citations
TL;DR: In this paper, the authors investigated the role of emission-inducing factors such as energy intensity, renewable energy, industrialization, urbanization, and international trade on carbon intensity in the 25 largest emerging economies of the world.
51 citations
TL;DR: In this article , the authors investigated the role of emission-inducing factors such as energy intensity, renewable energy, industrialization, urbanization, and international trade on carbon intensity in the 25 largest emerging economies of the world.
51 citations
References
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TL;DR: The relationship between co-integration and error correction models, first suggested in Granger (1981), is here extended and used to develop estimation procedures, tests, and empirical examples.
Abstract: The relationship between co-integration and error correction models, first suggested in Granger (1981), is here extended and used to develop estimation procedures, tests, and empirical examples. If each element of a vector of time series x first achieves stationarity after differencing, but a linear combination a'x is already stationary, the time series x are said to be co-integrated with co-integrating vector a. There may be several such co-integrating vectors so that a becomes a matrix. Interpreting a'x,= 0 as a long run equilibrium, co-integration implies that deviations from equilibrium are stationary, with finite variance, even though the series themselves are nonstationary and have infinite variance. The paper presents a representation theorem based on Granger (1983), which connects the moving average, autoregressive, and error correction representations for co-integrated systems. A vector autoregression in differenced variables is incompatible with these representations. Estimation of these models is discussed and a simple but asymptotically efficient two-step estimator is proposed. Testing for co-integration combines the problems of unit root tests and tests with parameters unidentified under the null. Seven statistics are formulated and analyzed. The critical values of these statistics are calculated based on a Monte Carlo simulation. Using these critical values, the power properties of the tests are examined and one test procedure is recommended for application. In a series of examples it is found that consumption and income are co-integrated, wages and prices are not, short and long interest rates are, and nominal GNP is co-integrated with M2, but not M1, M3, or aggregate liquid assets.
27,170 citations
TL;DR: In this paper, the authors consider a nonstationary vector autoregressive process which is integrated of order 1, and generated by i.i.d. Gaussian errors, and derive the maximum likelihood estimator of the space of cointegration vectors and the likelihood ratio test of the hypothesis that it has a given number of dimensions.
16,189 citations
TL;DR: In this paper, the authors developed a new approach to the problem of testing the existence of a level relationship between a dependent variable and a set of regressors, when it is not known with certainty whether the underlying regressors are trend- or first-difference stationary.
Abstract: This paper develops a new approach to the problem of testing the existence of a level relationship between a dependent variable and a set of regressors, when it is not known with certainty whether the underlying regressors are trend- or first-difference stationary. The proposed tests are based on standard F- and t-statistics used to test the significance of the lagged levels of the variables in a univariate equilibrium correction mechanism. The asymptotic distributions of these statistics are non-standard under the null hypothesis that there exists no level relationship, irrespective of whether the regressors are I(0) or I(1). Two sets of asymptotic critical values are provided: one when all regressors are purely I(1) and the other if they are all purely I(0). These two sets of critical values provide a band covering all possible classifications of the regressors into purely I(0), purely I(1) or mutually cointegrated. Accordingly, various bounds testing procedures are proposed. It is shown that the proposed tests are consistent, and their asymptotic distribution under the null and suitably defined local alternatives are derived. The empirical relevance of the bounds procedures is demonstrated by a re-examination of the earnings equation included in the UK Treasury macroeconometric model. Copyright © 2001 John Wiley & Sons, Ltd.
13,898 citations
TL;DR: In this paper, the estimation and testing of long-run relations in economic modeling are addressed, starting with a vector autoregressive (VAR) model, the hypothesis of cointegration is formulated as a hypothesis of reduced rank of the long run impact matrix.
Abstract: The estimation and testing of long-run relations in economic modeling are addressed. Starting with a vector autoregressive (VAR) model, the hypothesis of cointegration is formulated as the hypothesis of reduced rank of the long-run impact matrix. This is given in a simple parametric form that allows the application of the method of maximum likelihood and likelihood ratio tests. In this way, one can derive estimates and test statistics for the hypothesis of a given number of cointegration vectors, as well as estimates and tests for linear hypotheses about the cointegration vectors and their weights. The asymptotic inferences concerning the number of cointegrating vectors involve nonstandard distributions. Inference concerning linear restrictions on the cointegration vectors and their weights can be performed using the usual chi squared methods. In the case of linear restrictions on beta, a Wald test procedure is suggested. The proposed methods are illustrated by money demand data from the Danish and Finnish economies.
12,449 citations
01 Jan 2005
10,230 citations