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The wide fluctuations of oil prices from 2003 to 2008 have attracted the interest of academics and policymakers. A popular view is that these fluctuations were caused by speculative bubbles due to the increased financialization of oil futures markets. This hypothesis, however, is difficult to examine since the fundamental price of oil is unobservable and, therefore, econometric evidence in favor of bubbles may actually be due to misspecified market fundamentals. In this paper, we extend two recently proposed methodologies for bubble detection that alleviate this problem by using market expectations of future prices. Both methodologies provide no evidence of speculative bubbles. Oil price shocks may be associated with significant changes in the overall economic performance of countries (Kilian 2008a, Hamilton 2009, Blanchard and Galí 2010, Hamilton 2013, Kilian 2014, Baumeister and Kilian 2016c). As such, they have been subject to detailed scrutiny for more than three decades (for comprehensive reviews, see Hamilton 2013 and Baumeister and Kilian 2016a). Traditionally, research about the key drivers of oil price fluctuations has been dominated by arguments about fundamental supply and demand shocks (Barsky and Kilian 2002, Hamilton 2003, Kilian 2008b, 2009). It is only recently that the role of speculation has been put forward as a serious alternative (see, e.g., Hamilton 2009, Kilian and Murphy 2014, Knittel and Pindyck 2016). One of the reasons for this recent emphasis on speculation is the eruption of index trading and financialization of oil markets from 2004, and an almost simultaneous surge in the price of oil. The focus on speculative pressures was further fueled by press coverage of the testimonies of Michael Masters before the U.S. Senate (Masters 2008, 2010). The possibility that financial speculation plays a central role in the determination of oil prices naturally raises the issue of whether bubbles are a feature of the dynamics of oil prices. This view has found seeming support by studies providing evidence of bubbles in oil prices in the 2000s (see, e.g., Phillips and Yu 2011, Lammerding et al. 2013, Tsvetanov, Coakley, and Kellard 2016). Studies based on structural models, on the other hand, provide strong evidence that the surge in the real price of oil was driven primarily by shifts in the demand for oil associated with global business cycle fluctuations and that speculation did not have an important impact on the spot price (for a summary of this literature, see Fattouh, Kilian, and Mahadeva 2013). For example, Kilian and Murphy (2014) develop a structural vector autoregressive model of the global market for crude oil that allows for shocks to the speculative demand for oil. They find that expectations of agents about future demand and supply conditions not already captured by flow demand and flow supply shocks accounted for less than 10% of the long‐run variation in the real price of oil. The conclusion that speculative demand did not play a significant role in the determination of the real price of oil since 2003 is robust to the use of alternative proxies for inventory data and a wide range of values of price elasticities of oil demand (Kilian and Lee 2014). In this paper, we contribute to this debate by employing prices and market expectations to analyze the presence of speculative bubbles in the crude oil market. In particular, we extend two recent methodologies, proposed by Pavlidis, Paya, and Peel (2017), to test for periodically collapsing bubbles. The key idea behind these methodologies is simple: in the presence of an ongoing speculative bubble, future spot and expected prices will diverge because market participants rationally attach a nonzero probability to the bubble bursting when forming expectations. Under general conditions, the deviation of the future spot price from the expected price will be a function of the bubble process (and, as such, explosive) but it will not depend on market fundamentals. Consequently, one can simply test for bubbles, first, by running recursive right‐tailed unit root tests on the difference between future spot and expected prices and, second, by sequentially testing the unbiasedness hypothesis in the oil market. Various unit root tests have been proposed in the literature to test for explosive dynamics in time series (see, e.g., Gürkaynak 2008, Homm and Breitung 2012). In principle, any of these could be used to investigate the existence of bubbles. We choose to work with the Generalized Supremum Augmented Dickey Fuller (GSADF) of Phillips, Shi, and Yu (2015a, 2015b). Simulation evidence in Phillips, Shi, and Yu and Pavlidis, Paya, and Peel (2017) suggests that the GSADF displays good size properties and is superior to its rivals in detecting multiple bubble episodes. Furthermore, this test is accompanied by a date‐stamping strategy that permits the identification of the exact periods of market exuberance. With regard to testing the unbiasedness hypothesis, we adopt the rolling‐window approach of Pavlidis, Paya, and Peel (2017). This approach consists of sequentially estimating predictive regressions and drawing statistical inference using the IVX instrumentation method of Phillips and Magdalinos (2009), Phillips and Lee (2013), and Kostakis, Magdalinos, and Stamatogiannis (2015). The IVX method is particularly attractive in this setting because it allows robust chi‐square inference for a wide range of autoregressive processes, from stationary to mildly explosive. The main advantage of the above methodologies is that, by exploiting the information incorporated in market expectations, they do not require the specification of market fundamentals and, thus, ameliorate the well‐known joint hypothesis problem. Both methodologies require, instead, a proper measure of market expectations.1 Pavlidis, Paya, and Peel (2017) use derivative prices to proxy for expectations in the foreign exchange and stock markets. However, derivative prices are contaminated by a risk premium, which, if large and persistent, may confound the analysis. In this paper, we employ two alternative measures of expectations that do not suffer from this shortcoming. The first measure is obtained as in Baumeister and Kilian (2016b) by adjusting futures prices by an estimate of the risk premium based on the term structure model of Hamilton and Wu (2014). The second is directly obtained from survey data. By applying the GSADF and IVX tests to price and expectation data on the crude oil market from 1990 to 2013, we find that the two expectation measures yield qualitatively similar results. Overall, there is no evidence in favor of speculative bubbles. This is despite the fact that oil prices and expectations individually exhibit periods of explosive dynamics. Thus, our findings are consistent with the literature that supports the view that oil price movements are driven by changes in fundamental factors. The rest of the paper is structured as follows. Section 1 describes the theoretical framework. Section 2 discusses the evidence of risk premia in the oil futures market and the implications for tests based on derivative prices. Section 3 deals with the measurement of market expectations. Section 4 provides a brief outline of the econometric methods employed in the paper. Section 5 presents the empirical application, and the final section concludes.

The probabilistic structure of periodically collapsing bubbles creates a gap between future spot and forward (or futures) asset prices in small samples. By exploiting this fact, we use two econometric methods, namely, the recursive unit root method of Phillips, Shi, and Yu (2015a,b) and the rolling regression method of Fama (1984), for detecting bubbles. Both methods do not rely on a particular model of asset price determination, they are robust to an explosive root in the process for market fundamentals, and are accompanied by a date-stamping strategy. By applying these methods to the German mark-US dollar and British pound-US dollar exchange rates, we provide evidence in favor of speculative bubbles in the foreign exchange market during the interwar German hyperinflation, but not during the recent floating-rate period. A further application to the S&P 500 index supports the existence of speculative bubbles in the US equity market.

A variety of international macroeconomic models predict a relationship between the real exchange rate and consumption. The empirical evidence in favor of such a relationship is limited, the so-calledBackus and Smith puzzle. In this paper, we extend the analysis to allow for nonlinear dynamics and volatility changes across exchange rate regimes. Our findings suggest that long-run relationships in line with standard international business cycle models do exist for many Organization for Economic Co-operation and Development (OECD) countries. Further, Monte Carlo experiments illustrate that the nonlinear models can generate the Backus and Smith and the exchange rate disconnect puzzles. In this paper, we also contribute to the nonlinear real exchange rate literature by establishing a theoretical relationship between volatility and persistence. In accordance with the theoretical results, our empirical findings suggest that the increase in volatility in the post-Bretton Woods era is associated with relatively fast mean reversion of the real rate toward its equilibrium value.

This paper examines the effects of higher-order risk attitudes and statistical moments on the optimal allocation of risky assets within the standard portfolio choice model. We derive the expressions for the optimal proportion of wealth invested in the risky asset to show they are functions of portfolio returns third- and fourth-order moments as well as on the investor’s risk preferences of prudence and temperance. We illustrate the relative importance that the introduction of those higher-order effects have in the decision of expected utility maximizers using data for the US.

We exploit a rather neglected source of data, The Commercial and Financial Chronicle to shed light on the behaviour of daily and weekly exchange rates throughout several interwar hyperinflation episodes, in particular the German hyperinflation. The purpose of our analysis is three-fold: firstly, we investigate the consistency of exchange rate data by comparing the rates available from this source to those provided by a widely used source, Einzig (1937). Secondly, we scrutinize the commentaries offered by The Chronicle to shed light on the impact of news on the behaviour of the US dollar/German reichsmark exchange rate over the interwar German hyperinflation, and to check whether the narrative analysis provided therein is consistent with formal econometric analyses in dating when the probability of monetary reform became significantly different from zero. Thirdly, we examine the relationship between the US dollar/German reichsmark spot rate and German wholesale prices during the high inflation period of 1923 using data at (nearly) weekly frequency. We find evidence of long- run purchasing power parity, consistent with the findings of several extant studies that employ monthly data. However, we also show that complete adjustment to purchasing power parity did not appear to occur with the nearly weekly sampled data.

The monetary economics literature has highlighted four issues that are important in evaluating US monetary policy since the late 1960s: (i) time variation in policy parameters, (ii) asymmetric preferences, (iii) real-time nature of data, and (iv) heteroskedasticity. In this paper, we exploit advances in sequential monte carlo methods to estimate a time-varying nonlinear Taylor rule that addresses these four issues simultaneously. Our findings suggest that US monetary policy has experienced substantial changes in terms of both the response to inflation and to real economic activity, as well as changes in preferences. These changes cannot be captured adequately by a single structural break at the late 1970s, as has been commonly assumed in the literature, and play a non-trivial role in economic performance.

Punters may engage in betting on both a selection in an event to finish first or in one of the number of places, e.g. second, third or fourth. When the amounts staked with bookmakers at fixed odds on the win and place are equal, it is called an each-way bet. Each-way bets are apparently popular with punters but inconsistent with prominent models of wagering which assume gamblers are everywhere risk-seeking. In this note, we derive the conditions for win and place bets to be optimal in these three models of risky choice. The mathematical conditions for the each-way wager to be optimal, as opposed to a win and place wager with different stakes, are complicated and appear likely to occur rarely in practice. However, bettors obviously see the attraction in giving themselves two ways to bet on the one horse or two ways to win and betting each way. We suggest part of the ‘each-way’ betting attraction is that they are quick and easy to compute – a heuristic – to solve an otherwise complex betting strategy.

In this letter we illustrate a previously unrecognised implication of Cumulative Prospect Theory and Rank Dependent Utility. We demonstrate that the representative individual in both models will often engage in wagering on more than one outcome in an event. For instance when playing European roulette the representative agents in CPT or RDU will always optimally wager on more than one outcome. This implication contrasts with expected utility models of wagering based on risk-seeking preferences due to the assumption of a convex segment of the utility function.

We report striking evidence of semi-strong inefficiency in the UK fixed-odds football betting market using a reputable newspaper tipster which offers probabilities of match outcomes rather than simple result indicators. Betting on the Fink Tank probabilities of home wins across 10 bookmakers, when there are positive expected returns, would have generated positive returns in each of the seasons from 2006–07 to 2011–12 for a variety of different betting strategies. These returns could have been enhanced by employing the best odds from a greater number of bookmakers. However, the fact that pure arbitrage bets have existed for years and appear to last for several hours or days suggest they are in practice not exploitable to a magnitude that poses any threat to bookmakers.

The recent financial crisis exposed the inability of traditional theoretical and empirical models to parsimoniously capture the rich dynamics of the economic environment. This has stimulated the interest of both academics and practitioners in the development and application of more sophisticated models. By allowing for the presence of nonlinearities, complex dynamics, multiple equilibria, structural breaks and spurious trends, these latter models resemble more closely the properties of economic and financial time series. In this article, we illustrate the flexibility of a family of econometric models, namely the exponential smooth transition autoregressive (ESTAR), to encompass several of the above characteristics. We then re-assess the power of the ESTAR unit root test developed by Kapetanios, Shin and Snell ((2003)) in the presence of nuisance parameters typically encountered in the literature and compare its performance with that of the augmented Dickey-Fuller and the Enders and Granger ((1998)) tests....

The purpose in this paper is to demonstrate how the non-expected utility models of Markowitz and Kahneman and Tversky can explain why an agent, chooses to bet each way on a horse.We also show that that appeal to moments of return, such as a preference for skewness of return, ceteris paribus, to explain the choice of the each way gamble over the single win gamble is , in general, invalid.

Explanation of the Allais paradox and the preference of many for multiple prize lottery tickets provide a rationale for why a model of agent’s choice under uncertainty should embody the assumption that they distort probabilities. However the degree of probability distortion required to explain gambling on long shots in Cumulative Prospect Theory appears problematic since it implies subjective expected rates of return are dramatically higher than objective returns. Here we show that a Markowitz model of expected utility, supplemented by a small degree of probability distortion, has qualitatively similar predictions as Cumulative Prospect Theory for numerous experimental outcomes as well as the indifference curves between expected return and objective probabilities for a given stake gamble. In addition we show how a small degree of probability distortion can lead to a preference for a multiple prize lottery which has a rather different prize structure and associated probabilities than the optimally chosen one prize lottery even though the utility gain is small.

Given that the expected return and variance of return of two gambles are equal the hypothesis that the gamble with the greater positive skewness of return will be chosen by an expected utility maximiser is appealing. However the hypothesis is not, in general, correct. Brockett and Garven (1998) and Brocket and Kahane (1992) demonstrate this both theoretically and by constructing counter examples.A particularly revealing example is the following one constructed by Brockett and Kahane. Gamble A has the two outcomes 2.45 and 7.49 with probabilities 0.5141 and 0.4859 respectively. Gamble B has the three outcomes 0, 4.947 and 10 with probabilities 0.12096, 0.750085 and 0.128955 respectively. Even though gamble A exhibits lower expected return, a higher variance and lower positive skewness than gamble B it is preferred to gamble B by an expected utility maximiser on the basis of any standard utility function such as power, log or exponential. Consequently in this example of theirs the expected utility maximiser exhibits an aversion to higher expected return and higher skewness and a preference for higher variance. As noted by Brockett and Kahane these results cannot be dismissed as decision makers “trading” variance for mean or skewness or having a strange idiosyncratic utility function.

We demonstrate that a utility maximizing individual with an everywhere concave utility function may optimally wager on two or more outcomes in an event even though the expected returns to a unit stake are negative on all outcomes except one.

Recent literature contains numerous examples where researchers continue to assert that a positive third derivative of the utility function of a risk-averse agent implies a preference for skewness, ceteris paribus. The purpose in this letter is to provide some examples pertinent to occupational choice, the gambling literature and experimental work where the assertion is incorrect and an example relevant to experimental work where it is correct.

Despite displaying a statistically significant optimism bias, analysts' earnings forecasts are an important input to investors’ valuation models. Understanding the possible reasons for any bias is important if information is to be extracted from earnings forecasts and used optimally by investors. Extant research into the shape of analysts' loss functions explains optimism bias as resulting from analysts minimizing the mean absolute forecast error under symmetric, linear loss functions. When the distribution of earnings outcomes is skewed, optimalforecasts can appear biased. In contrast, research into analysts' economic incentives suggests that positive and negative earnings forecast errors made by analysts are not penalized or rewarded symmetrically, suggesting that asymmetric loss functions are an appropriate characterization. To reconcile these findings, we exploit results from economic theory relating to the Linex loss function to discriminate between the symmetric linear loss and the asymmetric loss explanations of analyst forecast bias. Under asymmetric loss functions optimal forecasts will appear biased even if earnings outcomes are symmetric. Our empirical results support the asymmetric loss function explanation. Further analysis also reveals that forecast bias varies systematically across firm characteristics that capture systematic variation in the earnings forecast error distribution.

This paper deals with the nonlinear modeling and forecasting of the dollar–sterling and franc–sterling real exchange rates using long spans of data. Our contribution is threefold. First, we provide significant evidence of smooth transition dynamics in the series by employing a battery of recently developed in-sample statistical tests. Second, we investigate the small-sample properties of several evaluation measures for comparing recursive forecasts when one of the competing models is nonlinear. Finally, we run a forecasting race for the post-Bretton Woods era between the nonlinear real exchange rate model, the random walk, and the linear autoregressive model. The nonlinear model outperforms all rival models in the dollar–sterling case but cannot beat the linear autoregressive in the franc–sterling.

Motivated by a central banker with an inflation target, we show that the optimal forecast bias under non-quadratic loss functions and non-normal forecast errors can decrease or initially increase and then decrease with the forecast horizon. We initially proof that, if the variable to forecast can be described by a generalized Rayleigh distribution, its conditional mean does in general not constitute the optimal prediction under a symmetric target zone loss function. Subsequently, we approximate the target zone loss function to show the potential for variation in optimal bias over the forecast horizon.

Based on the cost-of-carry model of future prices, a number of studies have estimated nonlinear autoregressive models for the basis at different frequencies (see, e.g., Dwyer GP, Locke, P, & Yu, W, 1996; Monoyios M and Sarno L, 2002; Taylor N, van Dijk D, Franses PH, & Lucas A, 2000). The structure of the models and the speed of adjustment to shocks reported are radically different. In this paper we examine the implications of systematic sampling. The results obtained show that regular sampling of the process seems important in attempting to explain the apparently contradictory results reported on the speed of adjustment to shocks in the cost-of-carry model.

Long memory processes can occur as a consequence of aggregation over heterogeneous agents. We examine the UK lottery and, by estimating the level of fractional differencing, find evidence of the long memory property in lottery sales, a result that has broader implications on the estimation of demand models for lotteries.

The purpose in this article is to demonstrate that buying more than one ticket in a lottery is readily explicable in models of utility that permit gambling at actuarially unfair odds. However, contrary to popular view, we show this choice cannot be explained in terms of a variance–skew trade-off.

International audience; Skewness of return has been suggested as a reason why agents might choose to gamble, ceteris paribus, in Cumulative Prospect Theory (CPT). We investigate the relationship between moments of return in two models where agents choices over uncertain outcomes are determined as in CPT. We illustrate via examples that in CPT theory, as with expected utility theory, propositions that agents have a preference for skewness may be invalid.

International audience; Standard parametric specifications of Cumulative Prospect theory (CPT) can explain why agents bet on longshots at actuarially unfair odds. However the standard specification of CPT cannot explain why people might bet on more favored outcomes, where by construction the greatest volume of money is bet. This paper outlines a parametric specification than can consistently explain gambling over all outcomes. In particular we assume that the value function is bounded from above and below and that the degree of loss aversion experienced by the agent is smaller for small-stake gambles (as a proportion of wealth) than usually assumed in CPT. There are a number of new implications of this specification. Boundedness of the value function in CPT implies that the indifference curve between expected-return and win-probability for a given stake will typically exhibit both an asymptote (implying rejection of an infinite gain bet) and a minimum, as the shape of the value function dominates the probability weighting function. Also the high probability section of the indifference curve will exhibit a maximum.

Previous research has reported that analysts’ forecasts of company profits are both optimistically biased and inefficient. However, many prior studies have applied ordinary least-squares regression to data where heteroskedasticity and non-normality are common problems, potentially resulting in misleading inferences. Furthermore, most prior studies deflate earnings and forecasts in an attempt to correct for non-constant error variances, often changing the specification of the underlying regression equation. We describe and employ the wild bootstrap—a technique that is robust both to heteroskedasticity and non-normality—to assess the reliability of prior studies of analysts’ forecasts. Based on a large sample of 23,283 firm years covering the period 1981–2002, our main results confirm the findings of prior research. Our results also suggest that deflation may not be a successful method of correcting for heteroskedasticity, providing a strong rationale for using the wild bootstrap in future work in this, and other areas of accounting and finance research.

The use of the power utility function is problematic in expected utility theory. We show that, this is also the case in cumulative prospect theory, where the power function violates the assumption of loss-aversion at small stake levels, so that an optimal model of gambling is precluded. In the case of rank-dependent expected utility it has the counterfactual implication that agents will gamble all of their wealth at actuarially unfair odds.

Nonlinear models of deviations from PPP have recently provided an important, theoretically well motivated, contribution to the PPP puzzle. Most of these studies use temporally aggregated data to empirically estimate the nonlinear models. As noted by Taylor (2001), if the true DGP is nonlinear, the temporally aggregated data could exhibit misleading properties regarding the adjustment speeds. We examine the effects of different levels of temporal aggregation on\ estimates of ESTAR models of real exchange rates. Our Monte Carlo results show that temporal aggregation does not imply the disappearance of nonlinearity and that adjustment speeds are significantly slower in temporally aggregated data than in the true DGP. Furthermore, the autoregressive structure of some monthly ESTAR estimates found in the literature is suggestive that adjustment speeds are even faster than implied by the monthly estimates

Nonlinear models of deviations from PPP have recently provided an important, theoretically well motivated, contribution to the PPP puzzle. In recent work the equilibrium level has been modelled either as constant or as time varying with very similar statistical fits and very different economic implications. The high persistence of both PPP deviations and the proxy variables for the equilibrium real rate might create a problem of spurious coefficient significance. This paper investigates the possibility of spurious regression within nonlinear models of PPP. Monte Carlo experiments show that standard critical values are not appropriate in such a context. To illustrate we consider the real Dollar-Sterling exchange rate over the period 1871-1994. Due to many exchange rate regime changes over the sample period we employ a Bootstrap methodology that preserves the original structure of the estimated residuals and obtain new critical values of the coefficient estimates. A nonlinear (ESTAR) process with a time varying equilibrium proxied by relative wealth and relative income per capita seems to parsimoniously fit the data. Our results provide further evidence for the nonlinear model with a shifting equilibrium and the implied speed of adjustment is found to be substantially faster than previously reported in the literature.

Recent research has reported the lack of correct size in stationarity test for PPP deviations within a linear framework. However, theoretically well motivated nonlinear models, such as the ESTAR, appear to parsimoniously fit the PPP data and provide an explanation for the PPP ?puzzle?. Employing Monte Carlo experiments we analyze the size and power of the nonlinear tests against a variety of nonstationary hypotheses. We also fit the ESTAR model to data from high inflation economies. Our results provide further support for ESTAR specification.

Slow adjustment of real exchange rate towards equilibrium in linear models has long puzzled researchers, stimulating the adoption of nonlinear models. The exponential smooth transition model has been particularly successful, providing faster adjustment speeds. This paper discusses some of its theoretical limitations, for example that expectations are adaptive. We propose a new nonlinear model conceptually superior to the ESTAR model since it is consistent with rational expectations. One of its advantages is that it can be solved and estimated by nonlinear least squares. Using monthly post-1973 real exchange rate data, we show that the model implies even faster speeds of adjustment.

By employing the wild bootstrap we calculate the ‘correct’ size of the relevant test statistics for the test of the null hypothesis of efficiency in betting markets. Our findings are that the overall conclusion of the rejection of the null hypothesis is still valid.

It appears that stock market index returns exhibit (weak) autocorrelation and there is evidence that volatility in equity markets is inversely related to first order autocorrelation. A prominent theory that can address both the aforementioned stylized facts is the feedback trading theory that relates autocorrelation to individual investor trading patterns. We provide a unified framework to simultaneously investigate market wide feedback trading strategies effects and the presence of volatility-correlation relationships. Our empirical application uses market portfolios from six international markets and employs conditional non-linear in mean and in variance models. Parametric specifications for conditional dependence beyond the mean and variance are also modelled following Hansen's (1994) (Hansen, B.E., 1994. Int. Econ. Rev. 35 (a), 705–730) autoregressive conditional density estimation. For the sample period under investigation (post 1990), our results support the presence of feedback traders and an inverse volatility–correlation relationship in three out of six markets while only two of the series under investigation do not provide evidence of autocorrelation. Our empirical work supports volatility induced sign switches in correlation. We document positive and significant risk premium parameters in three markets while we find that volatility at the market level is enhanced by past movements of the daily range (daily high–low price difference) series.

The traditional explanation for the usual favourite–longshot bias in gambling is that gamblers are risk-lovers. Conditions are derived under which the bias occurs and it is shown to be consistent with a utility function that has elasticity greater than one in a certain range. With a utility function that displays risk-aversion as well as risk-loving behaviour over its domain, it is demonstrated that the expected return–win probability frontier is not monotonic as has been hitherto tacitly assumed. This provides a consistent explanation for both the usual favourite–longshot bias and also for the few examples where a reverse bias has been observed. Pooled data supports the thesis that the frontier is not completely monotonic but does indeed have a turning point.

This article estimates a reduced form Taylor rule for the Pre-Volcker and Volcker–Greenspan periods. A novelty is that it follows a suggestion of Walsh and includes changes in the output gap as an explanatory variable. Either this variable or an interaction term between inflation and changes in the output gap are highly significant in both periods. The response to inflation and the interaction term are higher in the Volcker–Greenspan period of office.

A new panel unit root by Chang (Journal of Econometrics, 110, 261–92, 2002) is employed on a set of financial ratios with a view to improving the power of unit root tests when applied to a relatively small number of observations (in the present case 38 annual observations). The test is innovative in that it allows for cross-sectional dependencies and the asymptotic distribution of the test is standard. Although standard Dickey–Fuller tests suggest that individual financial ratio series are nonstationary, panel unit root tests strongly reject the null hypothesis of a joint unit root in the ratios. Taken together the evidence from the proposed new analysis implies strong persistence in the ratios but that their characterization as I(1) processes may be misleading. These findings have important implications for accounting and finance researchers who employ financial ratios as explanatory variables.

We examine whether inflation targeting should be regarded as optimal. Targeting inflation implies (undesirably) that price level variance tends to infinity: we produce some evidence from both a representative agent model and a long-used forecasting model that, once an endogenous indexation response is allowed for, price level targeting imposes no extra costs of macro variability, indeed gives significant gains.

Two different approaches intend to resolve the ‘puzzling’ slow convergence to purchasing power parity (PPP) reported in the literature [see Rogoff (1996), Journal of Economic Literature, Vol. 34.] On the one hand, there are models that consider a non-linear adjustment of real exchange rate to PPP induced by transaction costs. Such costs imply the presence of a certain transaction band where adjustment is too costly to be undertaken. On the other hand, there are models that relax the ‘classical’ PPP assumption of constant equilibrium real exchange rates. A prominent theory put together by Balassa (1964, Journal of Political Economy, Vol. 72) and Samuelson (1964 Review of Economics and Statistics, Vol. 46), the BS effect, suggests that a non-constant real exchange rate equilibrium is induced by different productivity growth rates between countries. This paper reconciles those two approaches by considering an exponential smooth transition-in-deviation non-linear adjustment mechanism towards non-constant equilibrium real exchange rates within the EMS (European Monetary System) and effective rates. The equilibrium is proxied, in a theoretically appealing manner, using deterministic trends and the relative price of non-tradables to proxy for BS effects. The empirical results provide further support for the hypothesis that real exchange rates are well described by symmetric, nonlinear processes. Furthermore, the half-life of shocks in such models is found to be dramatically shorter than that obtained in linear models.

Rogoff suggested in 1996 that the dollar–yen real exchange rate represented a ‘canonical’ case of a trend in the equilibrium real exchange rate. The implied speed of adjustment of the dollar–yen real exchange rate is found to be substantially faster, with half-life shocks of less than 2 years, from estimates of a non-linear model which incorporates a deterministic trend proxying the equilibrium level. We also examine the power of unit root tests against smooth transition non-linear models which incorporate a deterministic trend and the robustness of such non-linear estimations using Monte Carlo and bootstrap simulations.

This paper analyses the asymmetries in the response of petrol prices to oil price shocks. We show that previous work, based on the determination of asymmetric responses, can be improved upon by allowing for asymmetries in short term dynamics. The paper shows that a significant determinant of the response of petrol prices to oil price changes, is the extent to which petrol price can be seen to have departed from its long run equilibrium level.

This paper considers optimal monetary policy in the context of the central bank adopting an asymmetric objective function. Our results show that under asymmetric preferences, many of the extant results on the time consistency problem need no longer hold.

This paper re-evaluates the time series properties of financial ratios. It presents new empirical analysis which explicitly allows for the possibility that financial ratios can be characterized as non-linear mean-reverting processes. Financial ratios are widely employed as explanatory variables in accounting and finance research with applications ranging from the determinants of auditors’ compensation to explaining firms’ investment decisions. An implicit assumption in this empirical work is that the ratios are stationary so that the postulated models can be estimated by classical regression methods. However, recent empirical work on the time series properties of corporate financial ratios has reported that the level of the majority of ratios is described by non-stationary, I(1), integrated processes and that the ratio differences are parsimoniously described by random walks. We hypothesize that financial ratios may follow a random walk near their target level, but that the more distant a ratio is from target, the more likely the firm is to take remedial action to bring it back towards target. This behavior will result in a significant size distortion of the conventional stationarity tests and lead to frequent non-rejection of the null hypothesis of non-stationarity, a finding which undermines the use of these ratios as reliable conditioning variables for the explanation of firms’ decisions.

In two recent contributions Lothian and Taylor, and Cuddington and Liang, produced empirical evidence that annual data for the dollar-sterling real exchange rate spanning two centuries exhibited a non-linear deterministic trend. This trend could be proxying Harrod-Balassa-Samuelson effects. Lothian and Taylor showed that a linear stationary autoregressive mode, which embodied a cubic trend, implied much faster mean reversion of the real exchange rate to shocks than a model that excluded the trend. This article shows that both non-linearity and a deterministic trend can be allowed for in a theoretically appealing manner and that the fitted models provide a parsimonious explanation of both the dollar-sterling and franc-sterling real exchange rates over the two centuries of data. Generalized impulse response function analysis of the models demonstrates that the speed of adjustment to shocks can be even faster when trends are considered.

The purpose in this article is to investigate the implications of a non-linear supply curve for the standard discretionary inflation outcome obtained when the central bank has quadratic preferences. Some implications for the optimal output target and degree of conservativeness of the central banker are derived.

In this article we test for the structural stability of an output growth forecasting equation that includes the term structure of interest rates as a regressor, for the USA and Canada. We are able to confirm that there is parameter instability as the coefficient associated with the term structure is reduced in size as the policy maker becomes more averse to inflation. Our empirical results are in accordance with the theoretical predictions.

This paper examines the feasibility of using the term structure of nominal interest rates in empirical predictive relationships with future real activity growth serving as the dependent variable. In particular, we will focus on the strength and stability of the spread–output relationship. We employ smooth transition nonlinear models that can accommodate (a) regime switching type nonlinear behaviour and (b) time-varying parameters. We verify that the link exhibits strong threshold effects with respect to near past spread values implying that the relation is sufficiently strong in economic terms if past spread values did not exceed a positive threshold value. Furthermore, we are able to explicitly model time-variation in the preceding effects reaching the conclusion that the importance of the spread as an output predictor has been significantly diminished if not eradicated during recent years. The timing of the change in the information content of the spread appears to be related to a turn in certain monetary policy practices, in particular, the turn towards stronger inflation targeting practices.

Several theoretical models of money demand imply nonlinear functional forms for the aggregate demand for money, characterized by smooth adjustment toward long-run equilibrium. In this paper, we propose a nonlinear equilibrium correction model of U.S. money demand that is shown to be stable over the sample period from 1869 to 1997.