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on Forecasting |
| By: | Nobuyuki Hanaki (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis - CNRS - Centre National de la Recherche Scientifique); Eizo Akiyama (Faculty of Engineering, Information and Systems, University of Tsukuba - University of Tsukuba); Ryuichiro Ishikawa (Faculty of Engineering, Information and Systems, University of Tsukuba - University of Tsukuba) |
| Abstract: | We investigate (a) whether eliciting future price forecasts influences market outcomes , and (b) whether differences in the way subjects are incentivized to submit " accurate " price forecasts influence the market outcomes as well as the forecasts submitted by subjects in an experimental asset market. We consider three treatments: one without forecast elicitation (NF) and two with forecast elicitations. In one of the latter treatments, subjects are paid based on both their performance of forecasting and trading (Bonus), while in the other, they are paid based only on one of the two that is chosen randomly at the end of the experiment (Unique). While we found no statistical differences in terms of mispricing, trading volumes, and trading behavior between NF and Unique treatments, there were some statistically significant differences between NF and Bonus treatments. Thus, if the aim is to avoid influencing the behavior of subjects and the market outcomes by eliciting price forecasts compared to NF treatment, then the Unique treatment seems to be better than the Bonus treatment. |
| Keywords: | Price forecast elicitation, Experimental asset markets |
| Date: | 2016–01 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:halshs-01263661&r=for |
| By: | Kang, Wensheng (Kent State University); Ratti, Ronald A. (Western Sydney University); Vespignani, Joaquin L. (University of Tasmania) |
| Abstract: | The value of the US dollar is of major importance to the world economy. Global liquidity has grown sharply in recent years with growing importance of China’s money supply to global liquidity. We develop out-of-sample forecasts of the US dollar exchange rate value using US and non-US global data on inflation, output, interest rates, and liquidity on the US, China and non-US/non-China liquidity. Monetary model forecasts significantly outperform a random walk forecast in terms of MSFE at horizons over 12 to 30 months ahead. A monetary model with sticky prices performs best. Rolling sample analysis indicates changes over time in the influence of variables in forecasting the US dollar. China’s liquidity has a distinct, significant and changing influence on the US dollar exchange rate. Post global financial crisis, increases in the growth rate in China’s M2 forecast a significantly higher value for the US dollar 12 months and 18 months ahead and significantly lower values for the US dollar 24 and 30 months. |
| JEL: | E41 E51 F31 F41 |
| Date: | 2016–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:feddgw:264&r=for |
| By: | David Hendry |
| Abstract: | Abstract: Macroeconomic time-series data are aggregated, inaccurate, non-stationary, collinear and rarely match theoretical concepts. Macroeconomic theories are incomplete, incorrect and changeable: location shifts invalidate the law of iterated expectations and ‘rational expectations’ are then systematically biased. Empirical macro-econometric models are non-constant and mis-specified in numerous ways, so economic policy often has unexpected effects, and macroeconomic forecasts go awry. In place of using just one of the four main methods of deciding between alternative models, theory, empirical evidence, policy relevance and forecasting, we propose nesting ‘theory-driven’ and ‘datadriven’ approaches, where theory-models’ parameter estimates are unaffected by selection despite searching over rival candidate variables, longer lags, functional forms, and breaks. |
| Keywords: | Model Selection, Theory Retention, Location Shifts, Indicator Saturation, Autometrics. |
| JEL: | C51 C22 |
| Date: | 2016–01–27 |
| URL: | http://d.repec.org/n?u=RePEc:oxf:wpaper:778&r=for |
| By: | Jean-Charles Bricongne (LEO - Laboratoire d'économie d'Orleans - UO - Université d'Orléans - CNRS - Centre National de la Recherche Scientifique, Banque de France - Banque de France - Banque de France) |
| Abstract: | This article analyses the predictive power of household money holdings with regard to prices or current aggregates (consumption and disposable incomes) over the short term (i.e. over one quarter), as compared with that of other explanatory variables, namely unemployment and total monetary aggregates. Regardless of the approach used, in the short term, household holdings exhibit a comparative advantage over unemployment and total monetary aggregates. The gain in terms of RMSE compared to a simple autoregressive equation is often at least 10%. This is consistent with the quantity theory of money, which holds that there should be a fairly direct link between money and consumption with a limited lag. In the longer run (12 quarters), unemployment exhibits better forecasting properties than household money holdings, which is consistent with the findings of Stock & Watson (1999). |
| Keywords: | Quantity theory of money, household money holdings, inflation, forecasting |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:halshs-01252397&r=for |
| By: | Emre Kahraman; Gazanfer \"Unal |
| Abstract: | The assessment of co-movement among metals is crucial to better understand the behaviors of the metal prices and the interactions with others that affect the changes in prices. In this study, both Wavelet Analysis and VARMA (Vector Autoregressive Moving Average) models are utilized. First, Multiple Wavelet Coherence (MWC), where Wavelet Analysis is needed, is utilized to determine dynamic correlation time interval and scales. VARMA is then used for forecasting which results in reduced errors. The daily prices of steel, aluminium, copper and zinc between 10.05.2010 and 29.05.2014 are analyzed via wavelet analysis to highlight the interactions. Results uncover interesting dynamics between mentioned metals in the time-frequency space. VARMA (1,1) model forecasting is carried out considering the daily prices between 14.11.2011 and 16.11.2012 where the interactions are quite high and prediction errors are found quite limited with respect to ARMA(1.1). It is shown that dynamic co-movement detection via four variables wavelet coherency analysis in the determination of VARMA time interval enables to improve forecasting power of ARMA by decreasing forecasting errors. |
| Date: | 2016–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1602.01960&r=for |
| By: | Emmanuel Frenod (LMBA_UBS - LMBA - Laboratoire de Mathématiques de Bretagne Atlantique - UBS - Université de Bretagne Sud - UBO - Université de Bretagne Occidentale - CNRS - Centre National de la Recherche Scientifique); Tarik Chakkour (LMBA_UBS - LMBA - Laboratoire de Mathématiques de Bretagne Atlantique - UBS - Université de Bretagne Sud - UBO - Université de Bretagne Occidentale - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | In this paper, we construct a continuous-in-time model which is designed to be used for the finances of public institutions. This model is based on using measures over time interval to describe loan scheme, reimbursement scheme and interest payment scheme; and, on using mathematical operators to describe links existing between those quantities. The consistency of the model with respect to the real world is illustrated using examples and its mathematical consistency is checked. Then the model is used on simplified examples in order to show its capability to be used to forecast consequences of a decision or to set out a financial strategy. |
| Keywords: | Continuous-in-time modelling,Financial mathematics, Financial Strategy |
| Date: | 2016–02–01 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-01249324&r=for |
| By: | Savas Papadopoulos (Bank of Greece); Pantelis Stavroulias (Democritus University of Thrace); Thomas Sager (University of Texas) |
| Abstract: | Reliable forecasts of an economic crisis well in advance of its onset could permit effective preventative measures to mitigate its consequences. Using the EU15 crisis of 2008 as a template, we develop methodology that can accurately predict the crisis several quarters in advance in each country. The data for our predictions are standard, publicly available macroeconomic and market variables that are preprocessed by moving averages and filtering. The prediction models then utilize the filtered data to distinguish pre-crisis from normal quarters through standard statistical classification methodology plus a proposed new combined method, enhanced by an innovative threshold selection and goodness-of-fit measure. Empirical results are very satisfactory: Country-stratified 14-fold cross validation achieves 92.1% correct classification and 85.7% for both true positive rate and positive predictive value for the EU15 crisis of 2008. Results will be of use to policy makers, investors, and researchers who are interested in estimating the probability of a crisis as much as one and a half years in advance in order to deploy prudential policies. |
| Keywords: | Banking crisis; financial stability; macroprudential policy; classification methods; goodness-of-fit measures |
| JEL: | C53 E58 G28 |
| Date: | 2016–01 |
| URL: | http://d.repec.org/n?u=RePEc:bog:wpaper:202&r=for |