Academic literature on the topic 'Heteroscedasticity and autocorrelation consistent (HAC)'

Studi ini bertujuan untuk menganalisis bagaimanakah pengaruh desentralisasi fiskal terhadap disparitas pendapatan regional di Indonesia selama periode 2001-2008. Studi dilakukan terhadap Kabupaten/Kota di Indonesia yang merupakan titik tolak dari desentralisasi di Indonesia. Sampel yang diteliti sebanyak 253 Kabupaten/Kota. Variabel yang digunakan sebagai proxy dari desentralisasi fiskal adalah: belanja langsung (desentralisasi pengeluaran), serta PAD, DAU dan Dana Bagi Hasil (desentralisasi penerimaan). Studi menggunakan pendekatan data panel dan alat analisis Least Square Dummy Variables (LSDV) atau juga dikenal sebagai Fixed Effect Model (FEM) dan Newey West Method atau juga dikenal sebagai HAC (heteroscedasticity-and autocorrelation-consistent) untuk menghilangkan heteroskedastisitas dan autokorelasi. Hasil studi menunjukkan bahwa variabel belanja langsung telah mampu untuk mengurangi disparitas pendapatan regional, sebaliknya DAU justru mengakibatkan meningkatnya disparitas pendapatan regional. Terdapat indikasi bahwa penyebab utamanya adalah adanya missallocated di dalam mekanisme alokasi DAU. Sementara itu variabel PAD dan Dana Bagi Hasil tidak berpengaruh signifikan terhadap disparitas pendapatan regional. This study aims to analyze how the influence of fiscal decentralization on regional income disparity in Indonesia during the period 2001-2008. Studies conducted on municipalities in Indonesia. 253 municipalities are used as samples. The variables used as proxies of fiscal decentralization are: direct expenditure (expenditure decentralization), and PAD, DAU and DBH (revenue decentralization). The study is using panel data approach and Least Square Dummy Variables (LSDV) as an analytical tool or also known as the Fixed Effect Modeland Newey West method or also known as HAC (heteroscedasticity-and Autocorrelation-consistent) to remove heteroscedasticity and autocorrelation. The study shows that direct expenditure variable has been able to reduce regional income disparities, on the contrary DAU has increased regional income disparity. There are indications that the main cause is the presence missallocated in DAU allocation mechanism. Meanwhile, PAD and DBH have no significant effect on regional income disparities.

This work investigates the adequacy of core inflation measures as indicators of forward-looking expectations in the hybrid new Keynesian Phillips curve (HNKPC) for the Brazilian economy. For that purpose, we use monthly data between January 2002 and August 2015 and the heteroscedasticity and autocorrelation consistent generalized method of moments (HAC-GMM). The results indicate that the HNKPC is a robust mechanism to model Brazilian inflation dynamics in the period analyzed; that the recent increase in the degree of indexation of the Brazilian economy seems to have contributed to the formation of a stronger inertial component of inflation; and also that the core inflation measures appear to be potential indicators to model forward-looking expectations in the HNKPC in Brazil. Furthermore, the inflation forecasts extracted from these models are statistically similar to those generated by models that use market prognoses from the Focus survey published by the Central Bank of Brazil. Therefore, the core inflation measures appear to have adequately anchored the inflation expectations in Brazil in the period analyzed.

Purpose The purpose of this study is to invoke prospect theory to construct an empirical framework to predict idiosyncratic risk, and argue that when a firm performs better than its benchmarks, the firm tends to play safe by avoiding firm-specific risk to maintain its satisfactory performance level, but when a firm performs worse than its benchmarks, the firm may become aggressive with taking more risks to achieve an increased level of performance. Design/methodology/approach This study tested the relationships between restaurant firms’ future idiosyncratic risk and the proposed firm financial characteristics. Heteroscedasticity- and autocorrelation-consistent (HAC) standard errors (Newey and West, 1994) were used to deal with potential problems of autocorrelations and heteroscedasticity. The standard error of residuals from the Fama-French three-factor model (Fama and French, 1993) was estimated to proxy for restaurant idiosyncratic risk. Findings The main analysis reveals that five financial characteristics are significant predictors for restaurant firms’ future idiosyncratic risk in accordance with the proposed, negative relationship based on the prospect theory. Practical implications Managers may predict their competitors’ future risk-taking behaviors using the current study’s findings, which will provide competitive advantage in a highly competitive business environment that we have now. Also, in practice, restaurant investors may consider findings of this study in forecasting future risks of their portfolio to help evaluate and revise their portfolios. Originality/value First, this is a new endeavor of its kind dealing with the restaurant industry, filling the void in the literature in predicting the risk-taking behavior of restaurant firms in a time of crisis. Second, this study forms a prediction model that establishes “predictive causality” (Diebold, 2001) motivated by prospect theory. Third, building upon prior research, this study comprehensively examines relationships between the firm characteristics that capture firm-specific strategies (Ou and Penman, 1989) and the idiosyncratic risk that are “associated with firm-specific strategies” (Luo and Bhattacharya, 2009) in a restaurant setting. Finally, the findings of this study bear significant implications for practitioners and other parties of interest.

<p><em>A production process requires inputs to be used to produce output. The input according to Solow is the capital and labor described in the Cobb – Douglas function. Infrastructure can be said as capital in an effort to increase productivity, since labor requires supporting facilities that can increase their productivity. Therefore infrastructure is seen as having an important role in driving economic growth, so that adequate infrastructure is expected to have a positive impact on economic growth. This study aims to analyze the influence of the availability of infrastructure that is divided into economic infrastructure, social infrastructure and institutional infrastructure, on the economy in Indonesia which is described by the GDP. This study uses secondary data in 33 provinces in Indonesia in 2007-2014. This study uses panel data regression using the fixed effect model and correction of Heteroscedasticity and Autocorrelation Consistent (HAC). </em><em>Based on the results of econometric regression, it is known that the variables of road, electricity, education, health and capital expenditure have a positive and significant effect. While the employee expenditure variable has a positive but not significant relationship. In addition, it is known that telephone variables have a negative and insignificant relationship. The results of this study also show that electricity infrastructure has the biggest influence on economic growth..</em></p>

<p><span class="fontstyle0">VAT is a tax component that contributes the most to total tax revenue, this study analyzed the relationship of consumption to VAT receipts, but there are other free control variables such as; the money supply, population, and industrial sector revenues. This study refers to previous research that has been carried out by Harju, Kosonen, and Skans (2018) , entitled about “Firm types, pricesetting strategies, and consumption-tax incidence”. This study uses a Time Series Model, that is : is a forecast of future values based on past values of a variable and past mistakes. Time series models are usually use for forecasting. The research method used is quantitative descriptive, by using the Eviews test tool. Because at the beginning of testing this research experienced problems on the classical assumption test, then to overcome the problem of classical assumption test, the test continues using estimation method Newey-West HAC regression model (heteroscedasticity and autocorrelation consistent) standard error or more often called Newey-West HAC. The results of the study show that consumption as the main independent variable has a significant effect, and the control variable that has influence is the money supply and the industrial sector. the increasing amount of consumption each year can stimulate VAT revenues and economic growth in Indonesia</span><span class="fontstyle2">.</span></p><p><span class="fontstyle2"><br /></span><span class="fontstyle3">JEL Classification: </span><span class="fontstyle0">H20, H25, H26</span></p><p><span class="fontstyle0"><br /></span><span class="fontstyle3">Keywords</span><span class="fontstyle0">: Consumption, Industrial sector Money Supply, VAT</span></p>

In contrast to developed countries, Indian capital markets do not exhibit strong efficiency and therefore it appears possible that fund managers beat the benchmarks. We examine the existence of superior performance of open-ended equity mutual funds in India with various models including traditional Capital Asset Pricing Model (CAPM)-based as well as recent Fama–French–Carhart (FFC)-factors-based models. We use a survivorship-bias free database including all schemes since inception till recently. We found evidence of stock picking and timing abilities in Indian fund managers. Our results are robust to changes in benchmarks, return frequency, and effects of heteroscedasticity and autocorrelation (HAC).

Asymptotic theory for heteroskedasticity autocorrelation consistent (HAC) covariance matrix estimators requires the truncation lag, or bandwidth, to increase more slowly than the sample size. This paper considers an alternative approach covering the case with the asymptotic covariance matrix estimated by kernel methods with truncation lag equal to sample size. Although such estimators are inconsistent, valid tests (asymptotically pivotal) for regression parameters can be constructed. The limiting distributions explicitly capture the truncation lag and choice of kernel. A local asymptotic power analysis shows that the Bartlett kernel delivers the highest power within a group of popular kernels. Finite sample simulations suggest that, regardless of the kernel chosen, the null asymptotic approximation of the new tests is often more accurate than that for conventional HAC estimators and asymptotics. Finite sample results on power show that the new approach is competitive.

The usual t test, the t test based on heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators, and the heteroskedasticity and autocorrelation robust (HAR) test are three statistics that are widely used in applied econometric work. The use of these significance tests in trend regression is of particular interest given the potential for spurious relationships in trend formulations. Following a longstanding tradition in the spurious regression literature, this paper investigates the asymptotic and finite sample properties of these test statistics in several spurious regression contexts, including regression of stochastic trends on time polynomials and regressions among independent random walks. Concordant with existing theory (Phillips 1986, 1998; Sun 2004, 2014b) the usual t test and HAC standardized test fail to control size as the sample size n → ∞ in these spurious formulations, whereas HAR tests converge to well-defined limit distributions in each case and therefore have the capacity to be consistent and control size. However, it is shown that when the number of trend regressors K → ∞ , all three statistics, including the HAR test, diverge and fail to control size as n → ∞ . These findings are relevant to high-dimensional nonstationary time series regressions where machine learning methods may be employed.

This paper proposes a semiparametric generalized method of moments estimator (GMM) estimator for a partially parametric spatial model with endogenous spatially dependent regressors. The finite-dimensional estimator is shown to be consistent and root-n asymptotically normal under some reasonable conditions. A spatial heteroscedasticity and autocorrelation consistent covariance estimator is constructed for the GMM estimator. The leading application is nonlinear spatial autoregressions, which arise in a wide range of strategic interaction models. To derive the asymptotic properties of the estimator, the paper also establishes a stochastic equicontinuity criterion and functional central limit theorem for near-epoch dependent random fields.

The research aims to analyze the influence of the gold price, oil price and financial risk on Islamic and conventional securities on comparative as well as on individual bases. Monthly prices of oil and gold are extracted from the websites of West Texas Intermediate and World Gold Council, whereas time series data for financial risk is derived from the Volatility Index of S&P 500. All these variables are found to be cointegrated at the first difference with both the Dow Jones indices, which means that gold, oil and financial risk have long term association with Islamic and conventional stocks. In order to find the direction and magnitude, this study applied the Newey-West HAC test, which also handles autocorrelation and heteroscedasticity issues in the time series data. The findings of the study suggest that gold prices are positively associated whereas oil prices and financial risk are negatively associated with both types of securities. Though the direction of the nexus is similar for Islamic and conventional stocks, but the magnitude differs especially in case of oil and financial risk. Nevertheless, it can be concluded that there is no diversification prospect between conventional and Islamic stocks under the influence of oil prices, financial risk, and gold prices.

Portfolio selection has a long tradition in financial economics and plays an integral role in investment management. Portfolio selection provides the framework to determine optimal portfolio choice from a universe of available investments. However, the asset weightings from portfolio selection are optimal only if the empirical characteristics of asset returns do not violate the portfolio selection model assumptions. This thesis explores the empirical characteristics of traditional assets and hedge fund returns and examines their effects on the assumptions of linearity-in-the-mean testing and portfolio selection. The encompassing theme of this thesis is the empirical interplay between traditional assets and hedge fund returns. Despite the paucity of hedge fund research, pension funds continue to increase their portfolio allocations to global hedge funds in an effort to pursue higher risk-adjusted returns. This thesis presents three empirical studies which provide positive insights into the relationships between traditional assets and hedge fund returns. The first two empirical studies examine an emerging body of literature which suggests that the relationship between traditional assets and hedge fund returns is non-linear. For mean-variance investors, non-linear asset returns are problematic as they do not satisfy the assumption of linearity required for the covariance matrix in portfolio selection. To examine the linearity assumption as it relates to a mean-variance investor, a hypothesis test approach is employed which investigates the linearity-in-the-mean of traditional assets and hedge funds. The findings from the first two empirical studies reveal that conventional linearity-in-the-mean tests incorrectly conclude that asset returns are nonlinear. We demonstrate that the empirical characteristics of heteroscedasticity and autocorrelation in asset returns are the primary sources of test mis-specification in these linearity-in-the-mean hypothesis tests. To address this problem, an innovative approach is proposed to control heteroscedasticity and autocorrelation in the underlying tests and it is shown that traditional assets and hedge funds are indeed linear-in-the-mean. The third and final study of this thesis explores traditional assets and hedge funds in a portfolio selection framework. Following the theme of the previous two studies, the effects of heteroscedasticity and autocorrelation are examined in the portfolio selection context. The characteristics of serial correlation in bond and hedge fund returns are shown to cause a downward bias in the second sample moment. This thesis proposes two methods to control for this effect and it is shown that autocorrelation induces an overallocation to bonds and hedge funds. Whilst heteroscedasticity cannot be directly examined in portfolio selection, empirical evidence suggests that heteroscedastic events (such as those that occurred in August 1998) translate into the empirical feature known as tail-risk. The effects of tail-risk are examined by comparing the portfolio decisions of mean-variance analysis (MVA) versus mean-conditional value at risk (M-CVaR) investors. The findings reveal that the volatility of returns in a MVA portfolio decreases when hedge funds are included in the investment opportunity set. However, the reduction in the volatility of portfolio returns comes at a cost of undesirable third and fourth moments. Furthermore, it is shown that investors with M-CVaR preferences exhibit a decreasing demand for hedge funds as their aversion for tail-risk increases. The results of the thesis highlight the sensitivities of linearity tests and portfolio selection to the empirical features of heteroscedasticity, autocorrelation and tail-risk. This thesis contributes to the literature by providing refinements to these frameworks which allow improved inferences to be made when hedge funds are examined in linearity and portfolio selection settings.

Portfolio selection has a long tradition in financial economics and plays an integral role in investment management. Portfolio selection provides the framework to determine optimal portfolio choice from a universe of available investments. However, the asset weightings from portfolio selection are optimal only if the empirical characteristics of asset returns do not violate the portfolio selection model assumptions. This thesis explores the empirical characteristics of traditional assets and hedge fund returns and examines their effects on the assumptions of linearity-in-the-mean testing and portfolio selection. The encompassing theme of this thesis is the empirical interplay between traditional assets and hedge fund returns. Despite the paucity of hedge fund research, pension funds continue to increase their portfolio allocations to global hedge funds in an effort to pursue higher risk-adjusted returns. This thesis presents three empirical studies which provide positive insights into the relationships between traditional assets and hedge fund returns. The first two empirical studies examine an emerging body of literature which suggests that the relationship between traditional assets and hedge fund returns is non-linear. For mean-variance investors, non-linear asset returns are problematic as they do not satisfy the assumption of linearity required for the covariance matrix in portfolio selection. To examine the linearity assumption as it relates to a mean-variance investor, a hypothesis test approach is employed which investigates the linearity-in-the-mean of traditional assets and hedge funds. The findings from the first two empirical studies reveal that conventional linearity-in-the-mean tests incorrectly conclude that asset returns are nonlinear. We demonstrate that the empirical characteristics of heteroscedasticity and autocorrelation in asset returns are the primary sources of test mis-specification in these linearity-in-the-mean hypothesis tests. To address this problem, an innovative approach is proposed to control heteroscedasticity and autocorrelation in the underlying tests and it is shown that traditional assets and hedge funds are indeed linear-in-the-mean. The third and final study of this thesis explores traditional assets and hedge funds in a portfolio selection framework. Following the theme of the previous two studies, the effects of heteroscedasticity and autocorrelation are examined in the portfolio selection context. The characteristics of serial correlation in bond and hedge fund returns are shown to cause a downward bias in the second sample moment. This thesis proposes two methods to control for this effect and it is shown that autocorrelation induces an overallocation to bonds and hedge funds. Whilst heteroscedasticity cannot be directly examined in portfolio selection, empirical evidence suggests that heteroscedastic events (such as those that occurred in August 1998) translate into the empirical feature known as tail-risk. The effects of tail-risk are examined by comparing the portfolio decisions of mean-variance analysis (MVA) versus mean-conditional value at risk (M-CVaR) investors. The findings reveal that the volatility of returns in a MVA portfolio decreases when hedge funds are included in the investment opportunity set. However, the reduction in the volatility of portfolio returns comes at a cost of undesirable third and fourth moments. Furthermore, it is shown that investors with M-CVaR preferences exhibit a decreasing demand for hedge funds as their aversion for tail-risk increases. The results of the thesis highlight the sensitivities of linearity tests and portfolio selection to the empirical features of heteroscedasticity, autocorrelation and tail-risk. This thesis contributes to the literature by providing refinements to these frameworks which allow improved inferences to be made when hedge funds are examined in linearity and portfolio selection settings.

This chapter contains treatments of a range of topics associated with the central limit theorem. These include estimated normalization using methods of heteroscedasticity and autocorrelation consistent variance estimation, the CLT in linear prrocesses, random norming giving rise to a mixed Gaussian limiting distribution, and the Cramér–Wold device and multivariate CLT. The delta method to derive the limit distributions of differentiable functions is described. The law of the iterated logarithm is proved for Gaussian processes.