How do oil price forecast errors impact inflation forecast errors? An empirical analysis from US,French and UK inflation forecasts

This paper proposes an empirical investigation of the impact of oil price forecast errors on inflation forecast errors for three different sets of recent forecast data: the median of SPF inflation forecasts for the United States and the Central Bank inflation forecasts for France and the United Kingdom. Mainly two salient points emerge from our results. First, there is a significant contribution of oil price forecast errors to the explanation of inflation forecast errors, whatever the country or the period considered. Second, the pass-through of oil price forecast errors to inflation forecast errors is typically multiplied by around 2 when the oil price volatility is large.     

Long memory,economic policy uncertainty and forecasting US inflation: a Bayesian VARFIMA approach

We compare inflation forecasts of a vector autoregressive fractionally integrated moving average (VARFIMA) model against standard forecasting models. U.S. inflation forecasts improve when controlling for persistence and economic policy uncertainty (EPU). Importantly, the VARFIMA model, comprising of inflation and EPU, outperforms commonly used inflation forecast models.     

Inflation persistence in central and eastern European countries

This article studies inflation persistence with time-varying coefficient autoregressions for 12 central European countries in comparison with the United States and the euro area. We find that inflation persistence tends to be higher in times of high inflation. Since the oil price shocks, inflation persistence has declined both in the United States and the euro area. In most central and eastern European countries, for which our study covers 1993–2012, inflation persistence has also declined, with the main exceptions of the Czech Republic, Slovakia and Slovenia, where persistence seems to be rather stable. Our findings have implications for the conduct of monetary policy and for a possible membership in the euro area. Among the two time-varying coefficient methods we use, our results favour the flexible least squares smoother over the Kalman smoother. We also conclude that the OLS estimate of an autoregression is likely upward biased relative to the time-average of time-varying parameters, when the parameters change.     

Asymmetric and Time‐Varying Causality between Inflation and Inflation Uncertainty in G‐7 Countries

We use Granger causality tests within a conditional Gaussian Markov switching vector autoregressive (MS‐VAR) model using monthly data for G‐7 countries covering the period 1959:12–2008:10 to examine the relationship between inflation and inflation‐uncertainty. The MS‐VAR model allows us to model parameter time‐variation so as to reflect changes in Granger causality, assuming that these changes are stochastic and governed by an unobservable Markov chain. Inflation uncertainty is measured as the conditional variance generated by a Fractionally Integrated Smooth Transition Autoregressive Moving Average‐Asymmetric Power ARCH (FISTARMA‐APARCH) model. The distinguishing feature of our approach from the previous studies is the determination of the sign of the Granger causality relationship between inflation and its uncertainty over time. First, using a rolling VAR model, we show that the relationship between inflation and inflation uncertainty is time varying with frequent breaks. Second, using the MS‐VAR model, we obtain strong evidence in favour of the Holland's ‘stabilizing Fed hypothesis’ for Canada, France, Germany, Japan, United Kingdom, and the United States. We also find evidence in favour of the Friedman hypothesis for Canada and the United States.     

Do we need a global VAR model to forecast inflation and output in South Africa?

This study determines whether the global vector autoregressive (GVAR) approach provides better forecasts of key South African variables than a vector error correction model (VECM) and a Bayesian vector autoregressive (BVAR) model augmented with foreign variables. The article considers both a small GVAR model and a large GVAR model in determining the most appropriate model for forecasting South African variables. We compare the recursive out-of-sample forecasts for South African GDP and inflation from six types of models: a general 33 country (large) GVAR, a customized small GVAR for South Africa, a VECM for South Africa with weakly exogenous foreign variables, a BVAR model, autoregressive (AR) models and random walk models. The results show that the forecast performance of the large GVAR is generally superior to the performance of the customized small GVAR for South Africa. The forecasts of both the GVAR models tend to be better than the forecasts of the augmented VECM, especially at longer forecast horizons. Importantly, however, on average, the BVAR model performs the best when it comes to forecasting output, while the AR(1) model outperforms all the other models in predicting inflation. We also conduct ex ante forecasts from the BVAR and AR(1) models over 2010:Q1–2013:Q4 to highlight their ability to track turning points in output and inflation, respectively.     

Forecasting first births

This study is concerned with one aspect of the family cycle, namely, the transition from young married to young married with small children. The focus is on developing models to forecast entries into this latter stage for the purpose of marketing research. "Using ordinary least squares, forecasting models were estimated for (1) total number of first births, (2) number of white first births, and (3) number of nonwhite first births." Models are estimated for both the United States and California using data from official sources.     

Democracy and economic growth in Sub-Saharan Africa: a panel data approach

This paper studies the link between democracy and economic development for 28 countries of Sub-Saharan Africa for the period 1980–2005 in a panel data framework. A democracy index constructed from the Freedom House indices. A variety of panel data unit root and cointegration tests are applied. The variables are found to be integrated of order one and cointegrated. The Blundell–Bond system generalized methods-of-moments is employed to conduct a panel error-correction mechanism based causality test within a vector autoregressive structure. Economic growth is found to cause democracy in the short-run, while bidirectionality is uncovered in the long-run. In addition, the long-run coefficients are estimated through the panel fully modified ordinary least squares and dynamic ordinary least squares methods. Democracy has a positive impact on GDP and vice versa. These results lend support to the virtuous cycle hypothesis.     

A Bayesian method of combining judgmental and model-based density forecasts

This paper introduces a formal method of combining expert and model density forecasts when the sample of past forecasts is unavailable. It works directly with the expert forecast density and endogenously delivers weights for forecast combination, relying on probability rules only. The empirical part of the paper illustrates how the framework can be applied in forecasting US inflation by mixing density forecasts from an autoregressive model and the Survey of Professional Forecasters.     

Moments of OLS estimators in an autoregressive moving average model with explanatory variables

In this paper we derive the moments of the ordinary least squares (OLS) estimators in an autoregressive moving average model by a straightforward technique compared to the one used in Carter and Ullah (1979). The model contains exogenous variables and the technique also provides simpler moment expressions and can be used to derive the moments in more general dynamic models.     

Alternate estimation criteria and measures of forecast accuracy

The purpose of this paper is to investigate and illustrate the effect that alternate estimation criteria have on measured forecast accuracy. In most instances forecast evaluation criteria (error measures) differ from the model estimation criterion, the latter most often being the traditional least squares. The results suggest that forecast accuracy may be improved when criteria other than least squares are used for model estimation purposes.     

The J‐Curve: Evidence from Fiji*

This article provides new evidence on both long run and short‐run determinants of trade balance for Fiji and investigates evidence of J‐curve adjustment behaviour in the aftermath of a devaluation. We adopt a partial reduced form model that models the real trade balance directly as a function of the real exchange rate and real domestic and foreign incomes. Cointegration analysis is based on a recently developed autoregressive distributed lag approach—shown to provide robust results in finite samples. The long run elasticities are also estimated using a dynamic ordinary least squares approach and the Fully Modified Ordinary Least Squares (FM‐OLS) approach. Amongst our key results we find that there is a long‐run relationship between trade balance and its determinants. There is evidence of the J‐curve pattern; growth in domestic income affects Fiji’s trade balance adversely while foreign income improves it.     

Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting

To address the nonlinear and non-stationary characteristics of financial time series such as foreign exchange rates, this study proposes a hybrid forecasting model using empirical mode decomposition (EMD) and least squares support vector regression (LSSVR) for foreign exchange rate forecasting. EMD is used to decompose the dynamics of foreign exchange rate into several intrinsic mode function (IMF) components and one residual component. LSSVR is constructed to forecast these IMFs and residual value individually, and then all these forecasted values are aggregated to produce the final forecasted value for foreign exchange rates. Empirical results show that the proposed EMD-LSSVR model outperforms the EMD-ARIMA (autoregressive integrated moving average) as well as the LSSVR and ARIMA models without time series decomposition.     

Improving forecast accuracy of financial vulnerability: PLS factor model approach

We present a factor augmented forecasting model for assessing the financial vulnerability in Korea. Dynamic factor models often extract latent common factors from a large panel of time series data via the method of the principal components (PC). Instead, we employ the partial least squares (PLS) method that estimates target specific common factors, utilizing covariances between predictors and the target variable. Applying PLS to 198 monthly frequency macroeconomic time series variables and the Bank of Korea's Financial Stress Index (KFSTI), our PLS factor augmented forecasting models consistently outperformed the random walk benchmark model in out-of-sample prediction exercises in all forecast horizons we considered. Our models also outperformed the autoregressive benchmark model in short-term forecast horizons. We expect our models would provide useful early warning signs of the emergence of systemic risks in Korea's financial markets.     

Time-varying U.S. inflation dynamics and the New Keynesian Phillips curve

This paper introduces a form of boundedly-rational inflation expectations in the New Keynesian Phillips curve. The representative agent is assumed to behave as an econometrician, employing a time series model for inflation that allows for both permanent and temporary shocks. The near-unity coefficient on expected inflation in the Phillips curve causes the agent's perception of a unit root in inflation to become close to self-fulfilling. In a “consistent expectations equilibrium,” the value of the Kalman gain parameter in the agent's forecast rule is pinned down using the observed autocorrelation of inflation changes. The forecast errors observed by the agent are close to white noise, making it difficult for the agent to detect a misspecification of the forecast rule. I show that this simple model of inflation expectations can generate time-varying persistence and volatility that is broadly similar to that observed in long-run U.S. data. Model-based values for expected inflation track well with movements in survey-based measures of U.S. expected inflation. In numerical simulations, the model can generate pronounced low-frequency swings in the level of inflation that are driven solely by expectational feedback, not by changes in monetary policy.     

The properties of survey-based inflation expectations in Sweden

This article assesses the properties of survey-based inflation expectations in Sweden. The survey in question is conducted by Prospera once every quarter and consists of respondents from businesses and labour-market organisations. The article shows that inflation expectations measured in this survey tend to be biased and inefficient forecasts of future inflation. Moreover, evaluations of forecast accuracy show that these inflation expectations are worse predictors of inflation than those of a professional forecasting institution and also typically outperformed by a simple autoregressive model. Given that the true inflation expectations are captured by the survey, our results indicate that economic agents’ expectations formation process is suboptimal.     

Time-varying persistence in US inflation

The persistence property of inflation is an important issue not only for economists, but especially for central banks, given that the degree of inflation persistence determines the extent to which central banks can control inflation. Further, not only is it the level of inflation persistence that is important in economic analyses, but also the question of whether the persistence varies over time, for instance, across business cycle phases, is equally pertinent, since assuming constant persistence across states of the economy is sure to lead to misguided policy decisions. Against this backdrop, we extend the literature on long-memory models of inflation persistence for the US economy over the monthly period of 1920:1–2014:5, by developing an autoregressive fractionally integrated moving-average-generalized autoregressive conditional heteroskedastic model with a time-varying memory coefficient which varies across expansions and recessions. In sum, we find that inflation persistence does vary across recessions and expansions, with it being significantly higher in the former than in the latter. As an aside, we also show that persistence of inflation volatility is higher during expansions than in recessions. Understandably, our results have important policy implications.     

Macroeconomic Determinants of Crime: Evidence from India

This paper examines the relationship between crime, inflation, unemployment, and real GDP per capita in India. Based on the national-level data, the Johansen cointegration test confirms the presence of cointegration relationship between the variables. The Toda–Yamamoto Granger causality test suggests that macroeconomic indicators, especially unemployment, can significantly affect crime in India. Based on the state-level data, the ordinary least squares results corroborate the effect of inflation on crime even after controlling for governance. However, they fail to verify the relationship between crime, unemployment, and real GDP per capita.

     

Asset pricing with adaptive learning

We study the extent to which self-referential adaptive learning can explain stylized asset pricing facts in a general equilibrium framework. In particular, we analyze the effects of recursive least squares and constant gain algorithms in a production economy and a Lucas type endowment economy. We find that (a) recursive least squares learning has almost no effects on asset price behavior, since the algorithm converges relatively fast to rational expectations, (b) constant gain learning may contribute towards explaining the stock price and return volatility as well as the predictability of excess returns in the endowment economy but (c) in the production economy the effects of constant gain learning are mitigated by the persistence induced by capital accumulation. We conclude that in the context of these two commonly used models, standard linear self-referential learning does not resolve the asset pricing puzzles observed in the data.     

Testing similarities of short-run inflation dynamics among EU-25 countries after the Euro

In this paper, we introduce two new definitions of pair-wise and multi-wise similarity between short-run dynamics of inflation rates in terms of equality of forecast functions and show that in the context of invertible ARIMA processes the autoregressive metric introduced by Piccolo (1990) is a useful measure to evaluate such similarity. Then, we study the similarity of short-run inflation dynamics across European Union (EU)-25 Member States during the Euro period. Consistent with studies on inflation differentials and inflation persistence, our findings suggest that after seven years from the launch of the Euro the degree of similarity of short-run inflation dynamics across Euro area countries is still weak. By contrast, we find that EU countries not adopting the common currency, whether old EU or new accession Members, display a higher degree of inflation dynamics similarity both among each other and with Euro area countries.     

THE ECONOMIC VALUE OF PROTECTING INVENTORIED ROADLESS AREAS: A SPATIAL HEDONIC PRICE STUDY IN NEW MEXICO

The objective of this paper is to examine the off‐site benefits, as capitalized into housing values, of protecting 1.6 million acres of Inventoried Roadless Areas (IRAs) in the state of New Mexico, United States. In light of petitions filed by various U.S. states to maintain the status of IRAs as roadless lands, spatial hedonic price models are estimated and used to calculate the implicit value of IRAs in New Mexico. Findings show that a two‐stage least squares (2‐SLS), robust spatial‐lag model is the most appropriate econometric representation of the hedonic price function, and that IRA lands are a significant and positive determinant of house value. After controlling for the presence of Wilderness Areas (WAs) and other characteristics, results indicate that, on average, there is a 5.6% gain in the property value of a house from being located in, or adjacent to, a Census tract with IRAs. In the aggregate, this gain represents 3.5% of the value of all owner‐occupied units in New Mexico ($1.9 billion in capitalized value, or an annualized value in perpetuity of $95 million, assuming a 5% interest rate). (JEL R22, H40, Q51, C21)