R Scott Hacker

Tests for causality between integrated variables using asymptotic and bootstrap distributions: theory and application

Causality tests in the Grangers sense are increasingly applied in empirical research. Since the unit root revolution in time-series analysis, several modifications of tests for causality have been introduced in the literature. One of the recent developments is the Toda–Yamamoto modified Wald (MWALD) test, which is attractive due to its simple application, its absence of pre-testing distortions, and its basis on a standard asymptotical distribution irrespective of the number of unit roots and the cointegrating properties of the data. This study investigates the size properties of the MWALD test and finds that in small sample sizes this test performs poorly on those properties when using its asymptotical distribution, the chi-square. It is suggested that use be made of a leveraged bootstrap distribution to lower the size distortions. Monte Carlo simulation results show that an MWALD test based on a bootstrap distribution has much smaller size distortions than corresponding cases when the asymptotic distribution is used. These results hold for different sample sizes, integration orders, and error term processes (homoscedastic or ARCH). This new method is applied to the testing of the efficient market hypothesis.

A test for multivariate ARCH effects

This paper extends Engles LM test for ARCH affects to multivariate cases. The size and power properties of this multivariate test for ARCH effects in VAR models are investigated based on asymptotic and bootstrap distributions. Using the asymptotic distribution, deviations of actual size from nominal size do not appear to be very excessive. Nevertheless, there is a tendency for the actual size to overreject the null hypothesis when the nominal size is 1% and underreject the null when the nominal size is 5% or 10%. It is found that using a bootstrap distribution for the multivariate LM test is generally superior in achieving the appropriate size to using the asymptotic distribution when (1) the nominal size is 5%; (2) the sample size is small (40 observations) and/or the VAR system is stable. With a small sample, the power of the test using the bootstrap distribution also appears better at the 5% nominal size.This paper extends Engles LM test for ARCH affects to multivariate cases. The size and power properties of this multivariate test for ARCH effects in VAR models are investigated based on asymptotic and bootstrap distributions. Using the asymptotic distribution, deviations of actual size from nominal size do not appear to be very excessive. Nevertheless, there is a tendency for the actual size to overreject the null hypothesis when the nominal size is 1% and underreject the null when the nominal size is 5% or 10%. It is found that using a bootstrap distribution for the multivariate LM test is generally superior in achieving the appropriate size to using the asymptotic distribution when (1) the nominal size is 5%; (2) the sample size is small (40 observations) and/or the VAR system is stable. With a small sample, the power of the test using the bootstrap distribution also appears better at the 5% nominal size.

Is the J-Curve Effect Observable for Small North European Economies?

The present study tests for the J-curve for five North European countries—Belgium, Denmark, The Netherlands, Norway, and Sweden—using generalized impulse response functions. The results provide empirical support for the J-curve. Each country has an impulse response function generated from a vector error-correction model that suggests that after a depreciation, there will be a dip in the export-import ratio within the first half-year after the depreciation. The long-run export-import ratio appears to be higher than the low point of this early dip in almost all cases. Also, in most cases, the export-import ratio appears in many periods after the depreciation to be converging from below to a higher long-run equilibrium.

Optimal lag-length choice in stable and unstable VAR models under situations of homoscedasticity and ARCH

The performance of different information criteria – namely Akaike, corrected Akaike (AICC), Schwarz–Bayesian (SBC), and Hannan–Quinn – is investigated so as to choose the optimal lag length in stable and unstable vector autoregressive (VAR) models both when autoregressive conditional heteroscedasticity (ARCH) is present and when it is not. The investigation covers both large and small sample sizes. The Monte Carlo simulation results show that SBC has relatively better performance in lag-choice accuracy in many situations. It is also generally the least sensitive to ARCH regardless of stability or instability of the VAR model, especially in large sample sizes. These appealing properties of SBC make it the optimal criterion for choosing lag length in many situations, especially in the case of financial data, which are usually characterized by occasional periods of high volatility. SBC also has the best forecasting abilities in the majority of situations in which we vary sample size, stability, variance structure (ARCH or not), and forecast horizon (one period or five). frequently, AICC also has good lag-choosing and forecasting properties. However, when ARCH is present, the five-period forecast performance of all criteria in all situations worsens.

A bootstrap test for causality with endogenous lag length choice : theory and application in finance

Granger causality tests have become among the most popular empirical applications with time series data. Several new tests have been developed in the literature that can deal with different data generating processes. In all existing theoretical papers it is assumed that the lag length is known a priori. However, in applied research the lag length has to be selected before testing for causality. This paper suggests that in investigating the effectiveness of various Granger causality testing methodologies, including those using bootstrapping, the lag length choice should be endogenized, by which we mean the data-driven preselection of lag length should be taken into account. We provide and accordingly evaluate a Granger-causality bootstrap test which may be used with data that may or may not be integrated, and compare the performance of this test to that for the analogous asymptotic test. The suggested bootstrap test performs well and appears to be also robust to ARCH effects that usually characterize the financial data. This test is applied to testing the causal impact of the US financial market on the market of the United Arab Emirates.

The Relationship between Exchange Rates and Interest Rate Differentials: A Wavelet Approach

This paper uses wavelet analysis to investigate the relationship between the spot exchange rate and the interest rate differential for seven pairs of countries, with a small country, Sweden, included in each of the cases. The key empirical results show that there tends to be a negative relationship between the spot exchange rate (domestic-currency price of foreign currency) and the nominal interest rate differential (approximately the domestic interest rate minus the foreign interest rate) at the shortest time scales, while a positive relationship is shown at the longest time scales. This indicates that among models of exchange rate determination using the asset approach, the sticky-price models are supported in the short-run while in the long-run the flexible-price models appear to better explain the sign of the relationship.

An alternative method to test for contagion with an application to the Asian financial crisis

This paper investigates the size properties of a test for contagion based on an asymptotic t-distribution. The simulations show that this asymptotic test does not have correct size properties. An alternative test method based on case-resampling bootstrapping is introduced to improve on the correctness of inference. The simulations show that this new test has much better size properties. It also has quite high power properties and it is robust to ARCH effects. The method is applied to testing for contagion from Thailand to Indonesia during the Asian financial crisis.

Can the LR test be helpful in choosing the optimal lag order in the VAR model when information criteria suggest different lag orders

The objective of this simulation study is to investigate whether the likelihood ratio (LR) test can pick the optimal lag order in the vector autoregressive model when the most applied information criteria (i.e. vector Schwarz–Bayesian, SBC and vector Hannan–Quinn, HQC) suggest two different lag orders. This lag-choosing procedure has been suggested by Hatemi-J (1999). The results based on the Monte Carlo simulations show that combining the LR test with SBC and HQC causes a substantial increase in the success rate of choosing the optimal lag order compared to cases when only SBC or HQC are used. This appears to be the case irrespective of homoscedasticity or conditional heteroscedasticity properties of the error-term in small sample sizes. This improvement in choosing the right lag order also tends to improve the forecasting capability of the underlying model.

Mobility and Regional Economic Downturns

In this paper I show how higher unemployment in a region may reduce thepopulations residential mobility within that region. A period of higher unemployment creates more uncertainty among individuals about future income and place of employment so those with significant moving costs are more likely to consider delaying a move. Periods of relatively higher unemployment may also be characterized by fewer new hirings and fewer job quits, both of which tend to dampen mobility. A multinomial logit analysis using Panel Study of Income Dynamics data is used to examine the effect of state unemployment rates on the decision to move.

Sweden and the Baltic Sea Region: Transaction Costs and Trade Intensities

In her research, Karin Peschel has demonstrated that the friction of interaction between regions and between countries has a strong influence on location and trade (e.g., Peschel 1982, 1984). A particular feature of her findings is that the pertinent friction is affected by many other factors than just geographic distance. In this paper, we adopt this focus and show that geographic transaction costs can be identified indirectly for pair-wise trade links between countries in the Baltic Sea region, and that such costs strongly affect the distribution of trade flows.