Why is it so difficult to outperform the random walk in exchange rate forecasting?

A simulation exercise is used to demonstrate the difficulty to outperform the random walk in exchange rate forecasting if forecasting accuracy is judged by the Root Mean Square Error (RMSE) or similar criteria that depend on the magnitude of the forecasting error. It is shown that, as the exchange rate volatility rises, the RMSE of the model rises faster than that of the random walk. While the literature considers this finding to be a puzzle that casts a big shadow of doubt on the soundness of international monetary economics, the results show that failure to outperform the random walk, in both in-sample and out-of-sample forecasting, should be the rule rather than the exception. However, the results do not imply that the random walk is unbeatable, because it can be easily outperformed if forecasting accuracy is judged according to criteria such as direction accuracy and profitability.     

A reappraisal of the Meese–Rogoff puzzle

Several explanations have been put forward for the Meese–Rogoff puzzle that exchange rate models cannot outperform the random walk in out-of-sample forecasting. We suggest that a simple explanation for the puzzle is the use of the root mean square error (RMSE) to measure forecasting accuracy, presenting a rationale as to why it is difficult to beat the random walk in terms of the RMSE. By using exactly the same exchange rates, time periods and estimation methods as those of Meese and Rogoff, we find that their results cannot be overturned even if the models are estimated with time-varying coefficients. However, we also find that the random walk can be outperformed by the same models if forecasting accuracy is measured in terms of the ability to predict direction, in terms of a measure that combines magnitude and direction and in terms of profitability.     

Demystifying the Meese–Rogoff puzzle: structural breaks or measures of forecasting accuracy?

Structural breaks have been suggested by several economists as a possible explanation for the Meese–Rogoff puzzle, in the sense that an exchange rate model can outperform the random walk in terms of the out-of-sample forecasting error if the period under investigation is free of structural breaks. The results indicate that structural breaks cannot explain the inability of the flexible price monetary model to outperform the random walk. The only plausible explanation for the Meese–Rogoff puzzle is that forecasting accuracy is traditionally assessed by magnitude-only measures. When forecasting accuracy is assessed by alternative measures that do not rely exclusively on the magnitude of error, the monetary model can outperform the random walk regardless of the presence or otherwise of structural breaks.     

Forward-premium puzzle: is it time to abandon the usual regression?

The forward premium puzzle is usually evidenced by the rejection of the null hypothesis in the uncovered interest parity (UIP) regression. Because this parity need only hold in a risk-neutral world, a risk adjustment term is missing from the equation if speculation in foreign exchange markets is risky. We deal with this issue following the literature which assumes that discounted returns on foreign government bonds are log-normal, so we can linearize the Euler pricing equations (in level) and obtain a modified UIP system for which the risk adjustment term is obtained by applying to the pricing kernel-based relations a generalized autoregressive conditional heteroscedasticity-in-mean model. However, here we innovate by adopting a methodology which differs from all these related works. We construct and use a stochastic discount factor that does not depend on a specific model, by residing in the space of returns which we extract from the data by simply imposing the orthogonality restrictions represented by the Euler equations. So, we devise a purely statistical pricing kernel that performs well in in-sample level equations. Somewhat disappointingly, the risk premium inclusion in the conventional regression changes neither the significance nor the magnitude of the forecasting power of the forward premium for most currencies we study. The contrasting performance of the tests in level and in logs suggests that linearization may be to blame.     

Exchange rate variability and trade: why is it so difficult to find any empirical relationship?

This paper discusses why previous literature has found little evidence of any effect of exchange rate variability on international trade. Methodological and statistical issues are discussed. In particular, comparisons are made of estimations based on different specifications or using different data sets and changes in the results depending on the method used are shown.     

Cointegration tests under multiple regime shifts: An application to the stock price–dividend relationship

We examine the properties of several residual-based cointegration tests when long-run parameters are subject to multiple shifts driven by an unobservable Markov process. Unlike earlier study, which considered one-off deterministic breaks, our approach has the advantage of allowing for an unspecified number of stochastic breaks. We illustrate this issue by exploring the possibility of Markov switching cointegration in the stock price-dividend relationship and showing that this case is empirically relevant. Our subsequent Monte Carlo analysis reveals that standard cointegration tests are generally reliable, their performance often being robust for a number of plausible regime shift parameterizations.     

Can learning explain boom-bust cycles in asset prices? An application to the US housing boom

This paper argues that boom-bust behavior in asset prices can be explained by a model in which boundedly rational agents learn the process for prices. The novel feature of the model is that learning operates in both the demand for assets and the supply of credit. Interactions between agents on either side of the market create complementarities in their respective beliefs, yielding strong internal propagation. The model is applied to US housing markets. Quantitative exercises explain the recent boom-bust in US house prices from observed fundamentals whilst replicating key moments of housing market variables at business cycle frequencies.     

Why is the teen birth rate in the United States so high and why does it matter?

Teens in the United States are far more likely to give birth than in any other industrialized country in the world. U.S. teens are two and a half times as likely to give birth as compared to teens in Canada, around four times as likely as teens in Germany or Norway, and almost 10 times as likely as teens in Switzerland. Among more developed countries, Russia has the next highest teen birth rate after the United States, but an American teenage girl is still around 25 percent more likely to give birth than her counterpart in Russia. Moreover, these statistics incorporate the almost 40 percent fall in the teen birth rate that the United States has experienced over the past two decades. Differences across U.S. states are quite dramatic as well. A teenage girl in Mississippi is four times more likely to give birth than a teenage girl in New Hampshire--and 15 times more likely to give birth as a teen compared to a teenage girl in Switzerland. This paper has two overarching goals: understanding why the teen birth rate is so high in the United States and understanding why it matters. Thus, we begin by examining multiple sources of data to put current rates of teen childbearing into the perspective of cross-country comparisons and recent historical context. We examine teen birth rates alongside pregnancy, abortion, and "shotgun" marriage rates as well as the antecedent behaviors of sexual activity and contraceptive use. We seek insights as to why the rate of teen childbearing is so unusually high in the United States as a whole, and in some U.S. states in particular. We argue that explanations that economists have tended to study are unable to account for any sizable share of the variation in teen childbearing rates across place. We describe some recent empirical work demonstrating that variation in income inequality across U.S. states and developed countries can explain a sizable share of the geographic variation in teen childbearing. To the extent that income inequality is associated with a lack of economic opportunity and heightened social marginalization for those at the bottom of the distribution, this empirical finding is potentially consistent with the ideas that other social scientists have been promoting for decades but which have been largely untested with large data sets and standard econometric methods. Our reading of the totality of evidence leads us to conclude that being on a low economic trajectory in life leads many teenage girls to have children while they are young and unmarried and that poor outcomes seen later in life (relative to teens who do not have children) are simply the continuation of the original low economic trajectory. That is, teen childbearing is explained by the low economic trajectory but is not an additional cause of later difficulties in life. Surprisingly, teen birth itself does not appear to have much direct economic consequence. Moreover, no silver bullet such as expanding access to contraception or abstinence education will solve this particular social problem. Our view is that teen childbearing is so high in the United States because of underlying social and economic problems. It reflects a decision among a set of girls to "drop-out" of the economic mainstream; they choose non-marital motherhood at a young age instead of investing in their own economic progress because they feel they have little chance of advancement. This thesis suggests that to address teen childbearing in America will require addressing some difficult social problems: in particular, the perceived and actual lack of economic opportunity among those at the bottom of the economic ladder.     

Why was china trapped in an agrarian society? An economic geographical approach to the needham puzzle

This paper argues that before the world started to globalize, the differences in the geographical endowments that different population faced were the most important constraints to their long-term production and consumption. The paper uses this central hypothesis to explain the sharp contrast between the flourishing Song and the stagnant Ming and Qing. During the Song dynasty, despite the fact that China lost a significant amount of arable land to invading nomads as its population peaked, China witnessed a higher urbanization level, more prosperous commerce and international trade, and an explosion of technical inventions and institutional innovations. However, after having significantly improved its man-to-land ratio in the period after the Song China only found itself induced deeper into the agrarian trap, resulting in reduced urbanization, withering foreign trade, a declining division of labor, and stagnant in technology.     

Why is Germany’s energy transition so expensive? Quantifying the costs of wind-energy decentralisation

This paper discusses the efficiency of promotion schemes for renewable energy sources using the example of onshore wind energy in Germany. We analyse whether the scheme incentivised a cost-minimal capacity build-up by developing a model to derive two cost-minimal benchmark scenarios, which are then compared to the historical capacity build-up between 1995 and 2015. The costs of the two cost-minimising benchmark scenarios are significantly lower than those of the historical build-up. The benchmark cost reduction largely stems from greater efficiency—fewer turbines are being constructed overall but they are being placed at better wind sites so the annual production of wind energy remains unchanged. Hence, aggregated turbine land use is also significantly down in these scenarios. Furthermore, we compare costs for consumers, as protecting consumers from price discrimination of producers is sometimes used to justify higher payments for low-wind sites. However, our results show that the efficiency gain from building at high wind sites outweighs the distributional effect, even from a consumer’s perspective.     

An alternative approach to the construction of multi-regional input–output tables of the Czech Republic: application of the CHARM method

Empirica - The main scope of the paper is to review the construction method of (multi-)regional input–output tables (RIOTs) in the Czech Republic at the NUTS 3 regional level. We compare the...     

Does time difference between countries reduce bilateral trade? An application of the correlated random effects method using panel data

This article implements the correlated random effects (CRE) panel data technique in a gravity framework to analyse the effect of time difference between countries on bilateral trade. One major advantage of the CRE approach over the fixed-effects approach is that it is able to estimate the effect of variables that remain unchanged within panel clusters (e.g. time difference between countries), while these variables get dropped from regressions that use fixed-effects methods. Regression results based on the CRE Poisson pseudo-maximum likelihood estimator indicate statistically significant negative effect of time difference between countries on bilateral trade. An additional hour of time difference between countries is found to reduce bilateral merchandise exports by approximately 8%, even after controlling for the effect of distance in the regressions.     

Why did the terms of payment in international trade change so much?

This article examines factors contributing to the substantial change in the terms of payment, such as letter of credit (LC) and cash terms, in Korean export transactions during the period 1997–2015, using the Autoregressive Distributed Lag bounds test approach. We find that high competition in the world market, the expansion of exports of information technology products and intra-firm trade, and the improvement of information and communication technology (ICT) decrease the share of exports on LC terms in total exports in the long run. However, the increase in exports to developing countries raises the use of LC terms. On the other hand, the increase in intra-firm trade and ICT improvement raise the share of exports settled in cash, and higher trust between trading partners increases the use of cash terms.     

Interrelationships among the Taiwanese,Japanese and Korean TFT-LCD panel industry stock market indexes: An application of the trivariate FIEC–FIGARCH model

The purpose of this study is to analyze the interrelationships among the Taiwanese, Japanese and Korean TFT-LCD panel industry stock market indexes by applying a trivariate FIEC-FIGARCH model. The empirical results confirm that the FIEC-FIGARCH model can be used to capture long memory behavior and allow us to conclude that mean and volatility spillover, and long memory effects are found in these three markets. Furthermore, we found that deviations in the long-run equilibrium for Japanese TFT-LCD panel industry adjust back very slowly in comparison to the other two countries; and that, in terms of conditional covariance, dynamic interrelationships exist among the TFT-LCD panel industry stock market indices of these three countries.     

Can M&A activities act as a predictor of the performance of economic growth or stock prices?

One of the important issues with regard to the relationship between M&As (mergers and acquisitions) and economic growth or stock prices is whether such activities can act as a predictor of these two variables' performance, or whether these variables have resulted in significant impacts on M&A activities. The aim of this paper is to use the method proposed in Kónya (2006) to carry out a causality test among M&A activities, economic growth and stock prices, because the causal relationships that may be uncovered by this would be meaningful for both policymakers and stockholders. This paper uses quarterly data from six OECD countries for the period from April 1980 to March 2010. The bootstrap panel Granger causality test that this work applies also considers cross-sectional dependency and slope heterogeneity simultaneously. The findings of the paper are as follows. There is significant, one-way causality from stock prices to M&A activities, and thus changes in stock prices lead M&A activities. With real GDP as the control variable, for all the countries surveyed, except Australia, stock prices lead M&A activities. As for the impact that economic growth has on M&A activities, we conclude that, when using stock prices as the control variable, there is almost no lead-lag relationship between economic growth and M&A activities, except for in Japan.     

Does the vector error correction model perform better than others in forecasting stock price? An application of residual income valuation theory

This paper employs a multi-equation model approach to consider three statistic problems (heteroskedasticity, endogeneity and persistency), which are sources of bias and inefficiency in the predictive regression models. This paper applied the residual income valuation model (RIM) proposed by Ohlson (1995) to forecast stock prices for Taiwan three sectors. We compare relative forecasting accuracy of vector error correction model (VECM) with the vector autoregressive model (VAR) as well as OLS and RW models used in the prior studies. We conduct out-of-sample forecasting and employ two instruments to assess forecasting performance. Our empirical results suggest that the VECM statistically outperforms other three models in forecasting stock prices. When forecasting horizons extend longer, VECM produces smaller forecasting errors and performs substantially better than VAR, suggesting that the ability of VECM to improve VAR forecast accuracy is stronger with longer horizons. These findings imply that an error correction term (ECT) of the VECM contributes to improving forecast accuracy of stock prices. Our economic significance analyses and robustness tests for different data frequency are in favor of the superiority of VECM estimator.     

Buyer and/or seller power? An application to the Brazilian milk market

Despite its importance, the co-existence of buyer and seller power has been largely neglected in the empirical literature. In this article, we develop a stochastic frontier model to measure channel market power as deviations from a perfectly competitive frontier and decompose it into buyer and seller power. We provide an empirical illustration using milk data from five Brazilian states and find that channel market power ranges between 4% and 12% of the wholesale price, but that 75% of the market power is accounted for by retail buyer power. The methodology proposed can provide a rapid assessment of the degree of market power in other markets and a method for separating out buyer and seller power in the market channel.     

What drives compliance? An application of the theory of planned behaviour to urban water restrictions using structural equation modelling

This article presents analysis of households’ intentions to comply with government constraints on the use of urban water, or so-called water restrictions. The data are drawn from Australian cities and was collected during a severe drought. Structural equation modelling is employed to operationalize constructs taken from the theory of planned behaviour and to answer important policy questions related to compliance. The modelled data support the view that attitudes, social norms and perceived behavioural control (PBC) have a positive and significant influence on intentions to comply. PBC has the strongest influence on intentions, suggesting policy directed at assisting households to meet regulatory criteria might be at least as effective as advertising expenditures aimed at shaping attitudes and norms. Importantly, intentions to comply are also shown to have a positive and significant influence on reported compliance behaviour.