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Recently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample than random walk forecasts or Bayesian vector autoregression (VAR) forecasts. Del Negro and Schorfheide (2013) in particular suggest that the DSGE model forecast should become the benchmark for forecasting horse-races. We compare the real-time forecasting accuracy of the Smets and Wouters (2007) DSGE model with that of several reduced-form time series models. We first demonstrate that none of the forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically simple AR models are most accurate at short horizons and DSGE models are most accurate at long horizons when forecasting output growth, while for inflation forecasts the results are reversed. Moreover, the relative accuracy of all models tends to evolve over time. Third, we show that there is no support to the common practice of using large-scale Bayesian VAR models as the forecast benchmark when evaluating DSGE models. Indeed, low-dimensional unrestricted AR and VAR forecasts may forecast more accurately.

The authors propose novel tests for the detection of Markov switching deviations from forecast rationality. Existing forecast rationality tests either focus on constant deviations from forecast rationality over the full sample or are constructed to detect smooth deviations based on non-parametric techniques. In contrast, the proposed tests are parametric and have an advantage in detecting abrupt departures from unbiasedness and efficiency, which the authors demonstrate with Monte Carlo simulations. Using the proposed tests, the authors investigate whether Blue Chip Financial Forecasts (BCFF) for the Federal Funds Rate (FFR) are unbiased. The tests find evidence of a state-dependent bias: forecasters tend to systematically overpredict interest rates during periods of monetary easing, while the forecasts are unbiased otherwise. The authors show that a similar state-dependent bias is also present in market-based forecasts of interest rates, but not in the forecasts of real GDP growth and GDP deflator-based inflation. The results emphasize the special role played by monetary policy in shaping interest rate expectations above and beyond macroeconomic fundamentals.

Two stylized facts emerge from cash flow literature. One explores the link between free cash flow (FCF) to firm value (Jensen, 1986) and establishes that FCF increases firm value. The other posits FCF may be value decreasing as firms tend to over invest when there is high level of FCF (Richardson, 2006). Two camps have opposing views yet together they establish that FCF is value relevant. If FCF or cash flow, in general, is value relevant then managers will be motivated to present forecasts to investors. The paper aims to discuss these issues.

The authors hand collect data from each firm’s press releases and earnings announcements and perform an event study around this date to see how firm forecast and disclosure policies affect firm value.

The analysis demonstrates that disclosures and forecasts do have significantly positive relation with tech firms suggesting that firms in the technology industries are more forthcoming with cash flow disclosures and forecasts in their earnings announcements. The authors further show that these disclosures and forecasts negatively affect the firm value of tech firms.

This paper contributes to the literature that there is empirical evidence that cash flow disclosures and forecasts matter to the value of the firm. Further, it posits that unlike understanding the existing views as opposing each other, may be the authors will be better served if they view both of them as right depending on the optimality of forecasts. The future efforts will be directed toward exploring the optimality of cash flow disclosures.

The goal of making buy-in decisions is to purchase materials at the right time with the required quantity and a minimum material cost (MC). To help achieve this goal, the purpose of this paper is to find a way of optimizing the buy-in decision with the consideration of flexible starting date of non-critical activities which makes daily demand adjustable.

First, a specific algorithm is developed to calculate a series of demand combinations modeling daily material demand for all the possible start dates. Second, future material prices are predicted by applying artificial neural network. Third, the demand combinations and predicted prices are used to generate an optimal buy-in decision.

By comparing MC in situation when non-critical activities always start at the earliest date to that in situations when the starting date is flexible, it is found that making material buy-in decision with the consideration of the flexibility usually helps reduce MC.

In this paper, a material buy-in decision-making method that accounts non-critical activities’ flexible starting date is proposed. A ternary cycle algorithm is developed to calculate demand combinations. The results that making material buy-in decision considering non-critical activities’ flexible starting date can reduce MC in most times indicates that contractors may consider non-critical activities’ flexibility a part of the buy-in decision-making process, so as to achieve an MC decrease and profit increase.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

The author shows that extending the estimation window prior to structural breaks in cointegrated systems can be beneficial for forecasting performance and highlights under which conditions. In doing so, the author generalizes the Pesaran and Timmermann (2005)’s forecast error decomposition and shows that it depends on four terms: (1) a period ahead risk; (2) a bias due to a conditional mean shift; (3) a bias due to a variance mismatch; (4) a gap term valid only conditionally. The author also derives new expressions for the estimators of the adjustment matrix and a constant, which are auxiliary to the decomposition. Finally, the author introduces new simulation-based estimators for the finite sample forecast properties which are based on the derived decomposition. The author’s finding points out that, in some cases, parameter instability can be neglected by extending the window backward and forecasters can be insured against higher forecast risk under this model class as well, generalizing Pesaran and Timmermann (2005)’s result. The author’s result gives renewed importance to break tests, in order to distinguish cases when break-neglection is (not) appropriate.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

This paper aims to present a method for production planning that reduces the bullwhip effect.

The methods are derived using ARIMA models of the supply chain.

The resulting techniques produced an order policy that uses inventory or backlog to absorb the random variation in demand, but has immediate response to variation in the forecast level of demand.

The limitation of the research is that the optimality of the order policy is based on the assumption that the demand can be modeled using ARIMA models. When this is not the case the method can still be used as a heuristic approach to production smoothing.

This paper offers a practical and easy to implement approach to production planning.

The paper introduces a generalized order policy and derives optimal production smoothing parameters for that order policy.