What determines households inflation expectations? Theory and evidence from a household survey

The purpose of the present paper is to study how households form inflation expectations using a novel survey dataset of Italian households. We extend the existing ‘inattentiveness’ literature by incorporating explicitly inflation targets and distinguishing between aggregate and disaggregate dynamics based on demographic groups. We also consider both the short- and long-run dynamics as households update their inflation expectations. While we find clear distinctions between the various demographic groups behavior, households tend to absorb professionals forecast. The short-run dynamics also indicate they not only overreact when updating their expectations but also adjust asymmetrically to any perceived momentum change of future inflation.     

Functional food consumption and depression among the elderly — What can we learn from a longitudinal survey?

Depressive syndromes among the elderly have been recognized as important public health issues. This study contributes to this issue by investigating the causal effect of functional food consumption on depression among the elderly using a unique panel dataset of 1480 individuals drawn from a national representative survey of Taiwanese elderly in 1999, 2003 and 2007. In addition to estimating a standard individual fixed effect model, a random trend fixed effect model which allows for both unobserved individual-specific time invariant and variant factors is estimated. The results of the random trend model point to a significant and negative effect of functional food consumption on depression among the elderly. However, an insignificant effect is predicted by the standard fixed effect model. In addition to functional food consumption, living arrangement with children and engagement in the social activities of the elderly also significantly decrease the likelihood of the elderly becoming depressed.     

What can we learn from the distribution of trade patterns?

This paper proposes an empirical framework for analyzing the dynamics of trade specialization, using a symmetric transformation of the standard Balassa (Manch Sch Econ Soc Stud 33(2):99–123, 1965) index and the conditional density estimation methods suggested by Hyndman et al. (J Comput Graph Stat 5(4):315–336, 1996). The framework is implemented using data on the cross-sector export and import specialization of the four initial EU Cohesion countries over the last 40 years. We discuss the importance of studying both the distribution’s external shape and the intra-distribution dynamics and why it is interesting to include imports in the analysis. We find a reduction of the overall degree of export specialization in Portugal, Greece and Spain. Conversely, Ireland has the strongest export specialization and there is evidence of an increase over time. The export intra-distribution dynamics reveal persistence of the specialization status in the four countries, especially for high values of the index. In all countries, the degree of specialization is higher for exports than for imports and intra-distribution dynamics reveal more mobility of import specialization than that of exports.     

What can we learn from revisions to the Greenbook forecasts?

Although there have been many evaluations of the Federal Reserve’s Greenbook forecasts, we analyze them in a different dimension. We examine the revisions of these forecasts in the context of fixed event predictions to determine how new information is incorporated in the forecasting process. This analysis permits us to determine if there was an inefficient use of information in the sense that the forecast revision has predictive power for the forecast error. Research on forecast smoothing suggests that we might find a positive relationship between the forecast error and the forecast revision. Although we do find for some variables and horizons the Fed’s forecast errors are predictable from its forecast revisions, there is no evidence of forecast smoothing. Instead the revisions sometimes have a negative relationship with the forecast error, suggesting in these cases that the Fed may be over-responsive to new information.     

R&D activities in start-up firms: What can we learn from founding resources?

Research and development is critical in promoting innovation and firm development. While many studies have been discussing how firms’ R&D behaviour is affected by the internal factors and external factors, little has been done to link it to firms’ founding conditions. Using resource-based view theory, this study suggests the firms’ R&D activities in their later stages are partly determined by firms’ starting resource bundle. We propose that the financing structure, entrepreneurs’ education background and intellectual property of the new firms will affect their R&D behaviour. The study examines the performance of a random sample of firms established in 2004 using data from the Kauffman Firm Survey. The result shows a mixing impact of firms’ starting resources. The founders’ education and the ownership of intellectual property have a positive and long-lasting impact, while firms’ financial capital matters mostly in the short term. The study also finds subsequent resource development is highly reliant on the initial resource bundle.     

The long-run determinants of inequality: What can we learn from top income data?

This paper studies determinants of income inequality using a newly assembled panel of 16 countries over the entire twentieth century. We focus on three groups of income earners: the rich (P99–100), the upper middle class (P90–99), and the rest of the population (P0–90). The results show that periods of high economic growth disproportionately increases the top percentile income share at the expense of the rest of the top decile. Financial development is also pro-rich and the outbreak of banking crises is associated with reduced income shares of the rich. Trade openness has no clear distributional impact (if anything openness reduces top shares). Government spending, however, is negative for the upper middle class and positive for the nine lowest deciles but does not seem to affect the rich. Finally, tax progressivity reduces top income shares and when accounting for real dynamic effects the impact can be important over time.     

Does extracting inflation from stock returns solve the purchasing power parity puzzle?

In a recent study by Chowdhry et al. (American Economic Review 95: 255–276, 2005), they suggest that the empirical failure of relative purchasing power parity (PPP) may be because the official inflation data are too sticky. Thus, after extracting the unobservable pure price inflation from equity markets, they find strong evidence supporting relative PPP in the short run. As a replication study, this paper first replicates their original findings successfully. We further investigate whether long-run relative PPP holds using the pure price inflation data constructed by Chowdhry et al. (2005). After constructing pure real exchange rate series using their pure price inflation data, we implement both unit root and cointegration tests on the pure real exchange rates. According to the test results, the evidence suggests that relative PPP does not hold in the long run. Thus, it may be too early to suggest a resolution of the PPP puzzle.     

On the economic factors of deforestation: What can we learn from quantile analysis?

The deforestation factors at a global level have been widely studied in the empirical economic literature. However, the high heterogeneity among countries considerably limits the overall significance of the results. Using quantile approach, we show thatsome major deforestation factors are more prevalent in high deforestation countries, giving the insight that those factors have been under-estimated in previous studies.     

What can we learn from a large border effect in developing countries?

Failure of the law of one price (LOP) in the short run is an empirical regularity. Recent research, using disaggregated price data for different cities across an international boundary, has shown that the variation of the prices in equidistant cities located in two different countries is systematically larger than that for the cities within the same country. Nontariff barriers and exchange rate variability are cited as the proximate causes of this systematic difference in consumer price variability. Results using data from developing countries with large nontariff barriers and more volatile exchange rate suggest that those claims are overemphasized.     

What can we learn from the history of gasoline crack spreads?: Long memory,structural breaks and modeling implications

In this paper, we investigate the long-range auto-correlations of crack spreads using a nonparametric method, named detrended moving average (MF-DMA). We find that the auto-correlations display multiscaling behaviors and are dominated by the anti-persistence (mean-reversion) in the long-term. Moreover, the auto-correlations are multifractal, indicating that various small and large fluctuations display different scaling behaviors. Using a technique of rolling windows, we find that some extreme events can drive the degree of anti-persistence and the multifractality (complexity) to rise up. In other words, these events have negative impacts on market efficiency. However, the effects of these events are not alike. We also detect long-range auto-correlations in crack spread volatilities and find a strong persistent behavior and multifractality. Finally, we discuss the modeling implications of the findings on long-range auto-correlated patterns. Our results indicate that ARFIMA-GARCH models can capture the major dynamics of large fluctuations. For small fluctuations, they are misspecified. Interestingly, we find that the strong long-range auto-correlated behaviors do not imply that ARFIMA model which takes long memory into account can outperform random walk model in the sense of out-of-sample prediction. The major reason may be that market complexity exploited in this paper causes the low predictability of ARFIMA model.     

Sticky prices or rational inattention – What can we learn from sectoral price data?

This paper derives stylised facts on sectoral inflation dynamics and confronts these facts with two popular theoretical models of price setting. Based on sectoral price responses to macroeconomic shocks estimated from an approximate factor model, we find that the frequency of price changes explains a relevant share of the cross-sectional variation of the speed and size of responses. Moreover, there is little evidence that the volatility of sectoral inflation due to idiosyncratic shocks dampens the size and speed of the responses to macroeconomic shocks. These findings support a multi-sector model with sticky prices rather than a rational-inattention model. We derive the results from different modelling and sampling decisions proposed in the literature, and we find that the explanatory power of the frequency of price changes for the speed of response to a macroeconomic shock proves robust in the face of these decisions. Other results are sensitive with respect to the choice of the factor model and the treatment of outliers.     

Drivers of expected returns in Istanbul stock exchange: Fama–French factors and coskewness

We investigate the impact of coskewness on the variation of portfolio excess returns in Istanbul Stock Exchange (ISE) over the period July 1999 to December 2005. We form portfolios according to size, industry, size and book-to-market ratio, momentum and coskewness and compare alternative asset pricing models. The traditional capital asset pricing model (CAPM) and the three-factor model of Fama and French are tested in the multivariate testing procedure of Gibbons–Ross–Shanken (1989). Coskewness is introduced as a fourth factor and its incremental effect over CAPM and Fama–French factors is examined both in multivariate tests and in cross-sectional regressions. The findings reveal that coskewness is able to explain the size premium in ISE. Hence, the basic two-moment CAPM without the coskewness factor would underestimate the expected return of size portfolios. Multivariate test results indicate that coskewness reduces the pricing bias, albeit insignificantly. Cross-sectional analysis uncovers that coskewness has a significant additional explanatory power over CAPM, especially for size and industry portfolios. However, coskewness does not have a significant incremental explanatory power over Fama–French factors in ISE.     

R&D Spillovers through Student Flows,Institutions, and Economic Growth: What can we Learn from African Countries?

Using modern panel cointegration estimation techniques, this paper examines whether tertiary student flows can effectively transmit technological knowledge from industrialized countries to African countries. The results obtained lend strong support to this hypothesis. In addition, this paper extends the analysis to include institutional variables such as the ease of doing business, legal origins, and religious majority to see if institutional characteristics have any impact on the way knowledge diffusion affecting total factor productivity (TFP). However, it is not clear that institutional differences are important factors that influence the degree of R&D spillovers and, hence, the TFP of African countries.     

The presence of unemployment hysteresis in the OECD: what can we learn from out-of-sample forecasts?

This paper investigates the relevance of unemployment hysteresis in seventeen OECD countries. We employ an out-of-sample forecast exercise in which a mean-reverting autoregressive model is compared to an autoregressive model with an imposed unit root. A substantial difference in forecasting performance between the two models is established for many countries, but the results are mixed in their strength. The evidence for unemployment hysteresis in Austria, Finland, Iceland, Israel, Italy, Japan and Sweden is, however, convincing. For no country can unambiguous support for a mean reverting unemployment rate be found.     

Pension expectations,reforms and macroeconomic downturn in Italy. What can microdata tell us?

We use different years of the Bank of Italy’s Survey on Household Income and Wealth (SHIW) to explore how Italian workers’ expectations regarding their future level of pension benefits and retirement age changed from 2000 to 2014. Comparing expected and statutory values for future pension benefits and retirement ages, we find that knowledge of the pension system and its rules are not evenly distributed among workers. Some sections of the population, in particular, younger workers, women and the self-employed, are less precise in estimating their future pension benefits. As for retirement age, a large share of the working population still has not completely assimilated the implications of the linkage with the evolution of lifetime expectations at 65. Expectations in the final part of the period observed are dominated by increasing pessimism, which may be related to the macroeconomic crisis of the Italian economy and to the approval of a severe pension reform in 2011. Checking whether a household’s total wealth is consistent with lifetime consumption, we find that households where the head overestimates the future value of the pension benefit accumulate fewer resources than the remaining part of the population.     

Wealth Inequality in Sweden: What can we Learn from Capitalized Income Tax Data?

This paper presents new estimates of wealth inequality in Sweden during 2000–2012, linking wealth register data up to 2007 and individually capitalized wealth based on income and property tax registers for the period thereafter when a repeal of the wealth tax stopped the collection of individual wealth statistics. We find that wealth inequality increased after 2007 and that more unequal bank holdings and housing appear to be important drivers. We also evaluate the performance of the capitalization method by contrasting its estimates and their dispersion with observed stocks in register data up to 2007. The goodness‐of‐fit varies tremendously across assets and we conclude that although capitalized wealth estimates may well approximate overall inequality levels and trends, they are highly sensitive to assumptions and the quality of the underlying data sources.     

What can eastern European countries learn from the Slovak economy? A twin deficit growth approach

This paper applies an extended growth model to the Slovak economy and explains the potential pitfalls that a transition economy faces on the way to converging with other advanced European countries. Our empirical analysis shows that Slovakia grew at a higher rate than that allowed by the balance-of-payments equilibrium rate and that this is consistent with the accumulation of trade deficits over time. A scenarios analysis shows that improving trade competitiveness, changing import and export shares toward a current account balance, and financing the economy at a lower cost will be the most successful ways to achieve higher growth.     

Big data analytics in economics: What have we learned so far,and where should we go from here?

Research into predictive accuracy testing remains at the forefront of the forecasting field. One reason for this is that rankings of predictive accuracy across alternative models, which under misspecification are loss function dependent, are universally utilized to assess the usefulness of econometric models. A second reason, which corresponds to the objective of this paper, is that researchers are currently focusing considerable attention on so‐called big data and on new (and old) tools that are available for the analysis of this data. One of the objectives in this field is the assessment of whether big data leads to improvement in forecast accuracy. In this survey paper, we discuss some of the latest (and most interesting) methods currently available for analyzing and utilizing big data when the objective is improved prediction. Our discussion includes a summary of various so‐called dimension reduction, shrinkage and machine learning methods as well as a summary of recent tools that are useful for ranking prediction models associated with the implementation of these methods. We also provide a brief empirical illustration of big data in action, in which we show that big data are indeed useful when predicting the term structure of interest rates.